This notebook is from databricks document: - https://docs.databricks.com/_static/notebooks/package-cells.html
As you know, we need to eventually package and deploy our models, say using sbt (recall recommended home work 1).
However it is nice to be in a notebook environment to prototype and build intuition and create better pipelines.
Using package cells we can be in the best of both worlds to an extent.
Package Cells
Package cells are special cells that get compiled when executed. These cells have no visibility with respect to the rest of the notebook. You may think of them as separate scala files.
This means that only class and object definitions may go inside this cell. You may not have any variable or function definitions lying around by itself. The following cell will not work.
If you wish to use custom classes and/or objects defined within notebooks reliably in Spark, and across notebook sessions, you must use package cells to define those classes.
Unless you use package cells to define classes, you may also come across obscure bugs as follows:
// We define a class
case class TestKey(id: Long, str: String)
defined class TestKey
// we use that class as a key in the group by
val rdd = sc.parallelize(Array((TestKey(1L, "abd"), "dss"), (TestKey(2L, "ggs"), "dse"), (TestKey(1L, "abd"), "qrf")))
rdd.groupByKey().collect
rdd: org.apache.spark.rdd.RDD[(TestKey, String)] = ParallelCollectionRDD[0] at parallelize at command-2972105651607260:2
res0: Array[(TestKey, Iterable[String])] = Array((TestKey(2,ggs),CompactBuffer(dse)), (TestKey(1,abd),CompactBuffer(qrf)), (TestKey(1,abd),CompactBuffer(dss)))
What went wrong above? Even though we have two elements for the key TestKey(1L, "abd"), they behaved as two different keys resulting in:
Array[(TestKey, Iterable[String])] = Array(
(TestKey(2,ggs),CompactBuffer(dse)),
(TestKey(1,abd),CompactBuffer(dss)),
(TestKey(1,abd),CompactBuffer(qrf)))
Once we define our case class within a package cell, we will not face this issue.
package com.databricks.example
case class TestKey(id: Long, str: String)
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
import com.databricks.example
val rdd = sc.parallelize(Array(
(example.TestKey(1L, "abd"), "dss"), (example.TestKey(2L, "ggs"), "dse"), (example.TestKey(1L, "abd"), "qrf")))
rdd.groupByKey().collect
import com.databricks.example
rdd: org.apache.spark.rdd.RDD[(com.databricks.example.TestKey, String)] = ParallelCollectionRDD[2] at parallelize at command-2972105651607263:3
res2: Array[(com.databricks.example.TestKey, Iterable[String])] = Array((TestKey(2,ggs),CompactBuffer(dse)), (TestKey(1,abd),CompactBuffer(dss, qrf)))
As you can see above, the group by worked above, grouping two elements (dss, qrf) under TestKey(1,abd).
These cells behave as individual source files, therefore only classes and objects can be defined inside these cells.
package x.y.z
val aNumber = 5 // won't work
def functionThatWillNotWork(a: Int): Int = a + 1
The following cell is the way to go.
package x.y.z
object Utils {
val aNumber = 5 // works!
def functionThatWillWork(a: Int): Int = a + 1
}
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
import x.y.z.Utils
Utils.functionThatWillWork(Utils.aNumber)
import x.y.z.Utils
res5: Int = 6
Why did we get the warning: classes defined within packages cannot be redefined without a cluster restart?
Classes that get compiled with the package cells get dynamically injected into Spark's classloader. Currently it's not possible to remove classes from Spark's classloader. Any classes that you define and compile will have precedence in the classloader, therefore once you recompile, it will not be visible to your application.
Well that kind of beats the purpose of iterative notebook development if I have to restart the cluster, right? In that case, you may just rename the package to x.y.z2 during development/fast iteration and fix it once everything works.
One thing to remember with package cells is that it has no visiblity regarding the notebook environment.
- The SparkContext will not be defined as
sc. - The SQLContext will not be defined as
sqlContext. - Did you import a package in a separate cell? Those imports will not be available in the package cell and have to be remade.
- Variables imported through
%runcells will not be available.
It is really a standalone file that just looks like a cell in a notebook. This means that any function that uses anything that was defined in a separate cell, needs to take that variable as a parameter or the class needs to take it inside the constructor.
package x.y.zpackage
import org.apache.spark.SparkContext
case class IntArray(values: Array[Int])
class MyClass(sc: SparkContext) {
def sparkSum(array: IntArray): Int = {
sc.parallelize(array.values).reduce(_ + _)
}
}
object MyClass {
def sparkSum(sc: SparkContext, array: IntArray): Int = {
sc.parallelize(array.values).reduce(_ + _)
}
}
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
import x.y.zpackage._
val array = IntArray(Array(1, 2, 3, 4, 5))
val myClass = new MyClass(sc)
myClass.sparkSum(array)
import x.y.zpackage._
array: x.y.zpackage.IntArray = IntArray([I@44a91426)
myClass: x.y.zpackage.MyClass = x.y.zpackage.MyClass@5d3b142
res7: Int = 15
MyClass.sparkSum(sc, array)
res8: Int = 15
Core ideas in Monte Carlo simulation
- modular arithmetic gives pseudo-random streams that are indistiguishable from 'true' Uniformly distributed samples in integers from \({0,1,2,...,m}\)
- by diving the integer streams from above by \(m\) we get samples from \({0/m,1/m,...,(m-1)/m}\) and "pretend" this to be samples from the Uniform(0,1) RV
- we can use inverse distribution function of von Neumann's rejection sampler to convert samples from Uniform(0,1) RV to the following:
- any other random variable
- vector of random variables that could be dependent
- or more generally other random structures:
- random graphs and networks
- random walks or (sensible perturbations of live traffic data on open street maps for hypothesis tests)
- models of interacting paticle systems in ecology / chemcal physics, etc...
- https://en.wikipedia.org/wiki/Inversetransformsampling
- https://en.wikipedia.org/wiki/Rejection_sampling - will revisit below for Expoential RV
breeze.stats.distributions
Breeze also provides a fairly large number of probability distributions. These come with access to probability density function for either discrete or continuous distributions. Many distributions also have methods for giving the mean and the variance.
import breeze.stats.distributions._
val poi = new Poisson(3.0);
import breeze.stats.distributions._
poi: breeze.stats.distributions.Poisson = Poisson(3.0)
val s = poi.sample(5); // let's draw five samples - black-box
s: IndexedSeq[Int] = Vector(2, 1, 1, 3, 2)
Getting probabilities of the Poisson samples
s.map( x => poi.probabilityOf(x) ) // PMF
res0: IndexedSeq[Double] = Vector(0.22404180765538775, 0.14936120510359185, 0.14936120510359185, 0.22404180765538775, 0.22404180765538775)
val doublePoi = for(x <- poi) yield x.toDouble // meanAndVariance requires doubles, but Poisson samples over Ints
doublePoi: breeze.stats.distributions.Rand[Double] = MappedRand(Poisson(3.0),$Lambda$5455/238879880@20291ada)
breeze.stats.meanAndVariance(doublePoi.samples.take(1000));
res1: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(2.9940000000000055,3.0370010010010025,1000)
(poi.mean, poi.variance) // population mean and variance
res2: (Double, Double) = (3.0,3.0)
Exponential random Variable
Let's focus on getting our hands direty with a common random variable.
NOTE: Below, there is a possibility of confusion for the term rate in the family of exponential distributions. Breeze parameterizes the distribution with the mean, but refers to it as the rate.
val expo = new Exponential(0.5);
expo: breeze.stats.distributions.Exponential = Exponential(0.5)
expo.rate // what is the rate parameter
res3: Double = 0.5
A characteristic of exponential distributions is its half-life, but we can compute the probability a value falls between any two numbers.
expo.probability(0, math.log(2) * expo.rate)
res4: Double = 0.5
expo.probability(math.log(2) * expo.rate, 10000.0)
res5: Double = 0.5
expo.probability(0.0, 1.5)
res6: Double = 0.950212931632136
The above result means that approximately 95% of the draws from an exponential distribution fall between 0 and thrice the mean. We could have easily computed this with the cumulative distribution as well.
1 - math.exp(-3.0) // the CDF of the Exponential RV with rate parameter 3
res7: Double = 0.950212931632136
val samples = expo.sample(2).sorted; // built-in black box - we will roll our own shortly in Spark
samples: IndexedSeq[Double] = Vector(1.0491479844289333, 2.5388976008276827)
expo.probability(samples(0), samples(1));
res8: Double = 0.1164316379209569
breeze.stats.meanAndVariance(expo.samples.take(10000)); // mean and variance of the sample
res9: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(2.0066329747737677,3.9408880555553485,10000)
(1 / expo.rate, 1 / (expo.rate * expo.rate)) // mean and variance of the population
res10: (Double, Double) = (2.0,4.0)
import spark.implicits._
import org.apache.spark.sql.functions._
import spark.implicits._
import org.apache.spark.sql.functions._
val df = spark.range(1000).toDF("Id") // just make a DF of 1000 row indices
df: org.apache.spark.sql.DataFrame = [Id: bigint]
df.show(5)
+---+
| Id|
+---+
| 0|
| 1|
| 2|
| 3|
| 4|
+---+
only showing top 5 rows
val dfRand = df.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double]
dfRand.show(5) // these are first 5 of the 1000 samples from the Uniform(0,1) RV
+---+--------------------+
| Id| rand|
+---+--------------------+
| 0|0.024042816995877625|
| 1| 0.4763832311243641|
| 2| 0.3392911406256911|
| 3| 0.6282132043405342|
| 4| 0.9665960987271114|
+---+--------------------+
only showing top 5 rows
val dfRand = df.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
dfRand.show(5) // these are first 5 of the 1000 samples from the Uniform(0,1) RV
+---+--------------------+
| Id| rand|
+---+--------------------+
| 0|0.024042816995877625|
| 1| 0.4763832311243641|
| 2| 0.3392911406256911|
| 3| 0.6282132043405342|
| 4| 0.9665960987271114|
+---+--------------------+
only showing top 5 rows
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double]
val dfRand = df.select($"Id", rand(seed=879664) as "rand") // add a column of random numbers in (0,1)
dfRand.show(5) // these are first 5 of the 1000 samples from the Uniform(0,1) RV
+---+-------------------+
| Id| rand|
+---+-------------------+
| 0| 0.269224348263137|
| 1|0.20433965628039852|
| 2| 0.8481228617934538|
| 3| 0.6665103087537884|
| 4| 0.7712898780552342|
+---+-------------------+
only showing top 5 rows
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double]
Let's use the inverse CDF of the Exponential RV to transform these samples from the Uniform(0,1) RV into those from the Exponential RV.
val dfRand = df.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
.withColumn("one",lit(1.0))
.withColumn("rate",lit(0.5))
dfRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double ... 2 more fields]
dfRand.show(5)
+---+--------------------+---+----+
| Id| rand|one|rate|
+---+--------------------+---+----+
| 0|0.024042816995877625|1.0| 0.5|
| 1| 0.4763832311243641|1.0| 0.5|
| 2| 0.3392911406256911|1.0| 0.5|
| 3| 0.6282132043405342|1.0| 0.5|
| 4| 0.9665960987271114|1.0| 0.5|
+---+--------------------+---+----+
only showing top 5 rows
val dfExpRand = dfRand.withColumn("expo_sample", -($"one" / $"rate") * log($"one" - $"rand")) // samples from expo(rate=0.5)
dfExpRand: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double ... 3 more fields]
dfExpRand.show(5)
+---+--------------------+---+----+--------------------+
| Id| rand|one|rate| expo_sample|
+---+--------------------+---+----+--------------------+
| 0|0.024042816995877625|1.0| 0.5|0.048673126808362034|
| 1| 0.4763832311243641|1.0| 0.5| 1.2939904386814756|
| 2| 0.3392911406256911|1.0| 0.5| 0.8288839819270684|
| 3| 0.6282132043405342|1.0| 0.5| 1.9788694379168241|
| 4| 0.9665960987271114|1.0| 0.5| 6.798165162486205|
+---+--------------------+---+----+--------------------+
only showing top 5 rows
display(dfExpRand)
| Id | rand | one | rate | expo_sample |
|---|---|---|---|---|
| 0.0 | 2.4042816995877625e-2 | 1.0 | 0.5 | 4.8673126808362034e-2 |
| 1.0 | 0.4763832311243641 | 1.0 | 0.5 | 1.2939904386814756 |
| 2.0 | 0.3392911406256911 | 1.0 | 0.5 | 0.8288839819270684 |
| 3.0 | 0.6282132043405342 | 1.0 | 0.5 | 1.9788694379168241 |
| 4.0 | 0.9665960987271114 | 1.0 | 0.5 | 6.798165162486205 |
| 5.0 | 0.8269758822163711 | 1.0 | 0.5 | 3.508648570093974 |
| 6.0 | 0.3404052214826948 | 1.0 | 0.5 | 0.8322592089262 |
| 7.0 | 0.5658253891225227 | 1.0 | 0.5 | 1.6686169931673005 |
| 8.0 | 0.11737981749647353 | 1.0 | 0.5 | 0.24972063061386013 |
| 9.0 | 0.7848668745585088 | 1.0 | 0.5 | 3.072996508744745 |
| 10.0 | 0.10665116151243736 | 1.0 | 0.5 | 0.2255562755600773 |
| 11.0 | 0.34971775733163646 | 1.0 | 0.5 | 0.8606975816973311 |
| 12.0 | 3.349566781262969e-2 | 1.0 | 0.5 | 6.813899599567436e-2 |
| 13.0 | 0.334063669089834 | 1.0 | 0.5 | 0.8131224244912618 |
| 14.0 | 0.4105052702588069 | 1.0 | 0.5 | 1.0569789985263547 |
| 15.0 | 0.22519777483698333 | 1.0 | 0.5 | 0.5102949510683874 |
| 16.0 | 0.21124107883891907 | 1.0 | 0.5 | 0.47458910937069004 |
| 17.0 | 0.12332274767843698 | 1.0 | 0.5 | 0.26323273531964136 |
| 18.0 | 4.324422155449681e-2 | 1.0 | 0.5 | 8.841423006745963e-2 |
| 19.0 | 3.933267172708754e-2 | 1.0 | 0.5 | 8.025420479220831e-2 |
| 20.0 | 0.8162723223794007 | 1.0 | 0.5 | 3.388601261211285 |
| 21.0 | 7.785566240785313e-2 | 1.0 | 0.5 | 0.16210703862675058 |
| 22.0 | 0.1244015150504949 | 1.0 | 0.5 | 0.26569528726942954 |
| 23.0 | 0.5313795676534989 | 1.0 | 0.5 | 1.5159243018004147 |
| 24.0 | 0.8177063965639969 | 1.0 | 0.5 | 3.4042733720631824 |
| 25.0 | 0.7757379079578416 | 1.0 | 0.5 | 2.98987971469377 |
| 26.0 | 0.9019086568714294 | 1.0 | 0.5 | 4.643712323362237 |
| 27.0 | 0.5793202308042869 | 1.0 | 0.5 | 1.7317667559523853 |
| 28.0 | 4.913530139386124e-2 | 1.0 | 0.5 | 0.10076699863506142 |
| 29.0 | 0.6984668419742928 | 1.0 | 0.5 | 2.3977505839877837 |
| 30.0 | 1.579141234703818e-2 | 1.0 | 0.5 | 3.18348501444197e-2 |
| 31.0 | 0.3170147319677016 | 1.0 | 0.5 | 0.762563978285606 |
| 32.0 | 0.6243818511105477 | 1.0 | 0.5 | 1.9583644261768265 |
| 33.0 | 0.9720896733059052 | 1.0 | 0.5 | 7.157517052464175 |
| 34.0 | 6.406755887221727e-2 | 1.0 | 0.5 | 0.13242396678361196 |
| 35.0 | 0.8275324400058236 | 1.0 | 0.5 | 3.515092236401242 |
| 36.0 | 0.4223760255739688 | 1.0 | 0.5 | 1.0976643705892708 |
| 37.0 | 0.6237792697270514 | 1.0 | 0.5 | 1.9551585184791915 |
| 38.0 | 0.30676209726152626 | 1.0 | 0.5 | 0.7327640894184466 |
| 39.0 | 0.7209798363387625 | 1.0 | 0.5 | 2.5529424571699533 |
| 40.0 | 0.5408086036735253 | 1.0 | 0.5 | 1.556576340750279 |
| 41.0 | 0.22254408000797787 | 1.0 | 0.5 | 0.5034566621599345 |
| 42.0 | 0.6478051949133747 | 1.0 | 0.5 | 2.0871416658496442 |
| 43.0 | 0.6186563924196967 | 1.0 | 0.5 | 1.9281089062362353 |
| 44.0 | 0.2689179760895458 | 1.0 | 0.5 | 0.6264592354306907 |
| 45.0 | 0.1310898372764776 | 1.0 | 0.5 | 0.28103107825164747 |
| 46.0 | 0.8459785027719904 | 1.0 | 0.5 | 3.741326187841892 |
| 47.0 | 0.7844461581951896 | 1.0 | 0.5 | 3.069089109365284 |
| 48.0 | 0.3195541476806115 | 1.0 | 0.5 | 0.7700140609818293 |
| 49.0 | 0.326892158146161 | 1.0 | 0.5 | 0.7916994433570004 |
| 50.0 | 0.4392312327086285 | 1.0 | 0.5 | 1.1568932758900772 |
| 51.0 | 0.5377790694946909 | 1.0 | 0.5 | 1.543424595293625 |
| 52.0 | 0.4369640582174875 | 1.0 | 0.5 | 1.1488236262486446 |
| 53.0 | 0.26672242876690055 | 1.0 | 0.5 | 0.6204619408451196 |
| 54.0 | 0.9743181848790359 | 1.0 | 0.5 | 7.323944240755433 |
| 55.0 | 0.5636877255447679 | 1.0 | 0.5 | 1.6587941323713102 |
| 56.0 | 0.8214194684040824 | 1.0 | 0.5 | 3.44543124420649 |
| 57.0 | 4.2394218638494574e-2 | 1.0 | 0.5 | 8.663817482333575e-2 |
| 58.0 | 0.2986030735782996 | 1.0 | 0.5 | 0.7093626466953578 |
| 59.0 | 0.9251464009715654 | 1.0 | 0.5 | 5.184442172120483 |
| 60.0 | 0.9768531076059933 | 1.0 | 0.5 | 7.531789490600966 |
| 61.0 | 0.4147651110232632 | 1.0 | 0.5 | 1.0714839854387161 |
| 62.0 | 0.4525161010109643 | 1.0 | 0.5 | 1.2048444519262322 |
| 63.0 | 7.638776464132979e-2 | 1.0 | 0.5 | 0.1589259082490955 |
| 64.0 | 0.8203900440907219 | 1.0 | 0.5 | 3.433935381714252 |
| 65.0 | 0.9746746584977457 | 1.0 | 0.5 | 7.35189948830076 |
| 66.0 | 0.41832172801299305 | 1.0 | 0.5 | 1.083675562745256 |
| 67.0 | 3.3131200470495226e-2 | 1.0 | 0.5 | 6.738494114620364e-2 |
| 68.0 | 0.10008247055930286 | 1.0 | 0.5 | 0.21090430762250917 |
| 69.0 | 0.4268021831375646 | 1.0 | 0.5 | 1.113048783438383 |
| 70.0 | 0.8213380254753332 | 1.0 | 0.5 | 3.444519337826204 |
| 71.0 | 0.47431121070064797 | 1.0 | 0.5 | 1.2860917933318652 |
| 72.0 | 0.68679164992865 | 1.0 | 0.5 | 2.321773309424188 |
| 73.0 | 0.7057803676609208 | 1.0 | 0.5 | 2.4468574835462356 |
| 74.0 | 0.32184620342731496 | 1.0 | 0.5 | 0.776762356325894 |
| 75.0 | 0.3589329148022541 | 1.0 | 0.5 | 0.8892423408857425 |
| 76.0 | 0.7448782338623747 | 1.0 | 0.5 | 2.7320286670643386 |
| 77.0 | 0.9740613004640858 | 1.0 | 0.5 | 7.304038469785621 |
| 78.0 | 0.7453909609063684 | 1.0 | 0.5 | 2.736052180743762 |
| 79.0 | 6.853610233988816e-2 | 1.0 | 0.5 | 0.14199569380051374 |
| 80.0 | 0.336012781812836 | 1.0 | 0.5 | 0.8189847588182159 |
| 81.0 | 0.2963058480260412 | 1.0 | 0.5 | 0.7028229208813994 |
| 82.0 | 0.38627232525200883 | 1.0 | 0.5 | 0.9764079513820425 |
| 83.0 | 0.9097248909817963 | 1.0 | 0.5 | 4.809787008391862 |
| 84.0 | 0.20679745942261907 | 1.0 | 0.5 | 0.46335335878970363 |
| 85.0 | 0.16582497569469312 | 1.0 | 0.5 | 0.36262407472768277 |
| 86.0 | 0.6960738838737711 | 1.0 | 0.5 | 2.381941292346302 |
| 87.0 | 0.890094879396298 | 1.0 | 0.5 | 4.416275650715409 |
| 88.0 | 0.8258738496535992 | 1.0 | 0.5 | 3.4959504809275685 |
| 89.0 | 0.4066244237870885 | 1.0 | 0.5 | 1.0438554620679272 |
| 90.0 | 0.44414816275414526 | 1.0 | 0.5 | 1.1745070000344822 |
| 91.0 | 0.8820223175378497 | 1.0 | 0.5 | 4.274519608161631 |
| 92.0 | 0.3814200182827251 | 1.0 | 0.5 | 0.9606575597598919 |
| 93.0 | 0.491252177347836 | 1.0 | 0.5 | 1.3516056440673538 |
| 94.0 | 0.9042145561359308 | 1.0 | 0.5 | 4.691289097027222 |
| 95.0 | 0.8293982814391448 | 1.0 | 0.5 | 3.536847140695551 |
| 96.0 | 0.6957817964411346 | 1.0 | 0.5 | 2.38002012037681 |
| 97.0 | 0.46108317902841456 | 1.0 | 0.5 | 1.2363880819986401 |
| 98.0 | 0.658234439306857 | 1.0 | 0.5 | 2.14726054405596 |
| 99.0 | 0.43175037735005384 | 1.0 | 0.5 | 1.130388960612226 |
| 100.0 | 0.7719881477151886 | 1.0 | 0.5 | 2.9567153353469786 |
| 101.0 | 6.397418621476536e-2 | 1.0 | 0.5 | 0.13222444810891013 |
| 102.0 | 0.44410081132020296 | 1.0 | 0.5 | 1.1743366329905425 |
| 103.0 | 0.9364231799229901 | 1.0 | 0.5 | 5.511012678534471 |
| 104.0 | 0.5453471566845608 | 1.0 | 0.5 | 1.576442265948382 |
| 105.0 | 0.7346238968987211 | 1.0 | 0.5 | 2.653214404406468 |
| 106.0 | 0.3493365535175731 | 1.0 | 0.5 | 0.8595254994862054 |
| 107.0 | 0.5230037795263933 | 1.0 | 0.5 | 1.480493423321206 |
| 108.0 | 0.6886814284788837 | 1.0 | 0.5 | 2.333877090719847 |
| 109.0 | 0.7731252946239237 | 1.0 | 0.5 | 2.966714745163421 |
| 110.0 | 2.276168830750991e-2 | 1.0 | 0.5 | 4.604946957472576e-2 |
| 111.0 | 0.264951110753621 | 1.0 | 0.5 | 0.6156365319998442 |
| 112.0 | 0.14616140102686104 | 1.0 | 0.5 | 0.3160261944637388 |
| 113.0 | 3.066619523108849e-2 | 1.0 | 0.5 | 6.229248529326732e-2 |
| 114.0 | 0.49017052188807597 | 1.0 | 0.5 | 1.3473579316333943 |
| 115.0 | 9.835381148010536e-2 | 1.0 | 0.5 | 0.20706617613153588 |
| 116.0 | 8.828156810764243e-2 | 1.0 | 0.5 | 0.1848481470740138 |
| 117.0 | 0.6868446103815881 | 1.0 | 0.5 | 2.3221115183574854 |
| 118.0 | 0.4067862578001439 | 1.0 | 0.5 | 1.0444010055396156 |
| 119.0 | 0.44070042328022496 | 1.0 | 0.5 | 1.1621400679168603 |
| 120.0 | 0.19523663294276805 | 1.0 | 0.5 | 0.4344139974853618 |
| 121.0 | 0.34930320794183467 | 1.0 | 0.5 | 0.8594230049585315 |
| 122.0 | 0.37713232784782635 | 1.0 | 0.5 | 0.9468423740115679 |
| 123.0 | 0.6429635193876022 | 1.0 | 0.5 | 2.0598346316676848 |
| 124.0 | 0.5726782020473655 | 1.0 | 0.5 | 1.7004358488091253 |
| 125.0 | 0.9583607379261688 | 1.0 | 0.5 | 6.357423513924509 |
| 126.0 | 0.1736332737734787 | 1.0 | 0.5 | 0.38143325101594394 |
| 127.0 | 0.7191057722209122 | 1.0 | 0.5 | 2.539554188214649 |
| 128.0 | 0.5866772207662699 | 1.0 | 0.5 | 1.7670528869782172 |
| 129.0 | 0.44342504955089734 | 1.0 | 0.5 | 1.171906870972165 |
| 130.0 | 0.8638825079861409 | 1.0 | 0.5 | 3.988473708673089 |
| 131.0 | 0.19206484030065873 | 1.0 | 0.5 | 0.42654694315586256 |
| 132.0 | 6.329398370441452e-2 | 1.0 | 0.5 | 0.1307715918497558 |
| 133.0 | 0.8324305296152746 | 1.0 | 0.5 | 3.5727145302718077 |
| 134.0 | 0.788341172013531 | 1.0 | 0.5 | 3.105559205156182 |
| 135.0 | 0.9968069620813552 | 1.0 | 0.5 | 11.493564979531474 |
| 136.0 | 0.5738675981141458 | 1.0 | 0.5 | 1.70601035690584 |
| 137.0 | 2.1924901743684888e-2 | 1.0 | 0.5 | 4.433764862438196e-2 |
| 138.0 | 0.31475857342097047 | 1.0 | 0.5 | 0.755968110508673 |
| 139.0 | 0.5840924030390661 | 1.0 | 0.5 | 1.7545843321676697 |
| 140.0 | 0.7770937124350712 | 1.0 | 0.5 | 3.0020076619607927 |
| 141.0 | 0.7130309607190969 | 1.0 | 0.5 | 2.496761892223379 |
| 142.0 | 0.3991683439699586 | 1.0 | 0.5 | 1.0188809802465286 |
| 143.0 | 0.48509787688675254 | 1.0 | 0.5 | 1.3275568971757934 |
| 144.0 | 0.45169086393350866 | 1.0 | 0.5 | 1.2018320683571915 |
| 145.0 | 0.12902719463817103 | 1.0 | 0.5 | 0.2762890498682689 |
| 146.0 | 0.2592134704552379 | 1.0 | 0.5 | 0.6000855589507411 |
| 147.0 | 0.2813001484016546 | 1.0 | 0.5 | 0.660622921987986 |
| 148.0 | 0.3762566475369137 | 1.0 | 0.5 | 0.9440325786940167 |
| 149.0 | 0.9267694189625785 | 1.0 | 0.5 | 5.228284343045507 |
| 150.0 | 0.15197347800388006 | 1.0 | 0.5 | 0.32968673548099053 |
| 151.0 | 0.14593597548755854 | 1.0 | 0.5 | 0.3154982356972103 |
| 152.0 | 0.35991105880863383 | 1.0 | 0.5 | 0.8922962833448616 |
| 153.0 | 0.3071108301936344 | 1.0 | 0.5 | 0.7337704414228866 |
| 154.0 | 0.18406488587857806 | 1.0 | 0.5 | 0.40684088837547255 |
| 155.0 | 0.5136711150110314 | 1.0 | 0.5 | 1.4417403317355608 |
| 156.0 | 0.6056599490894785 | 1.0 | 0.5 | 1.8610833370818218 |
| 157.0 | 0.44302266378392197 | 1.0 | 0.5 | 1.1704614578045645 |
| 158.0 | 0.7453946147476727 | 1.0 | 0.5 | 2.736080882533261 |
| 159.0 | 0.35502683979054106 | 1.0 | 0.5 | 0.8770931502610587 |
| 160.0 | 0.8797839338461947 | 1.0 | 0.5 | 4.236929207935352 |
| 161.0 | 6.238712029644411e-2 | 1.0 | 0.5 | 0.12883624673709432 |
| 162.0 | 0.9424758304767943 | 1.0 | 0.5 | 5.711100159925468 |
| 163.0 | 0.5704103694439081 | 1.0 | 0.5 | 1.689849747036119 |
| 164.0 | 0.9158448954522559 | 1.0 | 0.5 | 4.950187400162322 |
| 165.0 | 0.8873811703242918 | 1.0 | 0.5 | 4.367492702015337 |
| 166.0 | 0.6216382152888731 | 1.0 | 0.5 | 1.9438088773652653 |
| 167.0 | 0.48827370203072595 | 1.0 | 0.5 | 1.3399307423143256 |
| 168.0 | 0.4572473011375716 | 1.0 | 0.5 | 1.2222029953319737 |
| 169.0 | 3.967393021768029e-2 | 1.0 | 0.5 | 8.096479233410842e-2 |
| 170.0 | 0.29505351687188286 | 1.0 | 0.5 | 0.6992667790155687 |
| 171.0 | 0.5639878272499856 | 1.0 | 0.5 | 1.6601702337428519 |
| 172.0 | 0.48965142848521204 | 1.0 | 0.5 | 1.3453226263340796 |
| 173.0 | 0.8749613569725964 | 1.0 | 0.5 | 4.15826489047167 |
| 174.0 | 0.9256239598413039 | 1.0 | 0.5 | 5.19724285977426 |
| 175.0 | 0.7569605924580269 | 1.0 | 0.5 | 2.8290633556705096 |
| 176.0 | 0.3844172120051691 | 1.0 | 0.5 | 0.9703716742615802 |
| 177.0 | 0.1362858511639865 | 1.0 | 0.5 | 0.29302682234878386 |
| 178.0 | 0.37983323803435165 | 1.0 | 0.5 | 0.9555337323937093 |
| 179.0 | 0.26566735487098103 | 1.0 | 0.5 | 0.6175863160615908 |
| 180.0 | 0.36961293921650573 | 1.0 | 0.5 | 0.9228425321120206 |
| 181.0 | 9.63913755811967e-2 | 1.0 | 0.5 | 0.2027178998486325 |
| 182.0 | 0.18788147800222665 | 1.0 | 0.5 | 0.4162179728411676 |
| 183.0 | 0.26504942094508055 | 1.0 | 0.5 | 0.6159040428220617 |
| 184.0 | 0.739578089934357 | 1.0 | 0.5 | 2.6909044643005315 |
| 185.0 | 0.6457524310661318 | 1.0 | 0.5 | 2.075518526013794 |
| 186.0 | 0.9092071659989257 | 1.0 | 0.5 | 4.798349834364611 |
| 187.0 | 0.210969342425946 | 1.0 | 0.5 | 0.4739002053005348 |
| 188.0 | 0.9875371965277739 | 1.0 | 0.5 | 8.770013586320161 |
| 189.0 | 5.026001823606596e-2 | 1.0 | 0.5 | 0.10313407051509839 |
| 190.0 | 0.12100530799389475 | 1.0 | 0.5 | 0.2579528399771316 |
| 191.0 | 0.5890018173984741 | 1.0 | 0.5 | 1.7783329727795534 |
| 192.0 | 0.7224532694248171 | 1.0 | 0.5 | 2.563531923007902 |
| 193.0 | 0.8883622267112601 | 1.0 | 0.5 | 4.384991631947751 |
| 194.0 | 1.997234871111797e-2 | 1.0 | 0.5 | 4.0348984229344e-2 |
| 195.0 | 0.2212019516532856 | 1.0 | 0.5 | 0.5000070229243507 |
| 196.0 | 0.4273320266042193 | 1.0 | 0.5 | 1.1148983665533385 |
| 197.0 | 0.9981606891187609 | 1.0 | 0.5 | 12.596728597154938 |
| 198.0 | 0.24173972097342367 | 1.0 | 0.5 | 0.5534571525106269 |
| 199.0 | 0.30996923292210077 | 1.0 | 0.5 | 0.7420381848339302 |
| 200.0 | 0.5586292644735815 | 1.0 | 0.5 | 1.635740173145147 |
| 201.0 | 0.5710479605847417 | 1.0 | 0.5 | 1.6928203250772282 |
| 202.0 | 0.7716668116970227 | 1.0 | 0.5 | 2.953898729115331 |
| 203.0 | 0.1347753271289409 | 1.0 | 0.5 | 0.2895321367059855 |
| 204.0 | 0.7338594799945437 | 1.0 | 0.5 | 2.647461677978153 |
| 205.0 | 0.11653928663743118 | 1.0 | 0.5 | 0.2478169105193333 |
| 206.0 | 0.939970706008818 | 1.0 | 0.5 | 5.6258452054414265 |
| 207.0 | 0.6523955046706064 | 1.0 | 0.5 | 2.1133799063928125 |
| 208.0 | 0.8032152138335183 | 1.0 | 0.5 | 3.2512892068337553 |
| 209.0 | 0.6707576127357098 | 1.0 | 0.5 | 2.22192212014176 |
| 210.0 | 0.706492182007378 | 1.0 | 0.5 | 2.451702005911791 |
| 211.0 | 0.7647771000645677 | 1.0 | 0.5 | 2.894443408052288 |
| 212.0 | 0.8173666289040515 | 1.0 | 0.5 | 3.400549144677044 |
| 213.0 | 0.21872300084295448 | 1.0 | 0.5 | 0.49365103921530656 |
| 214.0 | 0.7310001086753175 | 1.0 | 0.5 | 2.626088606755685 |
| 215.0 | 0.8697372961374548 | 1.0 | 0.5 | 4.076404137303028 |
| 216.0 | 0.16400157324713815 | 1.0 | 0.5 | 0.35825709554754664 |
| 217.0 | 0.443313209241608 | 1.0 | 0.5 | 1.1715050236598006 |
| 218.0 | 0.8053657385685586 | 1.0 | 0.5 | 3.2732661278566844 |
| 219.0 | 0.30079618388942353 | 1.0 | 0.5 | 0.7156259936633084 |
| 220.0 | 0.45365879994299696 | 1.0 | 0.5 | 1.2090231797639022 |
| 221.0 | 0.8069809893677921 | 1.0 | 0.5 | 3.2899331884862204 |
| 222.0 | 0.8245884511775761 | 1.0 | 0.5 | 3.4812407168767363 |
| 223.0 | 0.41234621682526684 | 1.0 | 0.5 | 1.063234617242673 |
| 224.0 | 0.7968250346184004 | 1.0 | 0.5 | 3.1873755454599793 |
| 225.0 | 0.3927417451741644 | 1.0 | 0.5 | 0.9976022348148476 |
| 226.0 | 0.2728256265562098 | 1.0 | 0.5 | 0.6371779535572102 |
| 227.0 | 8.203703710226784e-2 | 1.0 | 0.5 | 0.1711964692057045 |
| 228.0 | 0.37776869105901545 | 1.0 | 0.5 | 0.9488867520931888 |
| 229.0 | 0.629411417011517 | 1.0 | 0.5 | 1.9853255448725573 |
| 230.0 | 0.7501910378551001 | 1.0 | 0.5 | 2.774117609305619 |
| 231.0 | 0.5996333900313177 | 1.0 | 0.5 | 1.8307492534099181 |
| 232.0 | 6.970492186242305e-2 | 1.0 | 0.5 | 0.14450690968030883 |
| 233.0 | 0.8940723635978487 | 1.0 | 0.5 | 4.489998186900931 |
| 234.0 | 0.6914380476986998 | 1.0 | 0.5 | 2.3516652759309613 |
| 235.0 | 0.3650550811756098 | 1.0 | 0.5 | 0.9084340517211708 |
| 236.0 | 6.128389132423373e-2 | 1.0 | 0.5 | 0.12648435832908939 |
| 237.0 | 0.7680345331232672 | 1.0 | 0.5 | 2.9223335361285874 |
| 238.0 | 0.23058140936561866 | 1.0 | 0.5 | 0.5242402528938145 |
| 239.0 | 0.18527936511039267 | 1.0 | 0.5 | 0.40982000756014936 |
| 240.0 | 0.28710359124883345 | 1.0 | 0.5 | 0.676838316784801 |
| 241.0 | 0.2108511727487865 | 1.0 | 0.5 | 0.4736006964588179 |
| 242.0 | 0.4624966724507077 | 1.0 | 0.5 | 1.241640656420202 |
| 243.0 | 0.9811961480020145 | 1.0 | 0.5 | 7.947387073248349 |
| 244.0 | 0.8245811182215507 | 1.0 | 0.5 | 3.4811571100355176 |
| 245.0 | 0.8168888824501829 | 1.0 | 0.5 | 3.3953242213722077 |
| 246.0 | 0.6208021859846258 | 1.0 | 0.5 | 1.9393945466905502 |
| 247.0 | 0.6514815007857316 | 1.0 | 0.5 | 2.108127935592991 |
| 248.0 | 0.8002695403068458 | 1.0 | 0.5 | 3.221573045869622 |
| 249.0 | 0.44461502006124476 | 1.0 | 0.5 | 1.176187496315193 |
| 250.0 | 0.6612856951003029 | 1.0 | 0.5 | 2.1651965706603478 |
| 251.0 | 0.6397002571982446 | 1.0 | 0.5 | 2.041637950146084 |
| 252.0 | 0.41463824743842304 | 1.0 | 0.5 | 1.071050484843088 |
| 253.0 | 0.6862825956625578 | 1.0 | 0.5 | 2.3185253689821885 |
| 254.0 | 0.6912252005482692 | 1.0 | 0.5 | 2.3502861443094583 |
| 255.0 | 0.5891834968242496 | 1.0 | 0.5 | 1.779217256946675 |
| 256.0 | 0.8981246254935812 | 1.0 | 0.5 | 4.5680100625824815 |
| 257.0 | 0.15934873126214044 | 1.0 | 0.5 | 0.3471567352479698 |
| 258.0 | 0.11681184632506736 | 1.0 | 0.5 | 0.24843403301579353 |
| 259.0 | 0.14018427088376184 | 1.0 | 0.5 | 0.3020743623256845 |
| 260.0 | 0.3965186074249717 | 1.0 | 0.5 | 1.0100801428769541 |
| 261.0 | 0.7425604395714874 | 1.0 | 0.5 | 2.7139406064222333 |
| 262.0 | 0.8797958809508257 | 1.0 | 0.5 | 4.237127978344431 |
| 263.0 | 4.896102151223125e-2 | 1.0 | 0.5 | 0.10040046086720959 |
| 264.0 | 0.9921433329707989 | 1.0 | 0.5 | 9.692785609117339 |
| 265.0 | 0.7544869401235682 | 1.0 | 0.5 | 2.808810272219599 |
| 266.0 | 0.1498077093361896 | 1.0 | 0.5 | 0.324585461544541 |
| 267.0 | 5.85421235008452e-2 | 1.0 | 0.5 | 0.12065134545163093 |
| 268.0 | 0.3590373062728244 | 1.0 | 0.5 | 0.8895680477375398 |
| 269.0 | 0.6010966867818208 | 1.0 | 0.5 | 1.8380724284372671 |
| 270.0 | 0.5346840268041428 | 1.0 | 0.5 | 1.5300771838190839 |
| 271.0 | 0.3360717863227083 | 1.0 | 0.5 | 0.8191624945640179 |
| 272.0 | 0.23066472490507994 | 1.0 | 0.5 | 0.5244568321438122 |
| 273.0 | 0.7188347551898301 | 1.0 | 0.5 | 2.537625445330287 |
| 274.0 | 0.2730619118103619 | 1.0 | 0.5 | 0.6378279314989886 |
| 275.0 | 0.4176402740336481 | 1.0 | 0.5 | 1.081333872668498 |
| 276.0 | 0.928303417953019 | 1.0 | 0.5 | 5.270624405416689 |
| 277.0 | 0.9876818858628675 | 1.0 | 0.5 | 8.793368811756753 |
| 278.0 | 9.215204541688715e-2 | 1.0 | 0.5 | 0.1933567306039328 |
| 279.0 | 0.7034235613759132 | 1.0 | 0.5 | 2.4309005813064397 |
| 280.0 | 0.12983541875027071 | 1.0 | 0.5 | 0.2781458227487212 |
| 281.0 | 6.0020643624218994e-3 | 1.0 | 0.5 | 1.2040298302163377e-2 |
| 282.0 | 0.8488939892536755 | 1.0 | 0.5 | 3.7795472611438274 |
| 283.0 | 0.550628476873502 | 1.0 | 0.5 | 1.5998105753366676 |
| 284.0 | 0.771076875653035 | 1.0 | 0.5 | 2.948738066659822 |
| 285.0 | 0.4305445068367727 | 1.0 | 0.5 | 1.1261492997437392 |
| 286.0 | 0.6323928968906656 | 1.0 | 0.5 | 2.0014811315131316 |
| 287.0 | 0.5652982326601786 | 1.0 | 0.5 | 1.666190150683184 |
| 288.0 | 0.4599589658317047 | 1.0 | 0.5 | 1.2322203062206063 |
| 289.0 | 0.6425051193211746 | 1.0 | 0.5 | 2.0572684734602262 |
| 290.0 | 0.6380109379661701 | 1.0 | 0.5 | 2.032282565978041 |
| 291.0 | 0.664488833113522 | 1.0 | 0.5 | 2.1842000776258024 |
| 292.0 | 0.2078095802914609 | 1.0 | 0.5 | 0.4659069742823803 |
| 293.0 | 0.47914962275985984 | 1.0 | 0.5 | 1.3045849245480987 |
| 294.0 | 0.9137674412421714 | 1.0 | 0.5 | 4.901414921639761 |
| 295.0 | 0.557161675699462 | 1.0 | 0.5 | 1.6291010639640036 |
| 296.0 | 0.1678538026224934 | 1.0 | 0.5 | 0.3674942711914348 |
| 297.0 | 0.6661127048075736 | 1.0 | 0.5 | 2.193903564989207 |
| 298.0 | 0.28028333236305214 | 1.0 | 0.5 | 0.6577953231822851 |
| 299.0 | 0.1127138967969773 | 1.0 | 0.5 | 0.239175594311451 |
| 300.0 | 0.4734805364452864 | 1.0 | 0.5 | 1.2829339605523584 |
| 301.0 | 0.1909614967137384 | 1.0 | 0.5 | 0.4238175387544993 |
| 302.0 | 0.3910844543646478 | 1.0 | 0.5 | 0.9921513960128976 |
| 303.0 | 0.6138157305073431 | 1.0 | 0.5 | 1.902881282645127 |
| 304.0 | 0.11815755460603639 | 1.0 | 0.5 | 0.25148374454471695 |
| 305.0 | 0.8742519544410439 | 1.0 | 0.5 | 4.146950024795547 |
| 306.0 | 0.9498708378713526 | 1.0 | 0.5 | 5.986304723634267 |
| 307.0 | 0.5053975619060892 | 1.0 | 0.5 | 1.4080019889627051 |
| 308.0 | 0.583189875226974 | 1.0 | 0.5 | 1.7502489943121875 |
| 309.0 | 0.29821100432621517 | 1.0 | 0.5 | 0.7082449922032085 |
| 310.0 | 0.43496543585697767 | 1.0 | 0.5 | 1.1417367484649266 |
| 311.0 | 0.32002464140886755 | 1.0 | 0.5 | 0.7713974376691112 |
| 312.0 | 0.6612408447207943 | 1.0 | 0.5 | 2.164931760997916 |
| 313.0 | 0.1520042011418482 | 1.0 | 0.5 | 0.32975919475842747 |
| 314.0 | 0.5902935673386175 | 1.0 | 0.5 | 1.7846287872921907 |
| 315.0 | 0.5277040786515779 | 1.0 | 0.5 | 1.5002990756790346 |
| 316.0 | 0.6415483398508228 | 1.0 | 0.5 | 2.051922934454905 |
| 317.0 | 0.6652991333028194 | 1.0 | 0.5 | 2.1890361625870267 |
| 318.0 | 0.9380678887969137 | 1.0 | 0.5 | 5.563432948923322 |
| 319.0 | 0.5454079478330904 | 1.0 | 0.5 | 1.5767097017026976 |
| 320.0 | 0.6054467122591087 | 1.0 | 0.5 | 1.8600021423239732 |
| 321.0 | 0.35902852438102095 | 1.0 | 0.5 | 0.8895406457319968 |
| 322.0 | 0.8103657184494683 | 1.0 | 0.5 | 3.325315791279351 |
| 323.0 | 0.6828611148248662 | 1.0 | 0.5 | 2.2968309549442103 |
| 324.0 | 0.6176930897721922 | 1.0 | 0.5 | 1.9230631260737643 |
| 325.0 | 0.4206574039135048 | 1.0 | 0.5 | 1.09172254662181 |
| 326.0 | 0.790916069667254 | 1.0 | 0.5 | 3.1300390541795116 |
| 327.0 | 0.3040382358550948 | 1.0 | 0.5 | 0.7249211134603967 |
| 328.0 | 0.1663479330606389 | 1.0 | 0.5 | 0.36387829918735165 |
| 329.0 | 0.41926485267627167 | 1.0 | 0.5 | 1.086920965300184 |
| 330.0 | 0.24510074577757301 | 1.0 | 0.5 | 0.5623419535055211 |
| 331.0 | 0.6176839336221365 | 1.0 | 0.5 | 1.9230152271728091 |
| 332.0 | 0.4309814525635748 | 1.0 | 0.5 | 1.1276844976759688 |
| 333.0 | 0.4037262465331566 | 1.0 | 0.5 | 1.0341107989471487 |
| 334.0 | 0.8707915630110151 | 1.0 | 0.5 | 4.092656775986749 |
| 335.0 | 0.7258769054278222 | 1.0 | 0.5 | 2.5883560464266613 |
| 336.0 | 0.5204739363851301 | 1.0 | 0.5 | 1.4699140606351566 |
| 337.0 | 0.14381197075003016 | 1.0 | 0.5 | 0.31053053324689 |
| 338.0 | 0.49324394615909106 | 1.0 | 0.5 | 1.3594510946320792 |
| 339.0 | 0.20918760311088413 | 1.0 | 0.5 | 0.4693890228373402 |
| 340.0 | 5.669609099540762e-2 | 1.0 | 0.5 | 0.11673353873472628 |
| 341.0 | 0.19083303441937283 | 1.0 | 0.5 | 0.4234999961504061 |
| 342.0 | 0.9454331989548045 | 1.0 | 0.5 | 5.816659241255048 |
| 343.0 | 0.38569526224135775 | 1.0 | 0.5 | 0.9745283167820151 |
| 344.0 | 0.6796310845927921 | 1.0 | 0.5 | 2.276564173148733 |
| 345.0 | 0.6235762343862378 | 1.0 | 0.5 | 1.9540794680278915 |
| 346.0 | 0.7990196002839939 | 1.0 | 0.5 | 3.2090957790571237 |
| 347.0 | 0.8618995846099808 | 1.0 | 0.5 | 3.959548421356889 |
| 348.0 | 0.8602045679390417 | 1.0 | 0.5 | 3.935150249061157 |
| 349.0 | 0.804674131930031 | 1.0 | 0.5 | 3.2661719937507887 |
| 350.0 | 0.7643818367669448 | 1.0 | 0.5 | 2.8910854733146962 |
| 351.0 | 0.5152815340662249 | 1.0 | 0.5 | 1.4483740784013714 |
| 352.0 | 0.2913482624904987 | 1.0 | 0.5 | 0.6887821510275267 |
| 353.0 | 0.46899934718874836 | 1.0 | 0.5 | 1.2659840566824667 |
| 354.0 | 0.7668529548190675 | 1.0 | 0.5 | 2.9121718585522602 |
| 355.0 | 0.3885396070446382 | 1.0 | 0.5 | 0.9838101925254337 |
| 356.0 | 0.3037782773941975 | 1.0 | 0.5 | 0.7241742048340765 |
| 357.0 | 0.43017921035799733 | 1.0 | 0.5 | 1.1248667434514426 |
| 358.0 | 0.7708537153470264 | 1.0 | 0.5 | 2.9467893635220097 |
| 359.0 | 0.8527551676949937 | 1.0 | 0.5 | 3.831317103538236 |
| 360.0 | 0.25699551313840663 | 1.0 | 0.5 | 0.5941063908758737 |
| 361.0 | 0.5041581451079085 | 1.0 | 0.5 | 1.402996488082308 |
| 362.0 | 0.618456487694451 | 1.0 | 0.5 | 1.9270607578294254 |
| 363.0 | 0.7960632491263456 | 1.0 | 0.5 | 3.1798907558974308 |
| 364.0 | 0.5781936153609195 | 1.0 | 0.5 | 1.7264177488035175 |
| 365.0 | 0.9453056623315601 | 1.0 | 0.5 | 5.811990182057274 |
| 366.0 | 0.7906975259807959 | 1.0 | 0.5 | 3.127949658309461 |
| 367.0 | 0.4815660612686351 | 1.0 | 0.5 | 1.3138853357104474 |
| 368.0 | 0.7136937009564847 | 1.0 | 0.5 | 2.5013861311419405 |
| 369.0 | 0.36596944666057896 | 1.0 | 0.5 | 0.9113162686379586 |
| 370.0 | 0.6852376565043472 | 1.0 | 0.5 | 2.3118747800033814 |
| 371.0 | 8.659931015526856e-2 | 1.0 | 0.5 | 0.18116124596440175 |
| 372.0 | 0.39171379465377476 | 1.0 | 0.5 | 0.9942195505968021 |
| 373.0 | 0.4376432657953504 | 1.0 | 0.5 | 1.151237744182134 |
| 374.0 | 0.9338692749582953 | 1.0 | 0.5 | 5.432243626941651 |
| 375.0 | 0.8032814270489331 | 1.0 | 0.5 | 3.2519622706285665 |
| 376.0 | 0.3074481498223396 | 1.0 | 0.5 | 0.734744339641591 |
| 377.0 | 0.9553384399577382 | 1.0 | 0.5 | 6.21728420329452 |
| 378.0 | 0.3008687511755189 | 1.0 | 0.5 | 0.7158335756312652 |
| 379.0 | 0.6912255340556935 | 1.0 | 0.5 | 2.3502883045090925 |
| 380.0 | 0.31120482559269236 | 1.0 | 0.5 | 0.7456226633794968 |
| 381.0 | 0.6904222818554248 | 1.0 | 0.5 | 2.34509221934756 |
| 382.0 | 0.629845047659601 | 1.0 | 0.5 | 1.9876671418055656 |
| 383.0 | 0.8445543691848751 | 1.0 | 0.5 | 3.7229184991236512 |
| 384.0 | 0.8802395456238672 | 1.0 | 0.5 | 4.244523489644153 |
| 385.0 | 0.2554039082432842 | 1.0 | 0.5 | 0.5898267326791469 |
| 386.0 | 0.6976316813382629 | 1.0 | 0.5 | 2.392218812990738 |
| 387.0 | 0.20519475742157367 | 1.0 | 0.5 | 0.4593163444571233 |
| 388.0 | 0.22062697896423733 | 1.0 | 0.5 | 0.49853100343857476 |
| 389.0 | 0.1184163258432177 | 1.0 | 0.5 | 0.25207071835931527 |
| 390.0 | 0.13338703832783338 | 1.0 | 0.5 | 0.28632562572414416 |
| 391.0 | 0.5999899425385322 | 1.0 | 0.5 | 1.832531177073164 |
| 392.0 | 0.10891466860232824 | 1.0 | 0.5 | 0.23063017145851863 |
| 393.0 | 0.46849300971148855 | 1.0 | 0.5 | 1.264077858571318 |
| 394.0 | 0.15435965027359666 | 1.0 | 0.5 | 0.33532225658224557 |
| 395.0 | 5.559570749415488e-3 | 1.0 | 0.5 | 1.1150165365434313e-2 |
| 396.0 | 0.5983977207634705 | 1.0 | 0.5 | 1.824586070461916 |
| 397.0 | 0.6638816354984965 | 1.0 | 0.5 | 2.18058381150243 |
| 398.0 | 0.16106096414828341 | 1.0 | 0.5 | 0.35123447605085273 |
| 399.0 | 0.8657227371429501 | 1.0 | 0.5 | 4.0156969818497394 |
| 400.0 | 0.5445065402949494 | 1.0 | 0.5 | 1.5727478417625103 |
| 401.0 | 0.7929675086675783 | 1.0 | 0.5 | 3.14975907015121 |
| 402.0 | 0.9211487031082005 | 1.0 | 0.5 | 5.080383036253744 |
| 403.0 | 0.3673668809559498 | 1.0 | 0.5 | 0.9157292312666222 |
| 404.0 | 0.936472897914436 | 1.0 | 0.5 | 5.512577319283104 |
| 405.0 | 0.6470067888654516 | 1.0 | 0.5 | 2.082612908294473 |
| 406.0 | 0.8063760471139401 | 1.0 | 0.5 | 3.283674776415543 |
| 407.0 | 0.4579026360474032 | 1.0 | 0.5 | 1.2246193107731846 |
| 408.0 | 0.8186450229685123 | 1.0 | 0.5 | 3.4145979385749015 |
| 409.0 | 0.19945866840373805 | 1.0 | 0.5 | 0.4449342313061569 |
| 410.0 | 0.476087803931892 | 1.0 | 0.5 | 1.2928623469143121 |
| 411.0 | 0.41152484190493444 | 1.0 | 0.5 | 1.0604411308312933 |
| 412.0 | 5.955959504676034e-2 | 1.0 | 0.5 | 0.12281399505938953 |
| 413.0 | 0.8894759497512487 | 1.0 | 0.5 | 4.405044264793075 |
| 414.0 | 0.20421247549708854 | 1.0 | 0.5 | 0.4568461155721755 |
| 415.0 | 0.34236481139671393 | 1.0 | 0.5 | 0.8382098520440895 |
| 416.0 | 0.7010695788894424 | 1.0 | 0.5 | 2.4150888759747895 |
| 417.0 | 0.5457997824522273 | 1.0 | 0.5 | 1.5784343406833543 |
| 418.0 | 0.35467887211133586 | 1.0 | 0.5 | 0.8760144267765 |
| 419.0 | 6.117319191943771e-2 | 1.0 | 0.5 | 0.1262485194485262 |
| 420.0 | 0.5982204485154874 | 1.0 | 0.5 | 1.823703440339693 |
| 421.0 | 0.7438477718056294 | 1.0 | 0.5 | 2.7239667396948914 |
| 422.0 | 0.5235593195439584 | 1.0 | 0.5 | 1.482824107522302 |
| 423.0 | 0.27893973324859367 | 1.0 | 0.5 | 0.6540651149262309 |
| 424.0 | 0.598812224506282 | 1.0 | 0.5 | 1.8266513864294174 |
| 425.0 | 0.532862681796187 | 1.0 | 0.5 | 1.5222640425450091 |
| 426.0 | 0.5259874810383671 | 1.0 | 0.5 | 1.4930430926487426 |
| 427.0 | 0.9772073097054985 | 1.0 | 0.5 | 7.562630791087247 |
| 428.0 | 1.969623137057286e-2 | 1.0 | 0.5 | 3.9785574716920596e-2 |
| 429.0 | 0.9349901842305819 | 1.0 | 0.5 | 5.466434017300876 |
| 430.0 | 0.28420317357092906 | 1.0 | 0.5 | 0.6687178285202172 |
| 431.0 | 6.720830727942095e-2 | 1.0 | 0.5 | 0.13914673834217078 |
| 432.0 | 0.2393537952344139 | 1.0 | 0.5 | 0.547173875095271 |
| 433.0 | 0.3699192933800981 | 1.0 | 0.5 | 0.9238147241110459 |
| 434.0 | 0.7120207553475696 | 1.0 | 0.5 | 2.4897337372444324 |
| 435.0 | 0.892762209686267 | 1.0 | 0.5 | 4.465413141766478 |
| 436.0 | 0.7374650277069859 | 1.0 | 0.5 | 2.674741956083161 |
| 437.0 | 0.1520795313631268 | 1.0 | 0.5 | 0.32993686914665515 |
| 438.0 | 0.7870807087804627 | 1.0 | 0.5 | 3.0936841990824044 |
| 439.0 | 0.40930075403402244 | 1.0 | 0.5 | 1.052896562358053 |
| 440.0 | 0.5571955213247204 | 1.0 | 0.5 | 1.6292539275119766 |
| 441.0 | 0.6368618886962935 | 1.0 | 0.5 | 2.025944090334666 |
| 442.0 | 0.9913188397640513 | 1.0 | 0.5 | 9.493200183013185 |
| 443.0 | 9.212811877039273e-2 | 1.0 | 0.5 | 0.19330402060754706 |
| 444.0 | 0.14309308309408164 | 1.0 | 0.5 | 0.30885196265800263 |
| 445.0 | 0.2731389341195851 | 1.0 | 0.5 | 0.6380398515818411 |
| 446.0 | 0.6124978147948839 | 1.0 | 0.5 | 1.896067581931253 |
| 447.0 | 0.35873719923123937 | 1.0 | 0.5 | 0.8886318409678158 |
| 448.0 | 0.5986178642602705 | 1.0 | 0.5 | 1.8256826969877096 |
| 449.0 | 0.6186407292470238 | 1.0 | 0.5 | 1.928026760629499 |
| 450.0 | 0.15268791888277677 | 1.0 | 0.5 | 0.3313723950179759 |
| 451.0 | 0.38273380273729696 | 1.0 | 0.5 | 0.964909820184606 |
| 452.0 | 0.36369916355639675 | 1.0 | 0.5 | 0.9041676284028476 |
| 453.0 | 0.3354447018504748 | 1.0 | 0.5 | 0.8172743732189655 |
| 454.0 | 0.15961744162222669 | 1.0 | 0.5 | 0.3477961283596146 |
| 455.0 | 0.13383128505824216 | 1.0 | 0.5 | 0.2873511368918529 |
| 456.0 | 0.4205893728555665 | 1.0 | 0.5 | 1.0914877043563385 |
| 457.0 | 4.308409558865123e-2 | 1.0 | 0.5 | 8.807953113732708e-2 |
| 458.0 | 0.9867886319473154 | 1.0 | 0.5 | 8.653355207822308 |
| 459.0 | 0.21308112017425984 | 1.0 | 0.5 | 0.47926022213400593 |
| 460.0 | 0.5252379551321749 | 1.0 | 0.5 | 1.489883117283927 |
| 461.0 | 0.19847529538952913 | 1.0 | 0.5 | 0.44247896887505517 |
| 462.0 | 0.9845113769017905 | 1.0 | 0.5 | 8.335299036452144 |
| 463.0 | 0.31745462989533113 | 1.0 | 0.5 | 0.7638525555772786 |
| 464.0 | 0.9201422937734026 | 1.0 | 0.5 | 5.055017800382695 |
| 465.0 | 9.721172337822526e-2 | 1.0 | 0.5 | 0.20453443939861016 |
| 466.0 | 0.7689078447665054 | 1.0 | 0.5 | 2.929877415222341 |
| 467.0 | 0.24648775815946677 | 1.0 | 0.5 | 0.5660200288223087 |
| 468.0 | 0.916022820452202 | 1.0 | 0.5 | 4.954420378196766 |
| 469.0 | 0.2778277986897081 | 1.0 | 0.5 | 0.6509833251148092 |
| 470.0 | 0.4942978031059393 | 1.0 | 0.5 | 1.3636146532842373 |
| 471.0 | 0.9984512492957738 | 1.0 | 0.5 | 12.940613340629659 |
| 472.0 | 0.4858235291350349 | 1.0 | 0.5 | 1.3303774878393302 |
| 473.0 | 0.8835933750287146 | 1.0 | 0.5 | 4.301331659487115 |
| 474.0 | 0.46197108064871006 | 1.0 | 0.5 | 1.2396859336479154 |
| 475.0 | 0.9192258975621658 | 1.0 | 0.5 | 5.03219775842808 |
| 476.0 | 8.037605837101236e-2 | 1.0 | 0.5 | 0.1675809032055014 |
| 477.0 | 0.25998038300532433 | 1.0 | 0.5 | 0.6021571673660531 |
| 478.0 | 8.193099331692211e-2 | 1.0 | 0.5 | 0.17096544101617278 |
| 479.0 | 0.42471634873931763 | 1.0 | 0.5 | 1.10578410656028 |
| 480.0 | 0.8642188472201628 | 1.0 | 0.5 | 3.9934217201768596 |
| 481.0 | 0.5922467985366169 | 1.0 | 0.5 | 1.7941863719537383 |
| 482.0 | 0.3473229907714379 | 1.0 | 0.5 | 0.8533457961268887 |
| 483.0 | 0.8501770636465942 | 1.0 | 0.5 | 3.79660221289208 |
| 484.0 | 0.3700990562462938 | 1.0 | 0.5 | 0.9243854081916025 |
| 485.0 | 0.7997661351181974 | 1.0 | 0.5 | 3.2165385423047885 |
| 486.0 | 0.36852627173430763 | 1.0 | 0.5 | 0.919397880339802 |
| 487.0 | 0.10890619794395184 | 1.0 | 0.5 | 0.2306111595461526 |
| 488.0 | 0.20256490947332173 | 1.0 | 0.5 | 0.4527096776493963 |
| 489.0 | 0.2124771387757547 | 1.0 | 0.5 | 0.4777257571553782 |
| 490.0 | 0.26912032071402336 | 1.0 | 0.5 | 0.6270128604000103 |
| 491.0 | 0.12507142332737353 | 1.0 | 0.5 | 0.26722604523203036 |
| 492.0 | 0.5626587494199857 | 1.0 | 0.5 | 1.6540829896993592 |
| 493.0 | 0.3595244066447809 | 1.0 | 0.5 | 0.8910885279676917 |
| 494.0 | 0.7039520759639467 | 1.0 | 0.5 | 2.43446786447231 |
| 495.0 | 0.3282973606753571 | 1.0 | 0.5 | 0.7958790747928882 |
| 496.0 | 0.9492401902849879 | 1.0 | 0.5 | 5.961300769724403 |
| 497.0 | 0.5776806684742387 | 1.0 | 0.5 | 1.7239870826598631 |
| 498.0 | 8.602358898259499e-2 | 1.0 | 0.5 | 0.17990103275181332 |
| 499.0 | 0.33669894919195487 | 1.0 | 0.5 | 0.8210526364790832 |
| 500.0 | 0.6721507736564122 | 1.0 | 0.5 | 2.230402904126076 |
| 501.0 | 0.3243989277551754 | 1.0 | 0.5 | 0.7843050138993118 |
| 502.0 | 0.6964431127989001 | 1.0 | 0.5 | 2.3843724976216683 |
| 503.0 | 0.6969352554489141 | 1.0 | 0.5 | 2.3876176358246415 |
| 504.0 | 0.14810448993792713 | 1.0 | 0.5 | 0.32058280089206137 |
| 505.0 | 0.7040859012134064 | 1.0 | 0.5 | 2.4353721471565364 |
| 506.0 | 0.5650416097485739 | 1.0 | 0.5 | 1.6650098141261362 |
| 507.0 | 0.9423602259615177 | 1.0 | 0.5 | 5.707084856158279 |
| 508.0 | 0.1457136672809557 | 1.0 | 0.5 | 0.3149777143488425 |
| 509.0 | 0.9728185830324114 | 1.0 | 0.5 | 7.210443477776949 |
| 510.0 | 0.664554173487734 | 1.0 | 0.5 | 2.184589613021134 |
| 511.0 | 0.8972002827069626 | 1.0 | 0.5 | 4.549945352065167 |
| 512.0 | 0.10391946735687141 | 1.0 | 0.5 | 0.21944997972824995 |
| 513.0 | 0.6139748182828372 | 1.0 | 0.5 | 1.9037053480719126 |
| 514.0 | 0.9562959842747931 | 1.0 | 0.5 | 6.2606305761543375 |
| 515.0 | 0.7873081285663863 | 1.0 | 0.5 | 3.0958215472304182 |
| 516.0 | 0.7102461915924079 | 1.0 | 0.5 | 2.4774473061556894 |
| 517.0 | 9.768924697127424e-2 | 1.0 | 0.5 | 0.20559260539691734 |
| 518.0 | 0.9959913646270231 | 1.0 | 0.5 | 11.038608803143358 |
| 519.0 | 0.8315295817001068 | 1.0 | 0.5 | 3.5619902073061875 |
| 520.0 | 0.6481127649000105 | 1.0 | 0.5 | 2.0888890190763707 |
| 521.0 | 0.8902103325300512 | 1.0 | 0.5 | 4.41837771504554 |
| 522.0 | 0.21898585001825865 | 1.0 | 0.5 | 0.49432402306451856 |
| 523.0 | 0.2924598358723359 | 1.0 | 0.5 | 0.6919217639820207 |
| 524.0 | 0.19234155569895572 | 1.0 | 0.5 | 0.42723205456090335 |
| 525.0 | 0.2965414347587164 | 1.0 | 0.5 | 0.7034926043691805 |
| 526.0 | 0.8461825459749209 | 1.0 | 0.5 | 3.743977486281952 |
| 527.0 | 0.9438143991824705 | 1.0 | 0.5 | 5.758189536243844 |
| 528.0 | 0.277053878625964 | 1.0 | 0.5 | 0.6488411610426124 |
| 529.0 | 3.702411228537372e-2 | 1.0 | 0.5 | 7.545381243084566e-2 |
| 530.0 | 0.7549740371282225 | 1.0 | 0.5 | 2.812782206293431 |
| 531.0 | 0.9485773539699562 | 1.0 | 0.5 | 5.93535323861532 |
| 532.0 | 0.29574446713166325 | 1.0 | 0.5 | 0.7012280317169043 |
| 533.0 | 0.9088202624787483 | 1.0 | 0.5 | 4.789845165777453 |
| 534.0 | 0.5778841474515926 | 1.0 | 0.5 | 1.7249509410358743 |
| 535.0 | 0.6712366768025961 | 1.0 | 0.5 | 2.2248343389502865 |
| 536.0 | 0.7896613204265914 | 1.0 | 0.5 | 3.1180725749838563 |
| 537.0 | 0.44232973194629155 | 1.0 | 0.5 | 1.167974817455669 |
| 538.0 | 0.2950251626322148 | 1.0 | 0.5 | 0.6991863369674638 |
| 539.0 | 0.33978332513204 | 1.0 | 0.5 | 0.8303744051685533 |
| 540.0 | 0.451840175664563 | 1.0 | 0.5 | 1.2023767686172793 |
| 541.0 | 0.5040620157200235 | 1.0 | 0.5 | 1.4026087835358383 |
| 542.0 | 0.4050939433128876 | 1.0 | 0.5 | 1.0387035476458735 |
| 543.0 | 0.7242256573412612 | 1.0 | 0.5 | 2.576344693487865 |
| 544.0 | 0.19433356288205317 | 1.0 | 0.5 | 0.43217094372907366 |
| 545.0 | 0.5609254471377881 | 1.0 | 0.5 | 1.6461721120759583 |
| 546.0 | 0.8112489165094665 | 1.0 | 0.5 | 3.334652301044563 |
| 547.0 | 0.6183998569085001 | 1.0 | 0.5 | 1.9267639288607015 |
| 548.0 | 8.278159521102524e-2 | 1.0 | 0.5 | 0.172819323846057 |
| 549.0 | 0.9080107501828416 | 1.0 | 0.5 | 4.772167117148127 |
| 550.0 | 8.630915426350283e-2 | 1.0 | 0.5 | 0.18052601584362368 |
| 551.0 | 2.363046842986627e-2 | 1.0 | 0.5 | 4.782829162977301e-2 |
| 552.0 | 0.7713162122169146 | 1.0 | 0.5 | 2.9508301380805873 |
| 553.0 | 0.5413318956914454 | 1.0 | 0.5 | 1.5588568295364378 |
| 554.0 | 2.4576624796894375e-2 | 1.0 | 0.5 | 4.976734251546619e-2 |
| 555.0 | 0.18311117432601154 | 1.0 | 0.5 | 0.4045045393355597 |
| 556.0 | 0.6440773592850199 | 1.0 | 0.5 | 2.066083746211768 |
| 557.0 | 0.12209494488261241 | 1.0 | 0.5 | 0.2604336577476676 |
| 558.0 | 0.9701251894749069 | 1.0 | 0.5 | 7.0214792220137445 |
| 559.0 | 0.6448560433358252 | 1.0 | 0.5 | 2.070464119948625 |
| 560.0 | 0.7768842057191598 | 1.0 | 0.5 | 3.000128770848919 |
| 561.0 | 0.4993805198337039 | 1.0 | 0.5 | 1.3838179742107009 |
| 562.0 | 0.6206551405201586 | 1.0 | 0.5 | 1.9386191363072078 |
| 563.0 | 0.9109522404045987 | 1.0 | 0.5 | 4.837164857028188 |
| 564.0 | 3.416680098135194e-2 | 1.0 | 0.5 | 6.952826300492072e-2 |
| 565.0 | 0.8357048037446909 | 1.0 | 0.5 | 3.6121809840180283 |
| 566.0 | 0.30374995067176247 | 1.0 | 0.5 | 0.7240928337843795 |
| 567.0 | 0.4748185587502922 | 1.0 | 0.5 | 1.2880229474337281 |
| 568.0 | 0.20889537215882936 | 1.0 | 0.5 | 0.4686500941810061 |
| 569.0 | 5.358054574481308e-2 | 1.0 | 0.5 | 0.1101388209384597 |
| 570.0 | 0.12048364905194009 | 1.0 | 0.5 | 0.2567662475827379 |
| 571.0 | 0.22037400307188337 | 1.0 | 0.5 | 0.49788193081784987 |
| 572.0 | 0.14276196728749835 | 1.0 | 0.5 | 0.30807929565390746 |
| 573.0 | 2.3634214360506167e-3 | 1.0 | 0.5 | 4.732437449620207e-3 |
| 574.0 | 0.1413412778366614 | 1.0 | 0.5 | 0.30476746521554565 |
| 575.0 | 0.23731288268207418 | 1.0 | 0.5 | 0.5418148016899048 |
| 576.0 | 0.6428549206111174 | 1.0 | 0.5 | 2.0592263898232908 |
| 577.0 | 0.8041131622481903 | 1.0 | 0.5 | 3.260436289792602 |
| 578.0 | 0.6125002351635738 | 1.0 | 0.5 | 1.8960800741253165 |
| 579.0 | 0.7489936846591467 | 1.0 | 0.5 | 2.7645543588926955 |
| 580.0 | 0.2730932235042949 | 1.0 | 0.5 | 0.6379140801512924 |
| 581.0 | 0.43966710643970464 | 1.0 | 0.5 | 1.1584484381675355 |
| 582.0 | 0.43992238578202103 | 1.0 | 0.5 | 1.1593598160775482 |
| 583.0 | 9.590329301282519e-2 | 1.0 | 0.5 | 0.20163789511168256 |
| 584.0 | 0.6408237142175903 | 1.0 | 0.5 | 2.047883928540399 |
| 585.0 | 0.9397767089707971 | 1.0 | 0.5 | 5.6193922146376165 |
| 586.0 | 0.6198457391445756 | 1.0 | 0.5 | 1.9343563180348438 |
| 587.0 | 0.7494635303034605 | 1.0 | 0.5 | 2.768301562886258 |
| 588.0 | 0.2529616306024075 | 1.0 | 0.5 | 0.5832774610260537 |
| 589.0 | 0.3097765196461092 | 1.0 | 0.5 | 0.7414796985185217 |
| 590.0 | 0.8543318357710298 | 1.0 | 0.5 | 3.8528481837188475 |
| 591.0 | 0.2080392634624041 | 1.0 | 0.5 | 0.46648692695077815 |
| 592.0 | 0.871478415863244 | 1.0 | 0.5 | 4.103316837632778 |
| 593.0 | 0.6997344903344965 | 1.0 | 0.5 | 2.406176327035363 |
| 594.0 | 0.35411295503798623 | 1.0 | 0.5 | 0.8742612869761638 |
| 595.0 | 0.3264527627525824 | 1.0 | 0.5 | 0.7903942972479759 |
| 596.0 | 0.588832935143833 | 1.0 | 0.5 | 1.7775113264862386 |
| 597.0 | 0.5313844088978195 | 1.0 | 0.5 | 1.5159449635940268 |
| 598.0 | 0.4567316483098015 | 1.0 | 0.5 | 1.2203037582361256 |
| 599.0 | 0.8195841922237239 | 1.0 | 0.5 | 3.4249820978765353 |
| 600.0 | 0.14120568910841114 | 1.0 | 0.5 | 0.3044516749705204 |
| 601.0 | 0.571078352042275 | 1.0 | 0.5 | 1.6929620310555946 |
| 602.0 | 0.8648007170877782 | 1.0 | 0.5 | 4.002010838582048 |
| 603.0 | 0.6132893411625672 | 1.0 | 0.5 | 1.9001570343320942 |
| 604.0 | 5.4905745353656554e-2 | 1.0 | 0.5 | 0.11294123219126587 |
| 605.0 | 0.8027829474255819 | 1.0 | 0.5 | 3.246900734102227 |
| 606.0 | 0.5378605391250034 | 1.0 | 0.5 | 1.5437771401723677 |
| 607.0 | 0.14159974182553825 | 1.0 | 0.5 | 0.30536957380654617 |
| 608.0 | 3.88273370629193e-2 | 1.0 | 0.5 | 7.920243216155093e-2 |
| 609.0 | 0.18933424250449915 | 1.0 | 0.5 | 0.41979889215060434 |
| 610.0 | 0.8214874960728856 | 1.0 | 0.5 | 3.4461932604079712 |
| 611.0 | 0.8547738296139001 | 1.0 | 0.5 | 3.8589259120265513 |
| 612.0 | 0.9683432909151438 | 1.0 | 0.5 | 6.905610350902737 |
| 613.0 | 0.3371545375376538 | 1.0 | 0.5 | 0.8224268085153192 |
| 614.0 | 0.1722057249430613 | 1.0 | 0.5 | 0.3779812310887134 |
| 615.0 | 0.6365358781930919 | 1.0 | 0.5 | 2.024149377848399 |
| 616.0 | 0.11019387806573755 | 1.0 | 0.5 | 0.2335033610198907 |
| 617.0 | 0.9300093879772685 | 1.0 | 0.5 | 5.318788319775677 |
| 618.0 | 0.19234789704689392 | 1.0 | 0.5 | 0.4272477576662883 |
| 619.0 | 0.5955191207408357 | 1.0 | 0.5 | 1.810301627329452 |
| 620.0 | 8.656460793719478e-2 | 1.0 | 0.5 | 0.18108526275294312 |
| 621.0 | 0.21393028095509692 | 1.0 | 0.5 | 0.48141957882339587 |
| 622.0 | 0.2618275890802019 | 1.0 | 0.5 | 0.6071557250676938 |
| 623.0 | 0.879228176345618 | 1.0 | 0.5 | 4.227704537164264 |
| 624.0 | 0.8966482356381307 | 1.0 | 0.5 | 4.539233842567759 |
| 625.0 | 0.2536084981294937 | 1.0 | 0.5 | 0.5850100300069871 |
| 626.0 | 0.7129461615435944 | 1.0 | 0.5 | 2.4961709807499295 |
| 627.0 | 0.5246672381070606 | 1.0 | 0.5 | 1.4874803377309904 |
| 628.0 | 0.6466749238349577 | 1.0 | 0.5 | 2.0807335003228427 |
| 629.0 | 2.012080224756596e-2 | 1.0 | 0.5 | 4.065196503030301e-2 |
| 630.0 | 0.5695158610428649 | 1.0 | 0.5 | 1.6856895981964555 |
| 631.0 | 0.468651794622835 | 1.0 | 0.5 | 1.2646754372938958 |
| 632.0 | 0.8356263017963113 | 1.0 | 0.5 | 3.6112255915096907 |
| 633.0 | 0.2352932891569065 | 1.0 | 0.5 | 0.5365258063701341 |
| 634.0 | 0.2955805038834337 | 1.0 | 0.5 | 0.7007624502432606 |
| 635.0 | 0.984241737362848 | 1.0 | 0.5 | 8.300780878777072 |
| 636.0 | 0.21970788844780853 | 1.0 | 0.5 | 0.4961738548337551 |
| 637.0 | 0.546038569904511 | 1.0 | 0.5 | 1.579486080573081 |
| 638.0 | 0.5789663680615773 | 1.0 | 0.5 | 1.7300851253019511 |
| 639.0 | 0.2001721235450401 | 1.0 | 0.5 | 0.44671745778909 |
| 640.0 | 2.3472786733157558e-2 | 1.0 | 0.5 | 4.750532176980833e-2 |
| 641.0 | 0.5048312152099426 | 1.0 | 0.5 | 1.405713190319697 |
| 642.0 | 0.461772341145953 | 1.0 | 0.5 | 1.23894730121011 |
| 643.0 | 0.9608122973879519 | 1.0 | 0.5 | 6.478784580557936 |
| 644.0 | 0.3238394511319469 | 1.0 | 0.5 | 0.7826494656238266 |
| 645.0 | 0.2904311758595399 | 1.0 | 0.5 | 0.6861955667300356 |
| 646.0 | 0.9065763676999057 | 1.0 | 0.5 | 4.741221886534362 |
| 647.0 | 0.7720143320135644 | 1.0 | 0.5 | 2.9569450234241326 |
| 648.0 | 0.3422701885724606 | 1.0 | 0.5 | 0.8379221058309415 |
| 649.0 | 0.5405733785688802 | 1.0 | 0.5 | 1.5555520842900783 |
| 650.0 | 6.911595980644125e-2 | 1.0 | 0.5 | 0.14324112699482708 |
| 651.0 | 8.274269516507615e-2 | 1.0 | 0.5 | 0.17273450387106176 |
| 652.0 | 0.7110560538806846 | 1.0 | 0.5 | 2.483045135493311 |
| 653.0 | 0.9749904183375128 | 1.0 | 0.5 | 7.376992522084581 |
| 654.0 | 0.23136125851727452 | 1.0 | 0.5 | 0.5262683937236794 |
| 655.0 | 0.74932144448296 | 1.0 | 0.5 | 2.7671676318015224 |
| 656.0 | 0.3604597449042817 | 1.0 | 0.5 | 0.8940114243587316 |
| 657.0 | 0.5414116079321439 | 1.0 | 0.5 | 1.559204441098587 |
| 658.0 | 9.970671477777193e-2 | 1.0 | 0.5 | 0.21006939254716955 |
| 659.0 | 0.3135343711685803 | 1.0 | 0.5 | 0.7523982445592757 |
| 660.0 | 0.5937106288155716 | 1.0 | 0.5 | 1.8013792726985414 |
| 661.0 | 0.9689457835594395 | 1.0 | 0.5 | 6.944041369269695 |
| 662.0 | 0.16921934135688987 | 1.0 | 0.5 | 0.37077893530371697 |
| 663.0 | 0.9602571403398084 | 1.0 | 0.5 | 6.450650170415116 |
| 664.0 | 0.566521758396436 | 1.0 | 0.5 | 1.671827352801084 |
| 665.0 | 0.3115691390254367 | 1.0 | 0.5 | 0.7466807712917275 |
| 666.0 | 0.4920437957259731 | 1.0 | 0.5 | 1.3547200943582611 |
| 667.0 | 0.3992822271426719 | 1.0 | 0.5 | 1.0192601013057756 |
| 668.0 | 0.13709913454689215 | 1.0 | 0.5 | 0.29491093301925064 |
| 669.0 | 0.2917815657194186 | 1.0 | 0.5 | 0.6900054197533566 |
| 670.0 | 0.2269241072330097 | 1.0 | 0.5 | 0.5147561113931132 |
| 671.0 | 0.9828401338468801 | 1.0 | 0.5 | 8.130363969774953 |
| 672.0 | 0.8451764753478024 | 1.0 | 0.5 | 3.7309387226700608 |
| 673.0 | 0.6870405725176627 | 1.0 | 0.5 | 2.32336344300211 |
| 674.0 | 0.6651080818045797 | 1.0 | 0.5 | 2.1878948629330592 |
| 675.0 | 0.6851876699596857 | 1.0 | 0.5 | 2.311557190704652 |
| 676.0 | 5.9944766762465074e-2 | 1.0 | 0.5 | 0.12363329336196191 |
| 677.0 | 0.19523371546086676 | 1.0 | 0.5 | 0.434406746964942 |
| 678.0 | 0.9058818397272592 | 1.0 | 0.5 | 4.726408523854953 |
| 679.0 | 0.786600795252582 | 1.0 | 0.5 | 3.0891813334066978 |
| 680.0 | 0.4594518517519819 | 1.0 | 0.5 | 1.230343129860883 |
| 681.0 | 0.6313549186399953 | 1.0 | 0.5 | 1.9958418738769428 |
| 682.0 | 0.6121253287236518 | 1.0 | 0.5 | 1.8941460074257859 |
| 683.0 | 0.6481678392735192 | 1.0 | 0.5 | 2.089202066429891 |
| 684.0 | 0.8017662624162653 | 1.0 | 0.5 | 3.236616903348518 |
| 685.0 | 0.823698989553727 | 1.0 | 0.5 | 3.471124916384335 |
| 686.0 | 0.6000121001021462 | 1.0 | 0.5 | 1.8326419651741377 |
| 687.0 | 0.6734003919211814 | 1.0 | 0.5 | 2.2380405969002193 |
| 688.0 | 0.9386972793686914 | 1.0 | 0.5 | 5.583862109569779 |
| 689.0 | 0.6407234827488232 | 1.0 | 0.5 | 2.047325887878609 |
| 690.0 | 0.11784320922460867 | 1.0 | 0.5 | 0.25077094291009105 |
| 691.0 | 0.9292956025831624 | 1.0 | 0.5 | 5.298495019511766 |
| 692.0 | 0.624574711672123 | 1.0 | 0.5 | 1.9593915868243066 |
| 693.0 | 0.5318437450512953 | 1.0 | 0.5 | 1.517906321387711 |
| 694.0 | 0.5354746045292715 | 1.0 | 0.5 | 1.533478099038687 |
| 695.0 | 0.6287567299260138 | 1.0 | 0.5 | 1.981795433552752 |
| 696.0 | 3.3358509265942526e-2 | 1.0 | 0.5 | 6.785519216610078e-2 |
| 697.0 | 4.341457946982463e-2 | 1.0 | 0.5 | 8.87703775561965e-2 |
| 698.0 | 0.6857881743575658 | 1.0 | 0.5 | 2.315375833120105 |
| 699.0 | 0.527537235441933 | 1.0 | 0.5 | 1.4995926806434843 |
| 700.0 | 0.8695893937304817 | 1.0 | 0.5 | 4.0741345927490515 |
| 701.0 | 0.992196826147401 | 1.0 | 0.5 | 9.706449447740578 |
| 702.0 | 0.29926969636495526 | 1.0 | 0.5 | 0.7112643937625094 |
| 703.0 | 0.32536863346156475 | 1.0 | 0.5 | 0.7871777218724096 |
| 704.0 | 0.6007985546893826 | 1.0 | 0.5 | 1.836578228067607 |
| 705.0 | 3.135161402950315e-2 | 1.0 | 0.5 | 6.370719143508623e-2 |
| 706.0 | 0.42624013002321437 | 1.0 | 0.5 | 1.1110886303628529 |
| 707.0 | 0.7588906584582688 | 1.0 | 0.5 | 2.8450094980772525 |
| 708.0 | 0.28868991732045246 | 1.0 | 0.5 | 0.6812936446805068 |
| 709.0 | 0.37894801141661905 | 1.0 | 0.5 | 0.9526809660544031 |
| 710.0 | 0.8217402498986196 | 1.0 | 0.5 | 3.449027044315384 |
| 711.0 | 0.8046769170232717 | 1.0 | 0.5 | 3.266200511357026 |
| 712.0 | 0.5208060124828371 | 1.0 | 0.5 | 1.4712995583541006 |
| 713.0 | 0.3203397193592711 | 1.0 | 0.5 | 0.7723243858806824 |
| 714.0 | 0.5618997893410178 | 1.0 | 0.5 | 1.6506152066312039 |
| 715.0 | 3.1178092609852492e-2 | 1.0 | 0.5 | 6.334894817177689e-2 |
| 716.0 | 0.5642371505180444 | 1.0 | 0.5 | 1.661314213639032 |
| 717.0 | 1.9190481138238513e-2 | 1.0 | 0.5 | 3.875401729130485e-2 |
| 718.0 | 0.4135987918822288 | 1.0 | 0.5 | 1.0675021363673896 |
| 719.0 | 0.9127929690275294 | 1.0 | 0.5 | 4.878940641798839 |
| 720.0 | 0.7148798827297034 | 1.0 | 0.5 | 2.5096894467964637 |
| 721.0 | 0.9642792958561404 | 1.0 | 0.5 | 6.66404962058825 |
| 722.0 | 0.911960260043681 | 1.0 | 0.5 | 4.859933952054956 |
| 723.0 | 0.7092981673137959 | 1.0 | 0.5 | 2.470914334094145 |
| 724.0 | 0.24430347857004242 | 1.0 | 0.5 | 0.5602308201516258 |
| 725.0 | 0.9763859358701038 | 1.0 | 0.5 | 7.491825613486933 |
| 726.0 | 0.9989566209643947 | 1.0 | 0.5 | 13.730581519926131 |
| 727.0 | 0.25633033979661235 | 1.0 | 0.5 | 0.5923166957029428 |
| 728.0 | 0.8972180552427583 | 1.0 | 0.5 | 4.550291152087654 |
| 729.0 | 1.9528905049490786e-2 | 1.0 | 0.5 | 3.944422737593571e-2 |
| 730.0 | 0.39946670787520455 | 1.0 | 0.5 | 1.0198743966495578 |
| 731.0 | 0.6349487146278883 | 1.0 | 0.5 | 2.0154348548023044 |
| 732.0 | 0.45088040608537694 | 1.0 | 0.5 | 1.1988780432862982 |
| 733.0 | 1.1394653843765812e-2 | 1.0 | 0.5 | 2.2920140637373267e-2 |
| 734.0 | 0.9128621124432016 | 1.0 | 0.5 | 4.880527001065986 |
| 735.0 | 1.004360026539608e-2 | 1.0 | 0.5 | 2.0188754990875135e-2 |
| 736.0 | 1.9717183805037286e-2 | 1.0 | 0.5 | 3.982832199393943e-2 |
| 737.0 | 1.4926749904265346e-2 | 1.0 | 0.5 | 3.00785499912095e-2 |
| 738.0 | 7.927735525826707e-4 | 1.0 | 0.5 | 1.5861759274354245e-3 |
| 739.0 | 0.8027125643582385 | 1.0 | 0.5 | 3.2461870989452897 |
| 740.0 | 0.31113034342272394 | 1.0 | 0.5 | 0.7454064070931612 |
| 741.0 | 0.759739979845042 | 1.0 | 0.5 | 2.852067049601598 |
| 742.0 | 0.6177672446025673 | 1.0 | 0.5 | 1.9234510972095562 |
| 743.0 | 0.2069611048371751 | 1.0 | 0.5 | 0.4637660208509749 |
| 744.0 | 0.4096814931496281 | 1.0 | 0.5 | 1.0541860912508674 |
| 745.0 | 0.817678882950639 | 1.0 | 0.5 | 3.403971534404258 |
| 746.0 | 0.3125330238434748 | 1.0 | 0.5 | 0.7494829705530617 |
| 747.0 | 1.7715709585189865e-2 | 1.0 | 0.5 | 3.574902216876737e-2 |
| 748.0 | 0.9117252646361192 | 1.0 | 0.5 | 4.85460267006499 |
| 749.0 | 0.37936761917620854 | 1.0 | 0.5 | 0.954032703374775 |
| 750.0 | 0.13064378118132514 | 1.0 | 0.5 | 0.2800046392439565 |
| 751.0 | 0.6087333666590743 | 1.0 | 0.5 | 1.8767320494000013 |
| 752.0 | 0.7118039051707942 | 1.0 | 0.5 | 2.4882282914389293 |
| 753.0 | 0.2130012427126169 | 1.0 | 0.5 | 0.4790572192326393 |
| 754.0 | 0.30074068981788626 | 1.0 | 0.5 | 0.7154672649259595 |
| 755.0 | 0.6989168600391822 | 1.0 | 0.5 | 2.400737679756996 |
| 756.0 | 0.49145333200859054 | 1.0 | 0.5 | 1.3523965838206948 |
| 757.0 | 0.8939669245621267 | 1.0 | 0.5 | 4.488008402218595 |
| 758.0 | 5.06642125571628e-2 | 1.0 | 0.5 | 0.10398542006892682 |
| 759.0 | 0.7087728489367697 | 1.0 | 0.5 | 2.4673034569294723 |
| 760.0 | 0.37085175046848895 | 1.0 | 0.5 | 0.9267767184466104 |
| 761.0 | 0.4074932132586403 | 1.0 | 0.5 | 1.0467859032453104 |
| 762.0 | 0.8542082865253579 | 1.0 | 0.5 | 3.8511525918034795 |
| 763.0 | 7.484908714680683e-2 | 1.0 | 0.5 | 0.15559681149317692 |
| 764.0 | 9.517571658106372e-2 | 1.0 | 0.5 | 0.2000290322042246 |
| 765.0 | 0.12217771305705827 | 1.0 | 0.5 | 0.2606222250098486 |
| 766.0 | 0.925677393814992 | 1.0 | 0.5 | 5.198680236032941 |
| 767.0 | 0.5119666617129487 | 1.0 | 0.5 | 1.4347431186008843 |
| 768.0 | 0.17038782476746905 | 1.0 | 0.5 | 0.37359389229374074 |
| 769.0 | 0.7706783480932138 | 1.0 | 0.5 | 2.9452593349343505 |
| 770.0 | 0.19391235755126612 | 1.0 | 0.5 | 0.43112560971511776 |
| 771.0 | 6.888277974086554e-2 | 1.0 | 0.5 | 0.14274020345251326 |
| 772.0 | 0.17693474533783027 | 1.0 | 0.5 | 0.38943958534567436 |
| 773.0 | 0.6411819167454565 | 1.0 | 0.5 | 2.049879501614683 |
| 774.0 | 0.5848064536154285 | 1.0 | 0.5 | 1.7580209812820364 |
| 775.0 | 0.7817735283021328 | 1.0 | 0.5 | 3.044443788872624 |
| 776.0 | 0.8481433709350754 | 1.0 | 0.5 | 3.769636866186136 |
| 777.0 | 0.33536672542878343 | 1.0 | 0.5 | 0.8170397145004625 |
| 778.0 | 0.27882596730233244 | 1.0 | 0.5 | 0.6537495879787976 |
| 779.0 | 0.2970074562869147 | 1.0 | 0.5 | 0.7048179872204894 |
| 780.0 | 3.7958918861415225e-2 | 1.0 | 0.5 | 7.739625068390558e-2 |
| 781.0 | 0.6854586207216943 | 1.0 | 0.5 | 2.3132792797285946 |
| 782.0 | 8.267494508630024e-2 | 1.0 | 0.5 | 0.172586786155481 |
| 783.0 | 0.11564388362093858 | 1.0 | 0.5 | 0.24579090176548815 |
| 784.0 | 0.9961798812931946 | 1.0 | 0.5 | 11.134947563533036 |
| 785.0 | 0.5656730123779714 | 1.0 | 0.5 | 1.6679152017307481 |
| 786.0 | 7.896973665140383e-2 | 1.0 | 0.5 | 0.16452476807821648 |
| 787.0 | 0.9127352982992238 | 1.0 | 0.5 | 4.877618462451565 |
| 788.0 | 0.15021306153858782 | 1.0 | 0.5 | 0.32553924310407 |
| 789.0 | 0.6785051916416707 | 1.0 | 0.5 | 2.269547767240894 |
| 790.0 | 0.8950658048104646 | 1.0 | 0.5 | 4.5088436755431704 |
| 791.0 | 0.3196926130612555 | 1.0 | 0.5 | 0.7704210866706233 |
| 792.0 | 0.22785062673309964 | 1.0 | 0.5 | 0.517154517972376 |
| 793.0 | 0.4231990393744751 | 1.0 | 0.5 | 1.1005160551464954 |
| 794.0 | 7.554488811516613e-2 | 1.0 | 0.5 | 0.15710156654236235 |
| 795.0 | 0.2587492351400327 | 1.0 | 0.5 | 0.5988325936383525 |
| 796.0 | 0.21928049034296349 | 1.0 | 0.5 | 0.49507867242840237 |
| 797.0 | 0.14598560815451966 | 1.0 | 0.5 | 0.3156144661149704 |
| 798.0 | 0.2541007832902197 | 1.0 | 0.5 | 0.5863295722984566 |
| 799.0 | 4.151026996242069e-2 | 1.0 | 0.5 | 8.479286234590744e-2 |
| 800.0 | 0.22615143648494196 | 1.0 | 0.5 | 0.5127581578170677 |
| 801.0 | 0.46623021584902324 | 1.0 | 0.5 | 1.255581297602454 |
| 802.0 | 0.2324860880400741 | 1.0 | 0.5 | 0.5291973467058275 |
| 803.0 | 0.4842919851496078 | 1.0 | 0.5 | 1.3244290727086572 |
| 804.0 | 0.31228257130972625 | 1.0 | 0.5 | 0.7487544790558198 |
| 805.0 | 0.10572556114218468 | 1.0 | 0.5 | 0.22348514465749764 |
| 806.0 | 0.4123656576421121 | 1.0 | 0.5 | 1.0633007825222318 |
| 807.0 | 0.4217361362073324 | 1.0 | 0.5 | 1.0954500054550549 |
| 808.0 | 0.45778713734096 | 1.0 | 0.5 | 1.2241932382192404 |
| 809.0 | 0.6349336986117605 | 1.0 | 0.5 | 2.015352588513136 |
| 810.0 | 0.47455376816104156 | 1.0 | 0.5 | 1.287014823998035 |
| 811.0 | 0.6987591020670548 | 1.0 | 0.5 | 2.3996900179284135 |
| 812.0 | 0.5740300475253947 | 1.0 | 0.5 | 1.7067729385951673 |
| 813.0 | 0.8297214582171805 | 1.0 | 0.5 | 3.5406394034043 |
| 814.0 | 0.8755617566749287 | 1.0 | 0.5 | 4.167891447388655 |
| 815.0 | 0.15685402393809067 | 1.0 | 0.5 | 0.34123034676584946 |
| 816.0 | 0.12375980976397816 | 1.0 | 0.5 | 0.2642300717234836 |
| 817.0 | 0.6806752613847895 | 1.0 | 0.5 | 2.2830934092154727 |
| 818.0 | 0.5965972240604348 | 1.0 | 0.5 | 1.8156395442566535 |
| 819.0 | 0.2625946852290568 | 1.0 | 0.5 | 0.6092351715234201 |
| 820.0 | 0.8943631753763875 | 1.0 | 0.5 | 4.495496500942685 |
| 821.0 | 0.3465048409751804 | 1.0 | 0.5 | 0.8508403074229004 |
| 822.0 | 0.466552843202065 | 1.0 | 0.5 | 1.2567905263936805 |
| 823.0 | 0.6623144219307672 | 1.0 | 0.5 | 2.171280117683628 |
| 824.0 | 0.7366536795411063 | 1.0 | 0.5 | 2.668570610413903 |
| 825.0 | 0.3997851349619067 | 1.0 | 0.5 | 1.020935158949353 |
| 826.0 | 0.6353121717497426 | 1.0 | 0.5 | 2.0174271128305428 |
| 827.0 | 0.5403373613671404 | 1.0 | 0.5 | 1.5545249056810655 |
| 828.0 | 0.12004136168858848 | 1.0 | 0.5 | 0.25576074906672047 |
| 829.0 | 0.7799469882571323 | 1.0 | 0.5 | 3.027773598377975 |
| 830.0 | 0.11531195504077785 | 1.0 | 0.5 | 0.24504037537092968 |
| 831.0 | 0.6751338589310277 | 1.0 | 0.5 | 2.248684110259147 |
| 832.0 | 0.6049304752643492 | 1.0 | 0.5 | 1.857387035160637 |
| 833.0 | 0.48054940486561026 | 1.0 | 0.5 | 1.309967147530485 |
| 834.0 | 0.8445621852583549 | 1.0 | 0.5 | 3.7230190650893613 |
| 835.0 | 0.21563248255907475 | 1.0 | 0.5 | 0.48575519245007065 |
| 836.0 | 0.2207902668235011 | 1.0 | 0.5 | 0.49895007097777316 |
| 837.0 | 0.4704757763509425 | 1.0 | 0.5 | 1.2715527336604613 |
| 838.0 | 0.2845224459969764 | 1.0 | 0.5 | 0.6696101030853403 |
| 839.0 | 0.8856343128507066 | 1.0 | 0.5 | 4.336708365065732 |
| 840.0 | 0.7656286285064097 | 1.0 | 0.5 | 2.901696728009303 |
| 841.0 | 0.21265364413283017 | 1.0 | 0.5 | 0.4781740619565616 |
| 842.0 | 0.17416850369914083 | 1.0 | 0.5 | 0.38272905181535744 |
| 843.0 | 0.2538645788139029 | 1.0 | 0.5 | 0.5856963310256584 |
| 844.0 | 0.8444406724694766 | 1.0 | 0.5 | 3.721456185196974 |
| 845.0 | 0.40503484996861816 | 1.0 | 0.5 | 1.0385048930554124 |
| 846.0 | 0.7998738842755706 | 1.0 | 0.5 | 3.2176150650862265 |
| 847.0 | 0.8960279782756648 | 1.0 | 0.5 | 4.527266875830069 |
| 848.0 | 0.8070796797231495 | 1.0 | 0.5 | 3.290956047258102 |
| 849.0 | 0.728671064027279 | 1.0 | 0.5 | 2.6088468165355523 |
| 850.0 | 0.5165467620297932 | 1.0 | 0.5 | 1.4536013688859513 |
| 851.0 | 0.39508809603376394 | 1.0 | 0.5 | 1.005344889665774 |
| 852.0 | 0.7016498778483711 | 1.0 | 0.5 | 2.418975151180404 |
| 853.0 | 0.40918302696395925 | 1.0 | 0.5 | 1.0524979996686565 |
| 854.0 | 0.7768126306700964 | 1.0 | 0.5 | 2.99948727819986 |
| 855.0 | 0.8688162575893031 | 1.0 | 0.5 | 4.062312649405612 |
| 856.0 | 0.4568664964869603 | 1.0 | 0.5 | 1.2208002528884698 |
| 857.0 | 0.3885550233182733 | 1.0 | 0.5 | 0.9838606176000753 |
| 858.0 | 0.269986397413836 | 1.0 | 0.5 | 0.6293842226672568 |
| 859.0 | 4.5108123099849684e-2 | 1.0 | 0.5 | 9.231432563327888e-2 |
| 860.0 | 0.27486044943624455 | 1.0 | 0.5 | 0.6427823182276537 |
| 861.0 | 0.49980141898893304 | 1.0 | 0.5 | 1.3855001947715422 |
| 862.0 | 0.26964152335362046 | 1.0 | 0.5 | 0.6284396029459378 |
| 863.0 | 0.5541906086459795 | 1.0 | 0.5 | 1.6157275839158227 |
| 864.0 | 6.552647974406278e-2 | 1.0 | 0.5 | 0.13554397644533733 |
| 865.0 | 0.515540983127114 | 1.0 | 0.5 | 1.4494448794535606 |
| 866.0 | 0.1920988104560809 | 1.0 | 0.5 | 0.4266310362150219 |
| 867.0 | 0.950206757288115 | 1.0 | 0.5 | 5.999751985348829 |
| 868.0 | 0.23006202993243807 | 1.0 | 0.5 | 0.5228906514664433 |
| 869.0 | 0.5980822061857993 | 1.0 | 0.5 | 1.8230154085483221 |
| 870.0 | 0.25960050989226746 | 1.0 | 0.5 | 0.6011307739087632 |
| 871.0 | 0.8193733959148503 | 1.0 | 0.5 | 3.4226466788132806 |
| 872.0 | 0.7954883801990411 | 1.0 | 0.5 | 3.1742609691418404 |
| 873.0 | 0.8723724020447083 | 1.0 | 0.5 | 4.117277293083158 |
| 874.0 | 0.6192872690820723 | 1.0 | 0.5 | 1.9314203506774348 |
| 875.0 | 0.7580048507458014 | 1.0 | 0.5 | 2.8376751948609606 |
| 876.0 | 0.7848567480273286 | 1.0 | 0.5 | 3.0729023689619117 |
| 877.0 | 0.869215312266617 | 1.0 | 0.5 | 4.068405826140735 |
| 878.0 | 0.685275021203466 | 1.0 | 0.5 | 2.312112209402446 |
| 879.0 | 0.5350517178333166 | 1.0 | 0.5 | 1.5316582014544573 |
| 880.0 | 0.7931370500525133 | 1.0 | 0.5 | 3.151397565070539 |
| 881.0 | 0.966209505992918 | 1.0 | 0.5 | 6.775151516886493 |
| 882.0 | 0.6214626945446212 | 1.0 | 0.5 | 1.942881299305371 |
| 883.0 | 0.6376933645240845 | 1.0 | 0.5 | 2.0305287327658417 |
| 884.0 | 0.5741199138375093 | 1.0 | 0.5 | 1.7071949204387873 |
| 885.0 | 0.6958947628860042 | 1.0 | 0.5 | 2.380762925526933 |
| 886.0 | 4.160878124937972e-2 | 1.0 | 0.5 | 8.499842813674706e-2 |
| 887.0 | 9.221961421322367e-3 | 1.0 | 0.5 | 1.8529493910121025e-2 |
| 888.0 | 0.995560559447674 | 1.0 | 0.5 | 10.834453824410518 |
| 889.0 | 0.3605047941519841 | 1.0 | 0.5 | 0.8941523094212547 |
| 890.0 | 0.544321853362901 | 1.0 | 0.5 | 1.5719370749067736 |
| 891.0 | 0.2829784576164174 | 1.0 | 0.5 | 0.6652987873369529 |
| 892.0 | 0.12171357004250405 | 1.0 | 0.5 | 0.259565017057072 |
| 893.0 | 0.5365085199946079 | 1.0 | 0.5 | 1.53793455202113 |
| 894.0 | 0.8635772578438461 | 1.0 | 0.5 | 3.9839936322416003 |
| 895.0 | 0.5997846082263966 | 1.0 | 0.5 | 1.831504794736344 |
| 896.0 | 0.28535407011484903 | 1.0 | 0.5 | 0.6719361237988777 |
| 897.0 | 0.9907854500060016 | 1.0 | 0.5 | 9.373943046255649 |
| 898.0 | 0.38470242404619215 | 1.0 | 0.5 | 0.9712985297204246 |
| 899.0 | 0.996684691251383 | 1.0 | 0.5 | 11.418409045687161 |
| 900.0 | 0.6037185181547178 | 1.0 | 0.5 | 1.8512610149895417 |
| 901.0 | 0.631091973924771 | 1.0 | 0.5 | 1.9944158356773145 |
| 902.0 | 0.9033721835393809 | 1.0 | 0.5 | 4.673777248271796 |
| 903.0 | 0.9659118200688929 | 1.0 | 0.5 | 6.757609168737777 |
| 904.0 | 0.8347323363379702 | 1.0 | 0.5 | 3.6003778308418886 |
| 905.0 | 0.156889478641288 | 1.0 | 0.5 | 0.34131444951832346 |
| 906.0 | 0.60845309460352 | 1.0 | 0.5 | 1.8752999225720366 |
| 907.0 | 0.8633472985986762 | 1.0 | 0.5 | 3.98062519608297 |
| 908.0 | 0.4100799263036585 | 1.0 | 0.5 | 1.0555364391406359 |
| 909.0 | 0.8550582874604828 | 1.0 | 0.5 | 3.862847199728539 |
| 910.0 | 0.9904746098916691 | 1.0 | 0.5 | 9.307588805105695 |
| 911.0 | 1.386734564264469e-2 | 1.0 | 0.5 | 2.7928791082254642e-2 |
| 912.0 | 8.394149819044772e-2 | 1.0 | 0.5 | 0.1753500994861536 |
| 913.0 | 0.8351148309614497 | 1.0 | 0.5 | 3.605011985169511 |
| 914.0 | 0.6015316247550956 | 1.0 | 0.5 | 1.8402542867736174 |
| 915.0 | 0.14105954688113698 | 1.0 | 0.5 | 0.3041113611323453 |
| 916.0 | 0.6524787598107497 | 1.0 | 0.5 | 2.1138589859969033 |
| 917.0 | 0.6525615279637094 | 1.0 | 0.5 | 2.114335377084876 |
| 918.0 | 0.6946891466939034 | 1.0 | 0.5 | 2.3728496604597757 |
| 919.0 | 0.6033096485173985 | 1.0 | 0.5 | 1.8491985474144328 |
| 920.0 | 0.5527439828980938 | 1.0 | 0.5 | 1.6092482062601514 |
| 921.0 | 0.494578299821425 | 1.0 | 0.5 | 1.3647242966167434 |
| 922.0 | 0.8718659673492919 | 1.0 | 0.5 | 4.109356865674299 |
| 923.0 | 0.4118152262302006 | 1.0 | 0.5 | 1.0614282786836082 |
| 924.0 | 0.27277778113923457 | 1.0 | 0.5 | 0.6370463651933604 |
| 925.0 | 0.9588186661050422 | 1.0 | 0.5 | 6.379540372183412 |
| 926.0 | 0.6386447694611939 | 1.0 | 0.5 | 2.0357875727293435 |
| 927.0 | 9.334993320410911e-2 | 1.0 | 0.5 | 0.19599743441762477 |
| 928.0 | 0.7016861741132535 | 1.0 | 0.5 | 2.4192184792048073 |
| 929.0 | 0.18784637955961336 | 1.0 | 0.5 | 0.41613153795947083 |
| 930.0 | 0.6331343675906904 | 1.0 | 0.5 | 2.0055192443854386 |
| 931.0 | 0.6524932917663804 | 1.0 | 0.5 | 2.1139426197884124 |
| 932.0 | 0.4102851654153665 | 1.0 | 0.5 | 1.0562323802740772 |
| 933.0 | 0.7958768895050432 | 1.0 | 0.5 | 3.178063968684066 |
| 934.0 | 0.44152051106555945 | 1.0 | 0.5 | 1.1650747728426512 |
| 935.0 | 0.9841888496551107 | 1.0 | 0.5 | 8.294079739640237 |
| 936.0 | 0.898662857628188 | 1.0 | 0.5 | 4.578604555509935 |
| 937.0 | 0.9899684767226592 | 1.0 | 0.5 | 9.204045632843936 |
| 938.0 | 0.5694223880392236 | 1.0 | 0.5 | 1.6852553761262785 |
| 939.0 | 0.2978414233937231 | 1.0 | 0.5 | 0.7071920157293681 |
| 940.0 | 0.5285585390610126 | 1.0 | 0.5 | 1.5039206791418334 |
| 941.0 | 0.30098161980211935 | 1.0 | 0.5 | 0.716156484208438 |
| 942.0 | 0.4829602401634766 | 1.0 | 0.5 | 1.3192710050490075 |
| 943.0 | 0.7030369524005218 | 1.0 | 0.5 | 2.4282951335541623 |
| 944.0 | 0.6153251259709195 | 1.0 | 0.5 | 1.9107135690247623 |
| 945.0 | 0.11605983029146971 | 1.0 | 0.5 | 0.24673179992033967 |
| 946.0 | 0.919109564895397 | 1.0 | 0.5 | 5.029319386000415 |
| 947.0 | 0.27847812312185305 | 1.0 | 0.5 | 0.6527851596889246 |
| 948.0 | 0.8097051038884092 | 1.0 | 0.5 | 3.3183606505251126 |
| 949.0 | 0.8409591037197803 | 1.0 | 0.5 | 3.6771878010499828 |
| 950.0 | 0.30392721773059916 | 1.0 | 0.5 | 0.7246021037777217 |
| 951.0 | 0.8495038794482136 | 1.0 | 0.5 | 3.787635944379966 |
| 952.0 | 0.48885706969456755 | 1.0 | 0.5 | 1.342212041667035 |
| 953.0 | 0.20935625069016617 | 1.0 | 0.5 | 0.46981558561070996 |
| 954.0 | 0.7860961326946884 | 1.0 | 0.5 | 3.084457166176973 |
| 955.0 | 0.4148602300854256 | 1.0 | 0.5 | 1.0718090747138445 |
| 956.0 | 0.43256540344061756 | 1.0 | 0.5 | 1.1332595692433844 |
| 957.0 | 9.74617566034921e-2 | 1.0 | 0.5 | 0.20508842943662942 |
| 958.0 | 0.35607825002115845 | 1.0 | 0.5 | 0.8803561330085999 |
| 959.0 | 5.8682391469697226e-2 | 1.0 | 0.5 | 0.12094934799144909 |
| 960.0 | 0.22301709205961795 | 1.0 | 0.5 | 0.5046738527183281 |
| 961.0 | 0.31552644271623553 | 1.0 | 0.5 | 0.7582105314057301 |
| 962.0 | 0.38722066005942213 | 1.0 | 0.5 | 0.9795007506405535 |
| 963.0 | 0.6930849566647634 | 1.0 | 0.5 | 2.362368602998598 |
| 964.0 | 0.3276145977405852 | 1.0 | 0.5 | 0.7938471751780576 |
| 965.0 | 0.49738098013040544 | 1.0 | 0.5 | 1.375845623265706 |
| 966.0 | 0.5508204944886083 | 1.0 | 0.5 | 1.6006653631535386 |
| 967.0 | 0.38693668974398643 | 1.0 | 0.5 | 0.9785741380105565 |
| 968.0 | 0.4548891238031203 | 1.0 | 0.5 | 1.2135321248948858 |
| 969.0 | 0.5765426183052531 | 1.0 | 0.5 | 1.7186048069928659 |
| 970.0 | 0.6043932471419058 | 1.0 | 0.5 | 1.8546692189299532 |
| 971.0 | 0.5540865320847946 | 1.0 | 0.5 | 1.615260727826734 |
| 972.0 | 0.6922149458884131 | 1.0 | 0.5 | 2.356707231797275 |
| 973.0 | 0.958785269414793 | 1.0 | 0.5 | 6.377919096104818 |
| 974.0 | 0.24053086467843443 | 1.0 | 0.5 | 0.5502711918533648 |
| 975.0 | 0.7280165406338818 | 1.0 | 0.5 | 2.6040280513783802 |
| 976.0 | 0.25983069480411236 | 1.0 | 0.5 | 0.601752656294586 |
| 977.0 | 0.9631430150183512 | 1.0 | 0.5 | 6.601420253137288 |
| 978.0 | 0.9618464899092873 | 1.0 | 0.5 | 6.532275035727719 |
| 979.0 | 0.1737093794671214 | 1.0 | 0.5 | 0.38161745300759264 |
| 980.0 | 0.6297685595655134 | 1.0 | 0.5 | 1.9872539084176686 |
| 981.0 | 0.6821539282076621 | 1.0 | 0.5 | 2.292376128719879 |
| 982.0 | 0.20134207234771373 | 1.0 | 0.5 | 0.4496451009588417 |
| 983.0 | 0.706334629023196 | 1.0 | 0.5 | 2.45062870766914 |
| 984.0 | 0.35171371567805576 | 1.0 | 0.5 | 0.8668457668004146 |
| 985.0 | 0.4418132575532645 | 1.0 | 0.5 | 1.1661234174105077 |
| 986.0 | 0.32739555551180655 | 1.0 | 0.5 | 0.7931957435520275 |
| 987.0 | 0.634158494553163 | 1.0 | 0.5 | 2.011110169198309 |
| 988.0 | 4.308559782735366e-3 | 1.0 | 0.5 | 8.635736747611403e-3 |
| 989.0 | 0.4107240255611958 | 1.0 | 0.5 | 1.0577213152606424 |
| 990.0 | 0.6688896259565301 | 1.0 | 0.5 | 2.2106070057291913 |
| 991.0 | 0.35953267557398183 | 1.0 | 0.5 | 0.891114349350007 |
| 992.0 | 0.7399069275541614 | 1.0 | 0.5 | 2.693431482154778 |
| 993.0 | 0.12692177456699705 | 1.0 | 0.5 | 0.27146024369971955 |
| 994.0 | 1.1762786512343526e-2 | 1.0 | 0.5 | 2.3665030858473123e-2 |
| 995.0 | 0.9174681392193306 | 1.0 | 0.5 | 4.989141737826638 |
| 996.0 | 0.17908478481936818 | 1.0 | 0.5 | 0.39467089008875134 |
| 997.0 | 0.6401255388358325 | 1.0 | 0.5 | 2.0440000546740205 |
| 998.0 | 0.23394182884995784 | 1.0 | 0.5 | 0.5329943413516208 |
| 999.0 | 0.5255738075065223 | 1.0 | 0.5 | 1.4912984419798878 |
dfExpRand.describe().show() // look sensible
+-------+-----------------+--------------------+----+----+--------------------+
|summary| Id| rand| one|rate| expo_sample|
+-------+-----------------+--------------------+----+----+--------------------+
| count| 1000| 1000|1000|1000| 1000|
| mean| 499.5| 0.5021780527729537| 1.0| 0.5| 1.9988036562138296|
| stddev|288.8194360957494| 0.28411433707906236| 0.0| 0.0| 2.0004677979809777|
| min| 0|7.927735525826707E-4| 1.0| 0.5|0.001586175927435...|
| max| 999| 0.9989566209643947| 1.0| 0.5| 13.730581519926131|
+-------+-----------------+--------------------+----+----+--------------------+
val expoSamplesDF = spark.range(1000000000).toDF("Id") // just make a DF of 100 row indices
.select($"Id", rand(seed=1234567) as "rand") // add a column of random numbers in (0,1)
.withColumn("one",lit(1.0))
.withColumn("rate",lit(0.5))
.withColumn("expo_sample", -($"one" / $"rate") * log($"one" - $"rand"))
expoSamplesDF: org.apache.spark.sql.DataFrame = [Id: bigint, rand: double ... 3 more fields]
expoSamplesDF.describe().show()
+-------+-------------------+--------------------+----------+----------+--------------------+
|summary| Id| rand| one| rate| expo_sample|
+-------+-------------------+--------------------+----------+----------+--------------------+
| count| 1000000000| 1000000000|1000000000|1000000000| 1000000000|
| mean| 4.99999999147278E8| 0.49999187098618697| 1.0| 0.5| 1.9999737649593539|
| stddev|2.886751347391513E8| 0.2886780497633785| 0.0| 0.0| 2.000022741029218|
| min| 0|1.200616051022507...| 1.0| 0.5|2.401232103486493...|
| max| 999999999| 0.9999999996809935| 1.0| 0.5| 43.731619350261035|
+-------+-------------------+--------------------+----------+----------+--------------------+
Approximating Pi with Monte Carlo simulaitons
Uisng RDDs directly, let's estimate Pi.
//Calculate pi with Monte Carlo estimation
import scala.math.random
//make a very large unique set of 1 -> n
val partitions = 2
val n = math.min(100000L * partitions, Int.MaxValue).toInt
val xs = 1 until n
//split up n into the number of partitions we can use
val rdd = sc.parallelize(xs, partitions).setName("'N values rdd'")
//generate a random set of points within a 2x2 square
val sample = rdd.map { i =>
val x = random * 2 - 1
val y = random * 2 - 1
(x, y)
}.setName("'Random points rdd'")
//points w/in the square also w/in the center circle of r=1
val inside = sample.filter { case (x, y) => (x * x + y * y < 1) }.setName("'Random points inside circle'")
val count = inside.count()
//Area(circle)/Area(square) = inside/n => pi=4*inside/n
println("Pi is roughly " + 4.0 * count / n)
Pi is roughly 3.1423
import scala.math.random
partitions: Int = 2
n: Int = 200000
xs: scala.collection.immutable.Range = Range 1 until 200000
rdd: org.apache.spark.rdd.RDD[Int] = 'N values rdd' ParallelCollectionRDD[38] at parallelize at command-2744653148555222:10
sample: org.apache.spark.rdd.RDD[(Double, Double)] = 'Random points rdd' MapPartitionsRDD[39] at map at command-2744653148555222:13
inside: org.apache.spark.rdd.RDD[(Double, Double)] = 'Random points inside circle' MapPartitionsRDD[40] at filter at command-2744653148555222:20
count: Long = 157115
Doing it in PySpark is just as easy. This may be needed if there are pyhton libraries you want to take advantage of in Spark.
# # Estimating $\pi$
#
# This PySpark example shows you how to estimate $\pi$ in parallel
# using Monte Carlo integration.
from __future__ import print_function
import sys
from random import random
from operator import add
partitions = 2
n = 100000 * partitions
def f(_):
x = random() * 2 - 1
y = random() * 2 - 1
return 1 if x ** 2 + y ** 2 < 1 else 0
# To access the associated SparkContext
count = spark.sparkContext.parallelize(range(1, n + 1), partitions).map(f).reduce(add)
print("Pi is roughly %f" % (4.0 * count / n))
Pi is roughly 3.138820
The following is from this google turotial.
Programming a Monte Carlo simulation in Scala
Monte Carlo, of course, is famous as a gambling destination. In this section, you use Scala to create a simulation that models the mathematical advantage that a casino enjoys in a game of chance. The "house edge" at a real casino varies widely from game to game; it can be over 20% in keno, for example. This tutorial creates a simple game where the house has only a one-percent advantage. Here's how the game works:
- The player places a bet, consisting of a number of chips from a bankroll fund.
- The player rolls a 100-sided die (how cool would that be?).
- If the result of the roll is a number from 1 to 49, the player wins.
- For results 50 to 100, the player loses the bet.
You can see that this game creates a one-percent disadvantage for the player: in 51 of the 100 possible outcomes for each roll, the player loses.
Follow these steps to create and run the game:
val STARTING_FUND = 10
val STAKE = 1 // the amount of the bet
val NUMBER_OF_GAMES = 25
def rollDie: Int = {
val r = scala.util.Random
r.nextInt(99) + 1
}
def playGame(stake: Int): (Int) = {
val faceValue = rollDie
if (faceValue < 50)
(2*stake)
else
(0)
}
// Function to play the game multiple times
// Returns the final fund amount
def playSession(
startingFund: Int = STARTING_FUND,
stake: Int = STAKE,
numberOfGames: Int = NUMBER_OF_GAMES):
(Int) = {
// Initialize values
var (currentFund, currentStake, currentGame) = (startingFund, 0, 1)
// Keep playing until number of games is reached or funds run out
while (currentGame <= numberOfGames && currentFund > 0) {
// Set the current bet and deduct it from the fund
currentStake = math.min(stake, currentFund)
currentFund -= currentStake
// Play the game
val (winnings) = playGame(currentStake)
// Add any winnings
currentFund += winnings
// Increment the loop counter
currentGame += 1
}
(currentFund)
}
STARTING_FUND: Int = 10
STAKE: Int = 1
NUMBER_OF_GAMES: Int = 25
rollDie: Int
playGame: (stake: Int)Int
playSession: (startingFund: Int, stake: Int, numberOfGames: Int)Int
Enter the following code to play the game 25 times, which is the default value for NUMBER_OF_GAMES.
playSession()
res31: Int = 5
Your bankroll started with a value of 10 units. Is it higher or lower, now?
Now simulate 10,000 players betting 100 chips per game. Play 10,000 games in a session. This Monte Carlo simulation calculates the probability of losing all your money before the end of the session. Enter the follow code:
(sc.parallelize(1 to 10000, 500)
.map(i => playSession(100000, 100, 250000))
.map(i => if (i == 0) 1 else 0)
.reduce(_+_)/10000.0)
res32: Double = 0.9992
Note that the syntax .reduce(_+_) is shorthand in Scala for aggregating by using a summing function.
The preceding code performs the following steps:
- Creates an RDD with the results of playing the session.
- Replaces bankrupt players' results with the number 1 and nonzero results with the number 0.
- Sums the count of bankrupt players.
- Divides the count by the number of players.
A typical result might be:
res32: Double = 0.9992
Which represents a near guarantee of losing all your money, even though the casino had only a one-percent advantage.
Project Ideas
Try to create a scalable simulation of interest to you.
Here are some mature projects:
- https://github.com/zishanfu/GeoSparkSim
- https://github.com/srbaird/mc-var-spark
See a more complete VaR modeling: - https://databricks.com/blog/2020/05/27/modernizing-risk-management-part-1-streaming-data-ingestion-rapid-model-development-and-monte-carlo-simulations-at-scale.html
Introduction to Machine Learning
Some very useful resources we will weave around for Statistical Learning, Data Mining, Machine Learning:
- January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR).
- http://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/
- free PDF of the ISLR book: http://www-bcf.usc.edu/~gareth/ISL/
- A more theoretically sound book with interesting aplications is Elements of Statistical Learning by the Stanford Gang of 3 (Hastie, Tibshirani and Friedman):
- free PDF of the 10th printing: http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf
- Solutions: http://waxworksmath.com/Authors/GM/Hastie/WriteUp/weatherwaxepsteinhastiesolutions_manual.pdf
- 2015, UCLA Ass. Professor Ameet Talwalkar (from Spark team) in BerkeleyX: CS190.1x Scalable Machine Learning in edX Archive from 2015
- My preferred book for statistical learning is by Kevin P. Murphy, Machine Learning: A Probabilistic Perspective.
Deep Learning is a very popular method currently (2017) in Machine Learning and I recommend Andrew Ng's free course in Courseera for this.
Note: This is an applied course in data science and we will quickly move to doing things with data. You have to do work to get a deeper mathematical understanding or take other courses. We will focus on intution here and the distributed ML Pipeline in action.
I am assuming several of you have or are taking ML courses from experts in IT Department at Uppsala (if not, you might want to consider this seriously). In this course we will focus on getting our hads dirty quickly with some level of common understanding across all the disciplines represented here. Please dive deep at your own time to desired depths and tangents based on your background.
I may consider teaching a short theoretical course in statistical learning if there is enough interest for those with background in:
- Real Analysis,
- Geometry,
- Combinatorics and
- Probability, in the future.
Such a course could be an expanded version of the following notes built on the classic works of Luc Devroye and the L1-School of Statistical Learning:
- Short course on Non-parametric Density Estimation at Centre for Mathematics and its Applications, Ecole Polytechnique, Palaiseau, France
Machine Learning Introduction
ML Intro high-level by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning
(watch now 4:14):
Ameet's course is in databricks guide for your convenience:
Ameet's Summary of Machine Learning at a High Level
- rough definition of machine learning.
- constructing and studying methods that learn from and make predictions on data.
- This broad area involves tools and ideas from various domains, including:
- computer science,
- probability and statistics,
- optimization, and
- linear algebra.
- Common examples of ML, include:
- face recognition,
- link prediction,
- text or document classification, eg.::
- spam detection,
- protein structure prediction
- teaching computers to play games (go!)
Some common terminology
using example of spam detection as a running example.
-
the data points we learn from are call observations:
- they are items or entities used for::
- learning or
- evaluation.
- they are items or entities used for::
-
in the context of spam detection,
- emails are our observations.
- Features are attributes used to represent an observation.
- Features are typically numeric,
- and in spam detection, they can be:
- the length,
- the date, or
- the presence or absence of keywords in emails.
- Labels are values or categories assigned to observations.
- and in spam detection, they can be:
- an email being defined as spam or not spam.
-
Training and test data sets are the observations that we use to train and evaluate a learning algorithm. ...
-
Pop-Quiz
- What is the difference between supervised and unsupervised learning?
For a Stats@Stanford Hastie-Tibshirani Perspective on Supervised and Unsupervised Learning:
(watch later 12:12):
Typical Supervised Learning Pipeline by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning
(watch now 2:07):
This course is in databricks guide for your convenience:
Take your own notes if you want ....
Sample Classification Pipeline (Spam Filter) by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning
This course is in databricks guide for your convenience:
(watch later 7:48):
Million Song Dataset - Kaggle Challenge
Predict which songs a user will listen to.
SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.
When you first hear a song, do you ever categorize it as slow or fast in your head? Is it even a valid categorization? If so, can one do it automatically? I have always wondered about that. That is why I got excited when I learned about the Million Songs Dataset -Kaggle Challenge.
In this tutorial we will walk through a practical example of a data science project with Apache Spark in Python. We are going to parse, explore and model a sample from the million songs dataset stored on distributed storage. This tutorial is organized into three sections:
- ETL: Parses raw texts and creates a cached table
- Explore: Explores different aspects of the songs table using graphs
- Model: Uses SparkML to cluster songs based on some of their attributes

The goal of this tutorial is to prepare you for real world data science projects. Make sure you go through the tutorial in the above order and use the exercises to make yourself familiar further with the API. Also make sure you run these notebooks on a 1.6.x cluster.
1. ETL
The first step of most data science projects is extracting, transforming and loading data into well formated tables. Our example starts with ETL as well. By following the ETL noteboook you can expect to learn about following Spark concepts:
- RDD: Resilient Distributed Dataset
- Reading and transforming RDDs
- Schema in Spark
- Spark DataFrame
- Temp tables
- Caching tables
2. Explore
Exploratory analysis is a key step in any real data project. Data scientists use variety of tools to explore and visualize their data. In the second notebook of this tutorial we introduce several tools in Python and Databricks notebooks that can help you visually explore your large data. By reading this notebook and finishing its exercises you will become familiar with:
- How to view the schema of a table
- How to display ggplot and matplotlib figures in Notebooks
- How to summarize and visualize different aspects of large datasets
- How to sample and visualize large data
3. Model
The end goal of many data scientists is producing useful models. These models are often used for prediction of new and upcoming events in production. In our third notebook we construct a simple K-means clustering model. In this notebook you will learn about:
- Feature transformation
- Fitting a model using SparkML API
- Applying a model to data
- Visualizing model results
- Model tuning
Million Song Dataset - Kaggle Challenge
Predict which songs a user will listen to.
SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.
CAUTION: This notebook is expected to have an error in command 28 (Cmd 28 in databricks notebook). You are meant to learn how to fix this error with simple exception-handling to become a better data scientist. So ignore this warning, if any.
Stage 1: Parsing songs data

This is the first notebook in this tutorial. In this notebook we will read data from DBFS (DataBricks FileSystem). We will parse data and load it as a table that can be readily used in following notebooks.
By going through this notebook you can expect to learn how to read distributed data as an RDD, how to transform RDDs, and how to construct a Spark DataFrame from an RDD and register it as a table.
We first explore different files in our distributed file system. We use a header file to construct a Spark Schema object. We write a function that takes the header and casts strings in each line of our data to corresponding types. Once we run this function on the data we find that it fails on some corner caes. We update our function and finally get a parsed RDD. We combine that RDD and the Schema to construct a DataFame and register it as a temporary table in SparkSQL.
Text data files are stored in dbfs:/databricks-datasets/songs/data-001
You can conveniently list files on distributed file system (DBFS, S3 or HDFS) using %fs commands.
ls /databricks-datasets/songs/data-001/
| path | name | size |
|---|---|---|
| dbfs:/databricks-datasets/songs/data-001/header.txt | header.txt | 377.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00000 | part-00000 | 52837.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00001 | part-00001 | 52469.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00002 | part-00002 | 51778.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00003 | part-00003 | 50551.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00004 | part-00004 | 53449.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00005 | part-00005 | 53301.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00006 | part-00006 | 54184.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00007 | part-00007 | 50924.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00008 | part-00008 | 52533.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00009 | part-00009 | 54570.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00010 | part-00010 | 54338.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00011 | part-00011 | 51836.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00012 | part-00012 | 52297.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00013 | part-00013 | 52044.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00014 | part-00014 | 50704.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00015 | part-00015 | 54158.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00016 | part-00016 | 50080.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00017 | part-00017 | 47708.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00018 | part-00018 | 8858.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00019 | part-00019 | 53323.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00020 | part-00020 | 57877.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00021 | part-00021 | 52491.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00022 | part-00022 | 54791.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00023 | part-00023 | 50682.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00024 | part-00024 | 52863.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00025 | part-00025 | 47416.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00026 | part-00026 | 50130.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00027 | part-00027 | 53462.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00028 | part-00028 | 54179.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00029 | part-00029 | 52738.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00030 | part-00030 | 54159.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00031 | part-00031 | 51247.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00032 | part-00032 | 51610.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00033 | part-00033 | 53895.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00034 | part-00034 | 53125.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00035 | part-00035 | 54066.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00036 | part-00036 | 54265.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00037 | part-00037 | 54264.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00038 | part-00038 | 50540.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00039 | part-00039 | 55193.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00040 | part-00040 | 54537.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00041 | part-00041 | 52402.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00042 | part-00042 | 54673.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00043 | part-00043 | 53009.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00044 | part-00044 | 51789.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00045 | part-00045 | 52986.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00046 | part-00046 | 54442.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00047 | part-00047 | 52971.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00048 | part-00048 | 53331.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00049 | part-00049 | 44263.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00050 | part-00050 | 54841.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00051 | part-00051 | 54306.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00052 | part-00052 | 53610.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00053 | part-00053 | 53573.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00054 | part-00054 | 53854.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00055 | part-00055 | 54236.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00056 | part-00056 | 54455.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00057 | part-00057 | 52307.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00058 | part-00058 | 52313.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00059 | part-00059 | 52446.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00060 | part-00060 | 51958.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00061 | part-00061 | 53859.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00062 | part-00062 | 53698.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00063 | part-00063 | 54482.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00064 | part-00064 | 40182.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00065 | part-00065 | 54410.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00066 | part-00066 | 49123.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00067 | part-00067 | 50796.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00068 | part-00068 | 49561.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00069 | part-00069 | 52294.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00070 | part-00070 | 51250.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00071 | part-00071 | 58942.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00072 | part-00072 | 54589.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00073 | part-00073 | 54233.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00074 | part-00074 | 54725.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00075 | part-00075 | 54877.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00076 | part-00076 | 54333.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00077 | part-00077 | 51927.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00078 | part-00078 | 51744.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00079 | part-00079 | 53187.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00080 | part-00080 | 43246.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00081 | part-00081 | 54269.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00082 | part-00082 | 48464.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00083 | part-00083 | 52144.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00084 | part-00084 | 53375.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00085 | part-00085 | 55139.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00086 | part-00086 | 50924.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00087 | part-00087 | 52013.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00088 | part-00088 | 54262.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00089 | part-00089 | 53007.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00090 | part-00090 | 55142.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00091 | part-00091 | 52049.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00092 | part-00092 | 54714.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00093 | part-00093 | 52906.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00094 | part-00094 | 52188.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00095 | part-00095 | 50768.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00096 | part-00096 | 55242.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00097 | part-00097 | 52059.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00098 | part-00098 | 52982.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00099 | part-00099 | 52015.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00100 | part-00100 | 51467.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00101 | part-00101 | 50926.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00102 | part-00102 | 55018.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00103 | part-00103 | 50043.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00104 | part-00104 | 51936.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00105 | part-00105 | 57311.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00106 | part-00106 | 55090.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00107 | part-00107 | 54396.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00108 | part-00108 | 56594.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00109 | part-00109 | 53260.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00110 | part-00110 | 42007.0 |
| dbfs:/databricks-datasets/songs/data-001/part-00119 | part-00119 | 0.0 |
As you can see in the listing we have data files and a single header file. The header file seems interesting and worth a first inspection at first. The file is 377 bytes, therefore it is safe to collect the entire content of the file in the notebook.
sc.textFile("databricks-datasets/songs/data-001/header.txt").collect()
res1: Array[String] = Array(artist_id:string, artist_latitude:double, artist_longitude:double, artist_location:string, artist_name:string, duration:double, end_of_fade_in:double, key:int, key_confidence:double, loudness:double, release:string, song_hotnes:double, song_id:string, start_of_fade_out:double, tempo:double, time_signature:double, time_signature_confidence:double, title:string, year:double, partial_sequence:int)
Remember you can collect() a huge RDD and crash the driver program - so it is a good practise to take a couple lines and count the number of lines, especially if you have no idea what file you are trying to read.
sc.textFile("databricks-datasets/songs/data-001/header.txt").take(2)
res2: Array[String] = Array(artist_id:string, artist_latitude:double)
sc.textFile("databricks-datasets/songs/data-001/header.txt").count()
res3: Long = 20
sc.textFile("databricks-datasets/songs/data-001/header.txt").collect.map(println) // uncomment to see line-by-line
artist_id:string
artist_latitude:double
artist_longitude:double
artist_location:string
artist_name:string
duration:double
end_of_fade_in:double
key:int
key_confidence:double
loudness:double
release:string
song_hotnes:double
song_id:string
start_of_fade_out:double
tempo:double
time_signature:double
time_signature_confidence:double
title:string
year:double
partial_sequence:int
res4: Array[Unit] = Array((), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), ())
As seen above each line in the header consists of a name and a type separated by colon. We will need to parse the header file as follows:
val header = sc.textFile("/databricks-datasets/songs/data-001/header.txt").map(line => {
val headerElement = line.split(":")
(headerElement(0), headerElement(1))
}
).collect()
header: Array[(String, String)] = Array((artist_id,string), (artist_latitude,double), (artist_longitude,double), (artist_location,string), (artist_name,string), (duration,double), (end_of_fade_in,double), (key,int), (key_confidence,double), (loudness,double), (release,string), (song_hotnes,double), (song_id,string), (start_of_fade_out,double), (tempo,double), (time_signature,double), (time_signature_confidence,double), (title,string), (year,double), (partial_sequence,int))
Let's define a case class called Song that will be used to represent each row of data in the files:
/databricks-datasets/songs/data-001/part-00000through/databricks-datasets/songs/data-001/part-00119or the last.../part-*****file.
case class Song(artist_id: String, artist_latitude: Double, artist_longitude: Double, artist_location: String, artist_name: String, duration: Double, end_of_fade_in: Double, key: Int, key_confidence: Double, loudness: Double, release: String, song_hotness: Double, song_id: String, start_of_fade_out: Double, tempo: Double, time_signature: Double, time_signature_confidence: Double, title: String, year: Double, partial_sequence: Int)
defined class Song
Now we turn to data files. First, step is inspecting the first line of data to inspect its format.
// this is loads all the data - a subset of the 1M songs dataset
val dataRDD = sc.textFile("/databricks-datasets/songs/data-001/part-*")
dataRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/songs/data-001/part-* MapPartitionsRDD[3258] at textFile at command-2972105651607016:2
dataRDD.count // number of songs
res5: Long = 31369
dataRDD.take(3)
res6: Array[String] = Array(AR81V6H1187FB48872 nan nan Earl Sixteen 213.7073 0.0 11 0.419 -12.106 Soldier of Jah Army nan SOVNZSZ12AB018A9B8 208.289 125.882 1 0.0 Rastaman 2003 --, ARVVZQP11E2835DBCB nan nan Wavves 133.25016 0.0 0 0.282 0.596 Wavvves 0.471578247701 SOJTQHQ12A8C143C5F 128.116 89.519 1 0.0 I Want To See You (And Go To The Movies) 2009 --, ARFG9M11187FB3BBCB nan nan Nashua USA C-Side 247.32689 0.0 9 0.612 -4.896 Santa Festival Compilation 2008 vol.1 nan SOAJSQL12AB0180501 242.196 171.278 5 1.0 Loose on the Dancefloor 0 225261)
Each line of data consists of multiple fields separated by \t. With that information and what we learned from the header file, we set out to parse our data.
- We have already created a case class based on the header (which seems to agree with the 3 lines above).
- Next, we will create a function that takes each line as input and returns the case class as output.
// let's do this 'by hand' to re-flex our RDD-muscles :)
// although this is not a robust way to read from a data engineering perspective (without fielding exceptions)
def parseLine(line: String): Song = {
val tokens = line.split("\t")
Song(tokens(0), tokens(1).toDouble, tokens(2).toDouble, tokens(3), tokens(4), tokens(5).toDouble, tokens(6).toDouble, tokens(7).toInt, tokens(8).toDouble, tokens(9).toDouble, tokens(10), tokens(11).toDouble, tokens(12), tokens(13).toDouble, tokens(14).toDouble, tokens(15).toDouble, tokens(16).toDouble, tokens(17), tokens(18).toDouble, tokens(19).toInt)
}
parseLine: (line: String)Song
With this function we can transform the dataRDD to another RDD that consists of Song case classes
val parsedRDD = dataRDD.map(parseLine)
parsedRDD: org.apache.spark.rdd.RDD[Song] = MapPartitionsRDD[3259] at map at command-2972105651607022:1
To convert an RDD of case classes to a DataFrame, we just need to call the toDF method
val df = parsedRDD.toDF
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]
Once we get a DataFrame we can register it as a temporary table. That will allow us to use its name in SQL queries.
df.createOrReplaceTempView("songsTable")
We can now cache our table. So far all operations have been lazy. This is the first time Spark will attempt to actually read all our data and apply the transformations.
If you are running Spark 1.6+ the next command will throw a parsing error.
cache table songsTable
The error means that we are trying to convert a missing value to a Double. Here is an updated version of the parseLine function to deal with missing values
// good data engineering science practise
def parseLine(line: String): Song = {
def toDouble(value: String, defaultVal: Double): Double = {
try {
value.toDouble
} catch {
case e: Exception => defaultVal
}
}
def toInt(value: String, defaultVal: Int): Int = {
try {
value.toInt
} catch {
case e: Exception => defaultVal
}
}
val tokens = line.split("\t")
Song(tokens(0), toDouble(tokens(1), 0.0), toDouble(tokens(2), 0.0), tokens(3), tokens(4), toDouble(tokens(5), 0.0), toDouble(tokens(6), 0.0), toInt(tokens(7), -1), toDouble(tokens(8), 0.0), toDouble(tokens(9), 0.0), tokens(10), toDouble(tokens(11), 0.0), tokens(12), toDouble(tokens(13), 0.0), toDouble(tokens(14), 0.0), toDouble(tokens(15), 0.0), toDouble(tokens(16), 0.0), tokens(17), toDouble(tokens(18), 0.0), toInt(tokens(19), -1))
}
parseLine: (line: String)Song
val df = dataRDD.map(parseLine).toDF
df.createOrReplaceTempView("songsTable")
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]
And let's try caching the table. We are going to access this data multiple times in following notebooks, therefore it is a good idea to cache it in memory for faster subsequent access.
cache table songsTable
From now on we can easily query our data using the temporary table we just created and cached in memory. Since it is registered as a table we can conveniently use SQL as well as Spark API to access it.
select * from songsTable limit 10
| artist_id | artist_latitude | artist_longitude | artist_location | artist_name | duration | end_of_fade_in | key | key_confidence | loudness | release | song_hotness | song_id | start_of_fade_out | tempo | time_signature | time_signature_confidence | title | year | partial_sequence |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AR81V6H1187FB48872 | 0.0 | 0.0 | Earl Sixteen | 213.7073 | 0.0 | 11.0 | 0.419 | -12.106 | Soldier of Jah Army | 0.0 | SOVNZSZ12AB018A9B8 | 208.289 | 125.882 | 1.0 | 0.0 | Rastaman | 2003.0 | -1.0 | |
| ARVVZQP11E2835DBCB | 0.0 | 0.0 | Wavves | 133.25016 | 0.0 | 0.0 | 0.282 | 0.596 | Wavvves | 0.471578247701 | SOJTQHQ12A8C143C5F | 128.116 | 89.519 | 1.0 | 0.0 | I Want To See You (And Go To The Movies) | 2009.0 | -1.0 | |
| ARFG9M11187FB3BBCB | 0.0 | 0.0 | Nashua USA | C-Side | 247.32689 | 0.0 | 9.0 | 0.612 | -4.896 | Santa Festival Compilation 2008 vol.1 | 0.0 | SOAJSQL12AB0180501 | 242.196 | 171.278 | 5.0 | 1.0 | Loose on the Dancefloor | 0.0 | 225261.0 |
| ARK4Z2O1187FB45FF0 | 0.0 | 0.0 | Harvest | 337.05751 | 0.247 | 4.0 | 0.46 | -9.092 | Underground Community | 0.0 | SOTDRVW12AB018BEB9 | 327.436 | 84.986 | 4.0 | 0.673 | No Return | 0.0 | 101619.0 | |
| AR4VQSG1187FB57E18 | 35.25082 | -91.74015 | Searcy, AR | Gossip | 430.23628 | 0.0 | 2.0 | 3.4e-2 | -6.846 | Yr Mangled Heart | 0.0 | SOTVOCL12A8AE478DD | 424.06 | 121.998 | 4.0 | 0.847 | Yr Mangled Heart | 2006.0 | 740623.0 |
| ARNBV1X1187B996249 | 0.0 | 0.0 | Alex | 186.80118 | 0.0 | 4.0 | 0.641 | -16.108 | Jolgaledin | 0.0 | SODTGRY12AB0182438 | 166.156 | 140.735 | 4.0 | 5.5e-2 | Mariu Sonur Jesus | 0.0 | 673970.0 | |
| ARXOEZX1187B9B82A1 | 0.0 | 0.0 | Elie Attieh | 361.89995 | 0.0 | 7.0 | 0.863 | -4.919 | ELITE | 0.0 | SOIINTJ12AB0180BA6 | 354.476 | 128.024 | 4.0 | 0.399 | Fe Yom We Leila | 0.0 | 280304.0 | |
| ARXPUIA1187B9A32F1 | 0.0 | 0.0 | Rome, Italy | Simone Cristicchi | 220.00281 | 2.119 | 4.0 | 0.486 | -6.52 | Dall'Altra Parte Del Cancello | 0.484225272411 | SONHXJK12AAF3B5290 | 214.761 | 99.954 | 1.0 | 0.928 | L'Italiano | 2007.0 | 745962.0 |
| ARNPPTH1187B9AD429 | 51.4855 | -0.37196 | Heston, Middlesex, England | Jimmy Page | 156.86485 | 0.334 | 7.0 | 0.493 | -9.962 | No Introduction Necessary [Deluxe Edition] | 0.0 | SOGUHGW12A58A80E06 | 149.269 | 162.48 | 4.0 | 0.534 | Wailing Sounds | 2004.0 | 599250.0 |
| AROGWRA122988FEE45 | 0.0 | 0.0 | Christos Dantis | 256.67873 | 2.537 | 9.0 | 0.742 | -13.404 | Daktilika Apotipomata | 0.0 | SOJJOYI12A8C13399D | 248.912 | 134.944 | 4.0 | 0.162 | Stin Proigoumeni Zoi | 0.0 | 611396.0 |
Next up is exploring this data. Click on the Exploration notebook to continue the tutorial.
Million Song Dataset - Kaggle Challenge
Predict which songs a user will listen to.
SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.
Stage 2: Exploring songs data

This is the second notebook in this tutorial. In this notebook we do what any data scientist does with their data right after parsing it: exploring and understanding different aspect of data. Make sure you understand how we get the songsTable by reading and running the ETL notebook. In the ETL notebook we created and cached a temporary table named songsTable
// Let's quickly do everything to register the tempView of the table here
// fill in comment ... EXERCISE!
case class Song(artist_id: String, artist_latitude: Double, artist_longitude: Double, artist_location: String, artist_name: String, duration: Double, end_of_fade_in: Double, key: Int, key_confidence: Double, loudness: Double, release: String, song_hotness: Double, song_id: String, start_of_fade_out: Double, tempo: Double, time_signature: Double, time_signature_confidence: Double, title: String, year: Double, partial_sequence: Int)
def parseLine(line: String): Song = {
// fill in comment ...
def toDouble(value: String, defaultVal: Double): Double = {
try {
value.toDouble
} catch {
case e: Exception => defaultVal
}
}
def toInt(value: String, defaultVal: Int): Int = {
try {
value.toInt
} catch {
case e: Exception => defaultVal
}
}
// fill in comment ...
val tokens = line.split("\t")
Song(tokens(0), toDouble(tokens(1), 0.0), toDouble(tokens(2), 0.0), tokens(3), tokens(4), toDouble(tokens(5), 0.0), toDouble(tokens(6), 0.0), toInt(tokens(7), -1), toDouble(tokens(8), 0.0), toDouble(tokens(9), 0.0), tokens(10), toDouble(tokens(11), 0.0), tokens(12), toDouble(tokens(13), 0.0), toDouble(tokens(14), 0.0), toDouble(tokens(15), 0.0), toDouble(tokens(16), 0.0), tokens(17), toDouble(tokens(18), 0.0), toInt(tokens(19), -1))
}
// this is loads all the data - a subset of the 1M songs dataset
val dataRDD = sc.textFile("/databricks-datasets/songs/data-001/part-*")
// .. fill in comment
val df = dataRDD.map(parseLine).toDF
// .. fill in comment
df.createOrReplaceTempView("songsTable")
defined class Song
parseLine: (line: String)Song
dataRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/songs/data-001/part-* MapPartitionsRDD[3280] at textFile at command-2972105651606789:30
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]
spark.catalog.listTables.show(false) // make sure the temp view of our table is there
+------------------------------------------------------------+--------+-----------+---------+-----------+
|name |database|description|tableType|isTemporary|
+------------------------------------------------------------+--------+-----------+---------+-----------+
|sentimentlex_csv |default |null |EXTERNAL |false |
|simple_range |default |null |MANAGED |false |
|social_media_usage |default |null |MANAGED |false |
|social_media_usage_table_partitionedbyplatformbucketedbydate|default |null |MANAGED |false |
|songstable |null |null |TEMPORARY|true |
+------------------------------------------------------------+--------+-----------+---------+-----------+
A first inspection
A first step to any data exploration is viewing sample data. For this purpose we can use a simple SQL query that returns first 10 rows.
select * from songsTable limit 10
| artist_id | artist_latitude | artist_longitude | artist_location | artist_name | duration | end_of_fade_in | key | key_confidence | loudness | release | song_hotness | song_id | start_of_fade_out | tempo | time_signature | time_signature_confidence | title | year | partial_sequence |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AR81V6H1187FB48872 | 0.0 | 0.0 | Earl Sixteen | 213.7073 | 0.0 | 11.0 | 0.419 | -12.106 | Soldier of Jah Army | 0.0 | SOVNZSZ12AB018A9B8 | 208.289 | 125.882 | 1.0 | 0.0 | Rastaman | 2003.0 | -1.0 | |
| ARVVZQP11E2835DBCB | 0.0 | 0.0 | Wavves | 133.25016 | 0.0 | 0.0 | 0.282 | 0.596 | Wavvves | 0.471578247701 | SOJTQHQ12A8C143C5F | 128.116 | 89.519 | 1.0 | 0.0 | I Want To See You (And Go To The Movies) | 2009.0 | -1.0 | |
| ARFG9M11187FB3BBCB | 0.0 | 0.0 | Nashua USA | C-Side | 247.32689 | 0.0 | 9.0 | 0.612 | -4.896 | Santa Festival Compilation 2008 vol.1 | 0.0 | SOAJSQL12AB0180501 | 242.196 | 171.278 | 5.0 | 1.0 | Loose on the Dancefloor | 0.0 | 225261.0 |
| ARK4Z2O1187FB45FF0 | 0.0 | 0.0 | Harvest | 337.05751 | 0.247 | 4.0 | 0.46 | -9.092 | Underground Community | 0.0 | SOTDRVW12AB018BEB9 | 327.436 | 84.986 | 4.0 | 0.673 | No Return | 0.0 | 101619.0 | |
| AR4VQSG1187FB57E18 | 35.25082 | -91.74015 | Searcy, AR | Gossip | 430.23628 | 0.0 | 2.0 | 3.4e-2 | -6.846 | Yr Mangled Heart | 0.0 | SOTVOCL12A8AE478DD | 424.06 | 121.998 | 4.0 | 0.847 | Yr Mangled Heart | 2006.0 | 740623.0 |
| ARNBV1X1187B996249 | 0.0 | 0.0 | Alex | 186.80118 | 0.0 | 4.0 | 0.641 | -16.108 | Jolgaledin | 0.0 | SODTGRY12AB0182438 | 166.156 | 140.735 | 4.0 | 5.5e-2 | Mariu Sonur Jesus | 0.0 | 673970.0 | |
| ARXOEZX1187B9B82A1 | 0.0 | 0.0 | Elie Attieh | 361.89995 | 0.0 | 7.0 | 0.863 | -4.919 | ELITE | 0.0 | SOIINTJ12AB0180BA6 | 354.476 | 128.024 | 4.0 | 0.399 | Fe Yom We Leila | 0.0 | 280304.0 | |
| ARXPUIA1187B9A32F1 | 0.0 | 0.0 | Rome, Italy | Simone Cristicchi | 220.00281 | 2.119 | 4.0 | 0.486 | -6.52 | Dall'Altra Parte Del Cancello | 0.484225272411 | SONHXJK12AAF3B5290 | 214.761 | 99.954 | 1.0 | 0.928 | L'Italiano | 2007.0 | 745962.0 |
| ARNPPTH1187B9AD429 | 51.4855 | -0.37196 | Heston, Middlesex, England | Jimmy Page | 156.86485 | 0.334 | 7.0 | 0.493 | -9.962 | No Introduction Necessary [Deluxe Edition] | 0.0 | SOGUHGW12A58A80E06 | 149.269 | 162.48 | 4.0 | 0.534 | Wailing Sounds | 2004.0 | 599250.0 |
| AROGWRA122988FEE45 | 0.0 | 0.0 | Christos Dantis | 256.67873 | 2.537 | 9.0 | 0.742 | -13.404 | Daktilika Apotipomata | 0.0 | SOJJOYI12A8C13399D | 248.912 | 134.944 | 4.0 | 0.162 | Stin Proigoumeni Zoi | 0.0 | 611396.0 |
table("songsTable").printSchema()
root
|-- artist_id: string (nullable = true)
|-- artist_latitude: double (nullable = false)
|-- artist_longitude: double (nullable = false)
|-- artist_location: string (nullable = true)
|-- artist_name: string (nullable = true)
|-- duration: double (nullable = false)
|-- end_of_fade_in: double (nullable = false)
|-- key: integer (nullable = false)
|-- key_confidence: double (nullable = false)
|-- loudness: double (nullable = false)
|-- release: string (nullable = true)
|-- song_hotness: double (nullable = false)
|-- song_id: string (nullable = true)
|-- start_of_fade_out: double (nullable = false)
|-- tempo: double (nullable = false)
|-- time_signature: double (nullable = false)
|-- time_signature_confidence: double (nullable = false)
|-- title: string (nullable = true)
|-- year: double (nullable = false)
|-- partial_sequence: integer (nullable = false)
select count(*) from songsTable
| count(1) |
|---|
| 31369.0 |
table("songsTable").count() // or equivalently with DataFrame API - recall table("songsTable") is a DataFrame
res4: Long = 31369
display(sqlContext.sql("SELECT duration, year FROM songsTable")) // Aggregation is set to 'Average' in 'Plot Options'
| duration | year |
|---|---|
| 213.7073 | 2003.0 |
| 133.25016 | 2009.0 |
| 247.32689 | 0.0 |
| 337.05751 | 0.0 |
| 430.23628 | 2006.0 |
| 186.80118 | 0.0 |
| 361.89995 | 0.0 |
| 220.00281 | 2007.0 |
| 156.86485 | 2004.0 |
| 256.67873 | 0.0 |
| 204.64281 | 0.0 |
| 112.48281 | 0.0 |
| 170.39628 | 0.0 |
| 215.95383 | 0.0 |
| 303.62077 | 0.0 |
| 266.60526 | 0.0 |
| 326.19057 | 1997.0 |
| 51.04281 | 2009.0 |
| 129.4624 | 0.0 |
| 253.33506 | 2003.0 |
| 237.76608 | 2004.0 |
| 132.96281 | 0.0 |
| 399.35955 | 2006.0 |
| 168.75057 | 1991.0 |
| 396.042 | 0.0 |
| 192.10404 | 1968.0 |
| 12.2771 | 2006.0 |
| 367.56853 | 0.0 |
| 189.93587 | 0.0 |
| 233.50812 | 0.0 |
| 462.68036 | 0.0 |
| 202.60526 | 0.0 |
| 241.52771 | 0.0 |
| 275.64363 | 1992.0 |
| 350.69342 | 2007.0 |
| 166.55628 | 1968.0 |
| 249.49506 | 1983.0 |
| 53.86404 | 1992.0 |
| 233.76934 | 2001.0 |
| 275.12118 | 2009.0 |
| 191.13751 | 2006.0 |
| 299.07546 | 0.0 |
| 468.74077 | 0.0 |
| 110.34077 | 0.0 |
| 234.78812 | 2003.0 |
| 705.25342 | 2006.0 |
| 383.52934 | 0.0 |
| 196.10077 | 0.0 |
| 299.20608 | 1998.0 |
| 94.04036 | 0.0 |
| 28.08118 | 2006.0 |
| 207.93424 | 2006.0 |
| 152.0322 | 1999.0 |
| 207.96036 | 2002.0 |
| 371.25179 | 0.0 |
| 288.93995 | 2002.0 |
| 235.93751 | 2004.0 |
| 505.70404 | 0.0 |
| 177.57995 | 0.0 |
| 376.842 | 2004.0 |
| 266.84036 | 2004.0 |
| 270.8371 | 2006.0 |
| 178.18077 | 0.0 |
| 527.17669 | 0.0 |
| 244.27057 | 0.0 |
| 436.47955 | 2006.0 |
| 236.79955 | 0.0 |
| 134.53016 | 2005.0 |
| 181.002 | 0.0 |
| 239.41179 | 1999.0 |
| 72.98567 | 0.0 |
| 214.36036 | 2001.0 |
| 150.59546 | 2007.0 |
| 152.45016 | 1970.0 |
| 218.17424 | 0.0 |
| 290.63791 | 0.0 |
| 149.05424 | 0.0 |
| 440.21506 | 0.0 |
| 212.34893 | 1988.0 |
| 278.67383 | 0.0 |
| 269.60934 | 1974.0 |
| 182.69995 | 2002.0 |
| 207.882 | 2007.0 |
| 102.50404 | 0.0 |
| 437.60281 | 0.0 |
| 216.11057 | 2009.0 |
| 193.25342 | 0.0 |
| 234.16118 | 2009.0 |
| 695.77098 | 0.0 |
| 297.58649 | 1996.0 |
| 265.37751 | 2000.0 |
| 182.85669 | 1990.0 |
| 202.23955 | 0.0 |
| 390.08608 | 2009.0 |
| 242.78159 | 2000.0 |
| 242.54649 | 2002.0 |
| 496.66567 | 2004.0 |
| 395.36281 | 0.0 |
| 234.89261 | 1999.0 |
| 237.84444 | 2005.0 |
| 313.57342 | 2009.0 |
| 489.22077 | 2001.0 |
| 239.98649 | 2004.0 |
| 128.65261 | 0.0 |
| 193.07057 | 0.0 |
| 144.19546 | 0.0 |
| 196.96281 | 2006.0 |
| 222.06649 | 1997.0 |
| 58.38322 | 0.0 |
| 346.14812 | 1998.0 |
| 406.54322 | 0.0 |
| 304.09098 | 2009.0 |
| 180.21832 | 2003.0 |
| 213.41995 | 0.0 |
| 323.44771 | 0.0 |
| 54.7522 | 2009.0 |
| 437.02812 | 1994.0 |
| 268.7473 | 2009.0 |
| 104.75057 | 0.0 |
| 248.60689 | 2006.0 |
| 221.41342 | 0.0 |
| 237.81832 | 1991.0 |
| 216.34567 | 2009.0 |
| 78.94159 | 0.0 |
| 47.22893 | 2005.0 |
| 202.00444 | 2007.0 |
| 293.56363 | 0.0 |
| 206.44526 | 1986.0 |
| 267.78077 | 2003.0 |
| 187.27138 | 2008.0 |
| 249.05098 | 2009.0 |
| 221.51791 | 0.0 |
| 452.88444 | 0.0 |
| 163.76118 | 1992.0 |
| 257.17506 | 0.0 |
| 235.78077 | 0.0 |
| 257.82812 | 1996.0 |
| 195.34322 | 0.0 |
| 478.1971 | 0.0 |
| 268.01587 | 1997.0 |
| 136.93342 | 1983.0 |
| 397.53098 | 0.0 |
| 194.69016 | 2001.0 |
| 580.80608 | 0.0 |
| 177.71057 | 2006.0 |
| 257.43628 | 1999.0 |
| 184.13669 | 0.0 |
| 64.57424 | 2001.0 |
| 123.92444 | 1993.0 |
| 257.07057 | 0.0 |
| 219.48036 | 1996.0 |
| 679.41832 | 0.0 |
| 252.29016 | 1995.0 |
| 311.90159 | 2004.0 |
| 252.76036 | 1998.0 |
| 138.94485 | 0.0 |
| 428.64281 | 0.0 |
| 295.31383 | 0.0 |
| 212.03546 | 0.0 |
| 426.50077 | 0.0 |
| 197.11955 | 0.0 |
| 191.55546 | 0.0 |
| 187.53261 | 2006.0 |
| 184.97261 | 2004.0 |
| 388.41424 | 2009.0 |
| 218.90567 | 0.0 |
| 246.49098 | 0.0 |
| 452.88444 | 0.0 |
| 223.18975 | 0.0 |
| 245.2371 | 0.0 |
| 148.92363 | 0.0 |
| 362.81424 | 2005.0 |
| 171.44118 | 0.0 |
| 207.72526 | 2005.0 |
| 191.29424 | 0.0 |
| 208.50893 | 0.0 |
| 240.24771 | 1995.0 |
| 373.44608 | 2002.0 |
| 172.01587 | 0.0 |
| 153.25995 | 2007.0 |
| 242.36363 | 1994.0 |
| 177.55383 | 0.0 |
| 263.20934 | 1994.0 |
| 191.03302 | 2007.0 |
| 232.77669 | 0.0 |
| 220.65587 | 0.0 |
| 132.57098 | 2002.0 |
| 189.6224 | 1993.0 |
| 32.522 | 1997.0 |
| 173.94893 | 0.0 |
| 268.01587 | 2006.0 |
| 91.97669 | 0.0 |
| 215.77098 | 0.0 |
| 195.47383 | 0.0 |
| 234.81424 | 1977.0 |
| 110.78485 | 0.0 |
| 155.74159 | 0.0 |
| 172.5122 | 0.0 |
| 227.76118 | 1995.0 |
| 233.01179 | 2007.0 |
| 298.89261 | 0.0 |
| 245.36771 | 1994.0 |
| 276.08771 | 2005.0 |
| 375.77098 | 2003.0 |
| 273.71057 | 0.0 |
| 226.92526 | 0.0 |
| 196.46649 | 0.0 |
| 199.65342 | 1995.0 |
| 243.40853 | 0.0 |
| 207.62077 | 2006.0 |
| 252.73424 | 0.0 |
| 244.32281 | 0.0 |
| 152.65914 | 0.0 |
| 203.88526 | 2003.0 |
| 120.16281 | 0.0 |
| 214.77832 | 1977.0 |
| 204.9824 | 0.0 |
| 118.30812 | 1996.0 |
| 205.26975 | 0.0 |
| 499.22567 | 0.0 |
| 217.83465 | 2005.0 |
| 192.57424 | 2005.0 |
| 328.09751 | 0.0 |
| 298.03057 | 1968.0 |
| 501.49832 | 0.0 |
| 276.40118 | 0.0 |
| 507.55873 | 2006.0 |
| 191.08526 | 2008.0 |
| 324.38812 | 0.0 |
| 218.56608 | 0.0 |
| 232.30649 | 0.0 |
| 295.05261 | 1972.0 |
| 225.74975 | 2003.0 |
| 522.00444 | 0.0 |
| 245.86404 | 1967.0 |
| 263.67955 | 0.0 |
| 556.61669 | 2009.0 |
| 227.94404 | 1998.0 |
| 83.82649 | 1964.0 |
| 242.85995 | 0.0 |
| 233.09016 | 2008.0 |
| 201.74322 | 0.0 |
| 476.15955 | 0.0 |
| 370.93832 | 2005.0 |
| 229.17179 | 0.0 |
| 288.07791 | 2001.0 |
| 91.34975 | 0.0 |
| 230.79138 | 2005.0 |
| 256.46975 | 2003.0 |
| 203.44118 | 0.0 |
| 230.81751 | 2003.0 |
| 272.29995 | 0.0 |
| 201.22077 | 2008.0 |
| 204.93016 | 2010.0 |
| 372.84526 | 0.0 |
| 63.65995 | 2005.0 |
| 412.15955 | 0.0 |
| 270.10567 | 0.0 |
| 104.6722 | 0.0 |
| 214.25587 | 1970.0 |
| 230.05995 | 0.0 |
| 155.74159 | 0.0 |
| 218.04363 | 2008.0 |
| 357.77261 | 2007.0 |
| 318.27546 | 1985.0 |
| 444.55138 | 2010.0 |
| 509.07383 | 0.0 |
| 176.95302 | 0.0 |
| 95.34649 | 0.0 |
| 207.67302 | 0.0 |
| 256.67873 | 1994.0 |
| 252.78649 | 0.0 |
| 234.60526 | 0.0 |
| 167.65342 | 0.0 |
| 266.16118 | 0.0 |
| 188.05506 | 0.0 |
| 229.14567 | 2009.0 |
| 227.00363 | 2004.0 |
| 74.50077 | 1992.0 |
| 222.09261 | 0.0 |
| 212.68853 | 1984.0 |
| 155.74159 | 0.0 |
| 153.65179 | 0.0 |
| 548.51873 | 0.0 |
| 445.90975 | 2003.0 |
| 317.49179 | 1999.0 |
| 140.32934 | 0.0 |
| 309.4722 | 0.0 |
| 142.91546 | 0.0 |
| 429.24363 | 2007.0 |
| 172.19873 | 0.0 |
| 215.562 | 0.0 |
| 290.79465 | 2009.0 |
| 197.04118 | 0.0 |
| 309.44608 | 0.0 |
| 265.01179 | 1999.0 |
| 257.64526 | 2000.0 |
| 203.54567 | 0.0 |
| 161.56689 | 0.0 |
| 177.84118 | 0.0 |
| 260.04853 | 2004.0 |
| 195.00363 | 1988.0 |
| 268.042 | 0.0 |
| 195.97016 | 1991.0 |
| 351.92118 | 0.0 |
| 119.35302 | 0.0 |
| 177.24036 | 0.0 |
| 259.83955 | 0.0 |
| 222.51057 | 2008.0 |
| 163.97016 | 2004.0 |
| 139.49342 | 0.0 |
| 158.77179 | 0.0 |
| 193.4624 | 2000.0 |
| 131.082 | 1963.0 |
| 190.95465 | 1998.0 |
| 413.3873 | 2005.0 |
| 134.73914 | 1966.0 |
| 162.40281 | 1965.0 |
| 243.59138 | 1965.0 |
| 180.84526 | 0.0 |
| 315.14077 | 0.0 |
| 221.51791 | 1994.0 |
| 122.53995 | 2008.0 |
| 243.43465 | 1990.0 |
| 200.202 | 1982.0 |
| 95.50322 | 2000.0 |
| 200.4371 | 1998.0 |
| 186.93179 | 0.0 |
| 492.22485 | 1999.0 |
| 359.33995 | 1972.0 |
| 89.39057 | 1990.0 |
| 212.81914 | 0.0 |
| 315.03628 | 1996.0 |
| 214.69995 | 0.0 |
| 137.92608 | 1993.0 |
| 559.49016 | 0.0 |
| 382.14485 | 1991.0 |
| 430.31465 | 2008.0 |
| 171.25832 | 0.0 |
| 210.12853 | 2002.0 |
| 53.18485 | 2005.0 |
| 78.65424 | 1993.0 |
| 209.162 | 2008.0 |
| 237.60934 | 2006.0 |
| 184.47628 | 2009.0 |
| 323.02975 | 1997.0 |
| 158.27546 | 0.0 |
| 213.86404 | 0.0 |
| 470.69995 | 0.0 |
| 229.79873 | 2005.0 |
| 392.22812 | 0.0 |
| 196.62322 | 0.0 |
| 80.97914 | 0.0 |
| 124.55138 | 1989.0 |
| 230.32118 | 1971.0 |
| 132.51873 | 0.0 |
| 112.95302 | 1994.0 |
| 131.52608 | 0.0 |
| 153.25995 | 2010.0 |
| 211.01669 | 0.0 |
| 218.93179 | 2008.0 |
| 175.0722 | 2010.0 |
| 116.61016 | 1997.0 |
| 251.45424 | 2001.0 |
| 269.50485 | 2004.0 |
| 231.47057 | 0.0 |
| 298.37016 | 1996.0 |
| 314.122 | 2005.0 |
| 263.99302 | 0.0 |
| 480.91383 | 2001.0 |
| 305.10975 | 0.0 |
| 280.16281 | 0.0 |
| 295.65342 | 1999.0 |
| 411.45424 | 2007.0 |
| 265.97832 | 0.0 |
| 153.96526 | 0.0 |
| 210.31138 | 1970.0 |
| 241.44934 | 0.0 |
| 235.33669 | 0.0 |
| 352.65261 | 0.0 |
| 293.35465 | 0.0 |
| 243.66975 | 2003.0 |
| 133.22404 | 0.0 |
| 233.03791 | 0.0 |
| 339.93098 | 0.0 |
| 249.80853 | 1993.0 |
| 253.72689 | 2004.0 |
| 94.35383 | 1981.0 |
| 130.63791 | 0.0 |
| 195.36934 | 0.0 |
| 229.25016 | 2007.0 |
| 314.64444 | 2007.0 |
| 329.1424 | 1998.0 |
| 224.46975 | 1990.0 |
| 215.562 | 1987.0 |
| 236.85179 | 1990.0 |
| 197.11955 | 1957.0 |
| 251.76771 | 2004.0 |
| 183.50975 | 0.0 |
| 268.01587 | 2005.0 |
| 413.02159 | 0.0 |
| 385.17506 | 2000.0 |
| 358.16444 | 0.0 |
| 164.77995 | 0.0 |
| 253.36118 | 2004.0 |
| 196.49261 | 2007.0 |
| 157.6224 | 1999.0 |
| 310.93506 | 0.0 |
| 434.96444 | 1991.0 |
| 157.04771 | 1991.0 |
| 266.16118 | 2007.0 |
| 267.59791 | 1977.0 |
| 303.90812 | 0.0 |
| 277.18485 | 2009.0 |
| 272.22159 | 0.0 |
| 155.95057 | 0.0 |
| 127.00689 | 1997.0 |
| 152.86812 | 2005.0 |
| 224.7571 | 1990.0 |
| 175.41179 | 0.0 |
| 151.97995 | 0.0 |
| 199.99302 | 0.0 |
| 251.53261 | 0.0 |
| 252.96934 | 2004.0 |
| 181.13261 | 1984.0 |
| 195.49995 | 0.0 |
| 328.202 | 2001.0 |
| 187.71546 | 0.0 |
| 166.94812 | 1985.0 |
| 242.72934 | 1988.0 |
| 218.80118 | 2005.0 |
| 205.68771 | 0.0 |
| 146.93832 | 1996.0 |
| 449.4624 | 2000.0 |
| 503.40526 | 0.0 |
| 181.34159 | 0.0 |
| 143.90812 | 0.0 |
| 406.36036 | 0.0 |
| 269.87057 | 0.0 |
| 265.29914 | 0.0 |
| 242.88608 | 0.0 |
| 110.39302 | 0.0 |
| 262.84363 | 0.0 |
| 334.00118 | 1990.0 |
| 173.81832 | 2007.0 |
| 608.78322 | 0.0 |
| 197.22404 | 0.0 |
| 163.94404 | 2008.0 |
| 93.09995 | 2001.0 |
| 206.75873 | 0.0 |
| 183.50975 | 0.0 |
| 402.442 | 0.0 |
| 735.79057 | 1986.0 |
| 233.19465 | 1997.0 |
| 326.55628 | 0.0 |
| 525.50485 | 0.0 |
| 396.19873 | 0.0 |
| 171.12771 | 0.0 |
| 318.1971 | 2006.0 |
| 323.70893 | 2002.0 |
| 526.99383 | 0.0 |
| 161.09669 | 1991.0 |
| 168.41098 | 1990.0 |
| 249.57342 | 0.0 |
| 405.4722 | 0.0 |
| 271.0722 | 2010.0 |
| 190.69342 | 2009.0 |
| 151.61424 | 2001.0 |
| 121.57342 | 0.0 |
| 117.08036 | 0.0 |
| 244.24444 | 2008.0 |
| 246.85669 | 0.0 |
| 144.03873 | 2007.0 |
| 169.79546 | 1988.0 |
| 193.93261 | 2004.0 |
| 325.77261 | 0.0 |
| 337.34485 | 0.0 |
| 143.67302 | 2009.0 |
| 211.69587 | 0.0 |
| 299.4673 | 1978.0 |
| 159.76444 | 0.0 |
| 337.31873 | 0.0 |
| 259.18649 | 2007.0 |
| 221.64853 | 0.0 |
| 164.54485 | 0.0 |
| 56.34567 | 0.0 |
| 184.21506 | 0.0 |
| 249.23383 | 2010.0 |
| 127.29424 | 1994.0 |
| 306.6771 | 1980.0 |
| 168.98567 | 0.0 |
| 290.2722 | 0.0 |
| 182.33424 | 2004.0 |
| 180.92363 | 0.0 |
| 233.76934 | 1990.0 |
| 423.70567 | 0.0 |
| 139.36281 | 0.0 |
| 289.72363 | 2005.0 |
| 100.96281 | 2005.0 |
| 153.05098 | 2009.0 |
| 129.25342 | 0.0 |
| 190.11873 | 1993.0 |
| 158.1971 | 0.0 |
| 234.94485 | 2000.0 |
| 256.02567 | 0.0 |
| 279.84934 | 0.0 |
| 217.7824 | 2005.0 |
| 271.62077 | 2005.0 |
| 372.34893 | 0.0 |
| 264.88118 | 0.0 |
| 270.18404 | 1984.0 |
| 42.86649 | 0.0 |
| 247.27465 | 0.0 |
| 185.10322 | 1990.0 |
| 333.94893 | 0.0 |
| 380.49914 | 1999.0 |
| 517.72036 | 0.0 |
| 208.95302 | 2006.0 |
| 359.73179 | 0.0 |
| 378.72281 | 1995.0 |
| 110.41914 | 0.0 |
| 237.37424 | 2003.0 |
| 136.30649 | 0.0 |
| 153.73016 | 2005.0 |
| 209.8673 | 2007.0 |
| 224.86159 | 0.0 |
| 202.34404 | 0.0 |
| 229.43302 | 0.0 |
| 300.56444 | 2003.0 |
| 264.35873 | 0.0 |
| 213.9424 | 0.0 |
| 164.77995 | 2004.0 |
| 206.75873 | 0.0 |
| 249.73016 | 2009.0 |
| 521.11628 | 2002.0 |
| 240.09098 | 0.0 |
| 347.89832 | 0.0 |
| 224.96608 | 1993.0 |
| 250.25261 | 0.0 |
| 419.00363 | 0.0 |
| 593.3971 | 1958.0 |
| 269.89669 | 1999.0 |
| 235.12771 | 2009.0 |
| 180.76689 | 0.0 |
| 304.03873 | 2004.0 |
| 253.36118 | 2006.0 |
| 311.74485 | 2006.0 |
| 353.43628 | 0.0 |
| 337.00526 | 0.0 |
| 305.00526 | 2006.0 |
| 113.76281 | 2007.0 |
| 379.74159 | 0.0 |
| 258.76853 | 1993.0 |
| 157.64853 | 0.0 |
| 352.28689 | 0.0 |
| 221.51791 | 0.0 |
| 249.44281 | 0.0 |
| 205.42649 | 0.0 |
| 166.922 | 0.0 |
| 250.25261 | 0.0 |
| 224.73098 | 2003.0 |
| 316.83873 | 2002.0 |
| 269.34812 | 2007.0 |
| 188.02893 | 0.0 |
| 276.87138 | 2001.0 |
| 263.02649 | 0.0 |
| 320.44363 | 0.0 |
| 531.43465 | 2005.0 |
| 126.85016 | 2008.0 |
| 232.01914 | 0.0 |
| 243.87873 | 0.0 |
| 288.60036 | 2004.0 |
| 817.57995 | 2007.0 |
| 200.9073 | 0.0 |
| 229.48526 | 2009.0 |
| 263.65342 | 1971.0 |
| 209.71057 | 2008.0 |
| 430.54975 | 2007.0 |
| 531.9571 | 0.0 |
| 277.39383 | 0.0 |
| 253.41342 | 1999.0 |
| 538.5922 | 0.0 |
| 187.34975 | 0.0 |
| 189.67465 | 2006.0 |
| 247.66649 | 0.0 |
| 196.15302 | 2008.0 |
| 248.45016 | 0.0 |
| 266.26567 | 2005.0 |
| 174.41914 | 0.0 |
| 241.21424 | 1996.0 |
| 213.39383 | 0.0 |
| 201.66485 | 1956.0 |
| 141.16526 | 0.0 |
| 198.76526 | 2010.0 |
| 234.03057 | 2002.0 |
| 293.77261 | 0.0 |
| 149.83791 | 0.0 |
| 193.09669 | 0.0 |
| 416.62649 | 2007.0 |
| 206.18404 | 2008.0 |
| 292.15302 | 0.0 |
| 209.55383 | 1997.0 |
| 303.46404 | 0.0 |
| 284.31628 | 0.0 |
| 209.34485 | 0.0 |
| 131.34322 | 2010.0 |
| 127.16363 | 0.0 |
| 228.98893 | 1983.0 |
| 18.18077 | 0.0 |
| 202.762 | 1999.0 |
| 475.21914 | 1989.0 |
| 434.52036 | 2002.0 |
| 306.36363 | 0.0 |
| 251.84608 | 2007.0 |
| 392.80281 | 1999.0 |
| 191.63383 | 0.0 |
| 207.90812 | 0.0 |
| 298.86649 | 0.0 |
| 195.36934 | 0.0 |
| 236.06812 | 1995.0 |
| 315.76771 | 2009.0 |
| 214.5171 | 0.0 |
| 140.90404 | 0.0 |
| 147.66975 | 0.0 |
| 230.50404 | 0.0 |
| 259.99628 | 2010.0 |
| 234.70975 | 1994.0 |
| 191.97342 | 1992.0 |
| 305.6322 | 0.0 |
| 197.53751 | 1997.0 |
| 152.05832 | 0.0 |
| 360.82893 | 1998.0 |
| 440.37179 | 0.0 |
| 211.09506 | 2009.0 |
| 362.60526 | 1998.0 |
| 364.64281 | 1997.0 |
| 267.12771 | 0.0 |
| 380.81261 | 2007.0 |
| 248.13669 | 1995.0 |
| 253.20444 | 0.0 |
| 244.03546 | 0.0 |
| 159.13751 | 0.0 |
| 246.12526 | 0.0 |
| 40.95955 | 2005.0 |
| 200.04526 | 2007.0 |
| 155.08853 | 0.0 |
| 144.66567 | 0.0 |
| 170.86649 | 0.0 |
| 286.71955 | 2001.0 |
| 333.19138 | 1996.0 |
| 542.1971 | 0.0 |
| 222.37995 | 0.0 |
| 195.68281 | 2003.0 |
| 440.00608 | 0.0 |
| 223.08526 | 0.0 |
| 378.98404 | 0.0 |
| 91.45424 | 1983.0 |
| 114.65098 | 2009.0 |
| 218.80118 | 0.0 |
| 242.36363 | 0.0 |
| 143.0722 | 1962.0 |
| 242.78159 | 2007.0 |
| 256.31302 | 0.0 |
| 244.37506 | 0.0 |
| 36.54485 | 2007.0 |
| 401.94567 | 1999.0 |
| 178.65098 | 2003.0 |
| 277.002 | 2009.0 |
| 288.70485 | 2002.0 |
| 228.91057 | 2006.0 |
| 204.06812 | 0.0 |
| 212.40118 | 0.0 |
| 224.31302 | 2008.0 |
| 195.7873 | 1985.0 |
| 244.63628 | 2005.0 |
| 241.81506 | 0.0 |
| 224.10404 | 2001.0 |
| 132.75383 | 2008.0 |
| 113.3971 | 0.0 |
| 237.03465 | 0.0 |
| 162.58567 | 1987.0 |
| 247.24853 | 2008.0 |
| 285.30893 | 0.0 |
| 318.24934 | 0.0 |
| 375.53587 | 2007.0 |
| 188.78649 | 0.0 |
| 108.79955 | 0.0 |
| 270.91546 | 0.0 |
| 249.23383 | 0.0 |
| 192.80934 | 1984.0 |
| 295.20934 | 0.0 |
| 177.84118 | 2006.0 |
| 242.6771 | 0.0 |
| 245.28934 | 1999.0 |
| 105.61261 | 0.0 |
| 329.29914 | 0.0 |
| 207.46404 | 0.0 |
| 225.51465 | 2007.0 |
| 123.8722 | 0.0 |
| 270.10567 | 2008.0 |
| 174.86322 | 0.0 |
| 377.28608 | 0.0 |
| 220.18567 | 2005.0 |
| 1190.53016 | 0.0 |
| 1518.65424 | 0.0 |
| 438.64771 | 2008.0 |
| 344.842 | 2001.0 |
| 76.48608 | 1994.0 |
| 174.52363 | 2002.0 |
| 581.14567 | 0.0 |
| 177.68444 | 0.0 |
| 125.962 | 0.0 |
| 160.39138 | 2006.0 |
| 211.27791 | 0.0 |
| 182.88281 | 0.0 |
| 261.53751 | 2005.0 |
| 285.80526 | 0.0 |
| 263.44444 | 1991.0 |
| 133.32853 | 1998.0 |
| 313.99138 | 1990.0 |
| 199.18322 | 0.0 |
| 200.98567 | 0.0 |
| 170.84036 | 2009.0 |
| 194.48118 | 0.0 |
| 241.65832 | 0.0 |
| 245.15873 | 1970.0 |
| 262.66077 | 2002.0 |
| 307.46077 | 1999.0 |
| 295.20934 | 0.0 |
| 259.52608 | 0.0 |
| 347.19302 | 0.0 |
| 206.91546 | 0.0 |
| 399.51628 | 2008.0 |
| 271.25506 | 0.0 |
| 172.7473 | 1991.0 |
| 231.65342 | 1993.0 |
| 208.1171 | 1991.0 |
| 195.76118 | 1983.0 |
| 723.27791 | 1970.0 |
| 282.95791 | 0.0 |
| 153.12934 | 0.0 |
| 207.15057 | 0.0 |
| 174.41914 | 0.0 |
| 269.29587 | 2005.0 |
| 275.3824 | 0.0 |
| 149.41995 | 0.0 |
| 108.35546 | 1963.0 |
| 243.69587 | 0.0 |
| 308.27057 | 1996.0 |
| 204.90404 | 2007.0 |
| 311.24853 | 2001.0 |
| 164.77995 | 0.0 |
| 449.51465 | 0.0 |
| 140.93016 | 0.0 |
| 165.22404 | 2005.0 |
| 53.26322 | 2007.0 |
| 218.80118 | 2005.0 |
| 300.85179 | 1991.0 |
| 388.75383 | 2007.0 |
| 150.77832 | 1970.0 |
| 293.11955 | 0.0 |
| 177.71057 | 0.0 |
| 184.11057 | 2005.0 |
| 225.17506 | 2009.0 |
| 272.19546 | 2002.0 |
| 157.67465 | 0.0 |
| 204.61669 | 0.0 |
| 93.98812 | 0.0 |
| 204.45995 | 0.0 |
| 307.1473 | 0.0 |
| 347.0624 | 1970.0 |
| 184.73751 | 2005.0 |
| 146.65098 | 0.0 |
| 513.90649 | 2001.0 |
| 293.85098 | 1970.0 |
| 121.73016 | 2003.0 |
| 86.72608 | 0.0 |
| 171.25832 | 2007.0 |
| 264.95955 | 2007.0 |
| 411.68934 | 0.0 |
| 190.79791 | 1971.0 |
| 159.65995 | 0.0 |
| 162.89914 | 0.0 |
| 205.97506 | 2006.0 |
| 204.59057 | 0.0 |
| 117.02812 | 0.0 |
| 135.28771 | 0.0 |
| 163.65669 | 0.0 |
| 254.95465 | 0.0 |
| 178.31138 | 2001.0 |
| 150.77832 | 2001.0 |
| 410.53995 | 2001.0 |
| 222.30159 | 0.0 |
| 314.74893 | 0.0 |
| 233.11628 | 0.0 |
| 226.21995 | 0.0 |
| 441.67791 | 0.0 |
| 120.99873 | 2009.0 |
| 157.75302 | 0.0 |
| 203.65016 | 0.0 |
| 287.73832 | 0.0 |
| 226.7424 | 1997.0 |
| 69.56363 | 1993.0 |
| 174.52363 | 2007.0 |
| 363.67628 | 0.0 |
| 136.48934 | 0.0 |
| 390.60853 | 0.0 |
| 284.60363 | 0.0 |
| 291.81342 | 1990.0 |
| 502.7522 | 0.0 |
| 197.27628 | 0.0 |
| 329.53424 | 2009.0 |
| 340.1922 | 2003.0 |
| 170.94485 | 0.0 |
| 113.57995 | 0.0 |
| 205.24363 | 2009.0 |
| 169.22077 | 1994.0 |
| 285.70077 | 1980.0 |
| 221.23057 | 0.0 |
| 310.38649 | 0.0 |
| 353.48853 | 2008.0 |
| 415.92118 | 0.0 |
| 150.59546 | 0.0 |
| 236.90404 | 0.0 |
| 227.42159 | 1981.0 |
| 229.8771 | 1995.0 |
| 359.3922 | 0.0 |
| 403.17342 | 1998.0 |
| 296.59383 | 1997.0 |
| 117.65506 | 0.0 |
| 241.3971 | 0.0 |
| 34.92526 | 0.0 |
| 188.31628 | 0.0 |
| 409.02485 | 2002.0 |
| 335.5424 | 0.0 |
| 354.63791 | 0.0 |
| 213.31546 | 2007.0 |
| 238.62812 | 0.0 |
| 193.33179 | 1972.0 |
| 225.33179 | 0.0 |
| 166.84363 | 0.0 |
| 79.96036 | 1990.0 |
| 158.69342 | 2000.0 |
| 176.53506 | 0.0 |
| 347.61098 | 1999.0 |
| 106.39628 | 1994.0 |
| 147.93098 | 0.0 |
| 446.92853 | 0.0 |
| 360.22812 | 0.0 |
| 214.56934 | 0.0 |
| 325.35465 | 0.0 |
| 413.23057 | 0.0 |
| 218.04363 | 2001.0 |
| 215.30077 | 2002.0 |
| 57.44281 | 0.0 |
| 247.48363 | 2006.0 |
| 793.25995 | 0.0 |
| 467.3824 | 0.0 |
| 327.00036 | 1984.0 |
| 232.72444 | 2006.0 |
| 251.68934 | 0.0 |
| 197.3024 | 0.0 |
| 193.88036 | 0.0 |
| 383.32036 | 2004.0 |
| 269.71383 | 0.0 |
| 255.05914 | 0.0 |
| 337.18812 | 2009.0 |
| 240.92689 | 0.0 |
| 206.18404 | 2002.0 |
| 143.22893 | 0.0 |
| 244.27057 | 1980.0 |
| 83.56526 | 0.0 |
| 428.40771 | 0.0 |
| 261.11955 | 2007.0 |
| 208.37832 | 2008.0 |
| 369.78893 | 0.0 |
| 47.17669 | 2006.0 |
| 239.3073 | 0.0 |
| 17.37098 | 1993.0 |
| 257.04444 | 0.0 |
| 198.63465 | 0.0 |
| 208.40444 | 2002.0 |
| 338.28526 | 2005.0 |
| 175.15057 | 0.0 |
| 234.97098 | 0.0 |
| 275.06893 | 0.0 |
| 186.46159 | 0.0 |
| 201.74322 | 0.0 |
| 237.58322 | 0.0 |
| 219.402 | 0.0 |
| 461.29587 | 0.0 |
| 196.67546 | 0.0 |
| 290.63791 | 0.0 |
| 328.22812 | 0.0 |
| 260.64934 | 1981.0 |
| 245.83791 | 0.0 |
| 97.54077 | 0.0 |
| 248.0322 | 0.0 |
| 175.33342 | 1998.0 |
| 199.57506 | 0.0 |
| 229.45914 | 2005.0 |
| 902.26893 | 2000.0 |
| 271.12444 | 1991.0 |
| 211.17342 | 0.0 |
| 179.3824 | 1967.0 |
| 156.96934 | 0.0 |
| 281.0771 | 1998.0 |
| 291.97016 | 0.0 |
| 392.85506 | 0.0 |
| 223.00689 | 0.0 |
| 269.94893 | 1987.0 |
| 36.64934 | 1996.0 |
| 309.26322 | 0.0 |
| 178.41587 | 0.0 |
| 206.75873 | 2006.0 |
| 155.68934 | 1971.0 |
| 254.71955 | 1993.0 |
| 133.11955 | 0.0 |
| 260.362 | 0.0 |
| 135.28771 | 1984.0 |
| 158.27546 | 2005.0 |
| 154.93179 | 0.0 |
| 205.84444 | 2005.0 |
| 276.21832 | 0.0 |
| 193.61914 | 0.0 |
| 153.73016 | 1997.0 |
| 389.11955 | 0.0 |
| 195.23873 | 2007.0 |
| 210.72934 | 1995.0 |
| 336.06485 | 0.0 |
| 263.02649 | 0.0 |
| 230.26893 | 2001.0 |
| 40.6722 | 2007.0 |
| 255.92118 | 2009.0 |
| 305.60608 | 1995.0 |
| 177.8673 | 0.0 |
| 361.11628 | 0.0 |
| 357.66812 | 0.0 |
| 196.49261 | 2004.0 |
| 218.40934 | 1992.0 |
| 91.58485 | 0.0 |
| 185.25995 | 0.0 |
| 282.80118 | 0.0 |
| 244.68853 | 0.0 |
| 215.40526 | 1993.0 |
| 211.19955 | 1978.0 |
| 327.75791 | 0.0 |
| 510.40608 | 2005.0 |
| 212.74077 | 2009.0 |
| 120.86812 | 2008.0 |
| 507.08853 | 2001.0 |
| 265.11628 | 0.0 |
| 183.06567 | 0.0 |
| 199.54893 | 1982.0 |
| 41.92608 | 0.0 |
| 164.75383 | 0.0 |
| 267.33669 | 0.0 |
| 208.74404 | 1984.0 |
| 253.09995 | 2007.0 |
| 244.50567 | 0.0 |
| 195.73506 | 2007.0 |
| 160.07791 | 2007.0 |
| 327.70567 | 0.0 |
| 174.86322 | 0.0 |
| 272.92689 | 2005.0 |
| 251.53261 | 0.0 |
| 216.99873 | 0.0 |
| 195.3171 | 0.0 |
| 247.11791 | 0.0 |
| 101.3024 | 0.0 |
| 315.97669 | 2003.0 |
| 449.67138 | 0.0 |
| 173.16526 | 1998.0 |
| 394.44853 | 0.0 |
| 226.69016 | 2007.0 |
| 219.11465 | 0.0 |
| 240.92689 | 1997.0 |
| 227.91791 | 1999.0 |
| 119.84934 | 0.0 |
| 109.92281 | 1997.0 |
| 116.08771 | 0.0 |
| 187.71546 | 1975.0 |
| 191.65995 | 0.0 |
| 116.32281 | 1979.0 |
| 482.45506 | 0.0 |
| 262.71302 | 2003.0 |
| 208.97914 | 2005.0 |
| 209.81506 | 1975.0 |
| 129.85424 | 2002.0 |
| 219.0624 | 0.0 |
| 500.4273 | 2010.0 |
| 224.7571 | 0.0 |
| 274.85995 | 2003.0 |
| 145.162 | 1993.0 |
| 211.27791 | 0.0 |
| 167.49669 | 1981.0 |
| 415.7122 | 0.0 |
| 346.33098 | 0.0 |
| 399.69914 | 1999.0 |
| 136.56771 | 0.0 |
Exercises
- Why do you think average song durations increase dramatically in 70's?
- Add error bars with standard deviation around each average point in the plot.
- How did average loudness change over time?
- How did tempo change over time?
- What other aspects of songs can you explore with this technique?
Sampling and visualizing
You can dive deep into Scala visualisations here: - https://docs.databricks.com/notebooks/visualizations/charts-and-graphs-scala.html
You can also use R and Python for visualisations in the same notebook: - https://docs.databricks.com/notebooks/visualizations/index.html
Million Song Dataset - Kaggle Challenge
Predict which songs a user will listen to.
SOURCE: This is just a Scala-rification of the Python notebook published in databricks community edition in 2016.
Stage 3: Modeling Songs via k-means

This is the third step into our project. In the first step we parsed raw text files and created a table. Then we explored different aspects of data and learned that things have been changing over time. In this step we attempt to gain deeper understanding of our data by categorizing (a.k.a. clustering) our data. For the sake of training we pick a fairly simple model based on only three parameters. We leave more sophisticated modeling as exercies to the reader
We pick the most commonly used and simplest clustering algorithm (KMeans) for our job. The SparkML KMeans implementation expects input in a vector column. Fortunately, there are already utilities in SparkML that can help us convert existing columns in our table to a vector field. It is called VectorAssembler. Here we import that functionality and use it to create a new DataFrame
// Let's quickly do everything to register the tempView of the table here
// this is a case class for our row objects
case class Song(artist_id: String, artist_latitude: Double, artist_longitude: Double, artist_location: String, artist_name: String, duration: Double, end_of_fade_in: Double, key: Int, key_confidence: Double, loudness: Double, release: String, song_hotness: Double, song_id: String, start_of_fade_out: Double, tempo: Double, time_signature: Double, time_signature_confidence: Double, title: String, year: Double, partial_sequence: Int)
def parseLine(line: String): Song = {
// this is robust parsing by try-catching type exceptions
def toDouble(value: String, defaultVal: Double): Double = {
try {
value.toDouble
} catch {
case e: Exception => defaultVal
}
}
def toInt(value: String, defaultVal: Int): Int = {
try {
value.toInt
} catch {
case e: Exception => defaultVal
}
}
// splitting the sting of each line by the delimiter TAB character '\t'
val tokens = line.split("\t")
// making song objects
Song(tokens(0), toDouble(tokens(1), 0.0), toDouble(tokens(2), 0.0), tokens(3), tokens(4), toDouble(tokens(5), 0.0), toDouble(tokens(6), 0.0), toInt(tokens(7), -1), toDouble(tokens(8), 0.0), toDouble(tokens(9), 0.0), tokens(10), toDouble(tokens(11), 0.0), tokens(12), toDouble(tokens(13), 0.0), toDouble(tokens(14), 0.0), toDouble(tokens(15), 0.0), toDouble(tokens(16), 0.0), tokens(17), toDouble(tokens(18), 0.0), toInt(tokens(19), -1))
}
// this is loads all the data - a subset of the 1M songs dataset
val dataRDD = sc.textFile("/databricks-datasets/songs/data-001/part-*")
// .. fill in comment
val df = dataRDD.map(parseLine).toDF
// .. fill in comment
df.createOrReplaceTempView("songsTable")
defined class Song
parseLine: (line: String)Song
dataRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/songs/data-001/part-* MapPartitionsRDD[3305] at textFile at command-2972105651606577:32
df: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 18 more fields]
import org.apache.spark.ml.feature.VectorAssembler
val trainingData = new VectorAssembler()
.setInputCols(Array("duration", "tempo", "loudness"))
.setOutputCol("features")
.transform(table("songsTable"))
import org.apache.spark.ml.feature.VectorAssembler
trainingData: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 19 more fields]
All we have done above with the VectorAssembler method is:
- created a DataFrame called
trainingData - that
transformed ourtablecalledsongsTable - by adding an output column named
featuresusingsetOutputCol("features") - that was obtained from an
Arrayof thesongsTable's columns namedduration,tempoandloudnessusingsetInputCols(Array("duration", "tempo", "loudness")).
trainingData.take(3) // see first 3 rows of trainingData DataFrame, notice the vectors in the last column
res3: Array[org.apache.spark.sql.Row] = Array([AR81V6H1187FB48872,0.0,0.0,,Earl Sixteen,213.7073,0.0,11,0.419,-12.106,Soldier of Jah Army,0.0,SOVNZSZ12AB018A9B8,208.289,125.882,1.0,0.0,Rastaman,2003.0,-1,[213.7073,125.882,-12.106]], [ARVVZQP11E2835DBCB,0.0,0.0,,Wavves,133.25016,0.0,0,0.282,0.596,Wavvves,0.471578247701,SOJTQHQ12A8C143C5F,128.116,89.519,1.0,0.0,I Want To See You (And Go To The Movies),2009.0,-1,[133.25016,89.519,0.596]], [ARFG9M11187FB3BBCB,0.0,0.0,Nashua USA,C-Side,247.32689,0.0,9,0.612,-4.896,Santa Festival Compilation 2008 vol.1,0.0,SOAJSQL12AB0180501,242.196,171.278,5.0,1.0,Loose on the Dancefloor,0.0,225261,[247.32689,171.278,-4.896]])
Transformers
A Transformer is an abstraction that includes feature transformers and learned models. Technically, a Transformer implements a method transform(), which converts one DataFrame into another, generally by appending one or more columns. For example:
- A feature transformer might take a DataFrame, read a column (e.g., text), map it into a new column (e.g., feature vectors), and output a new DataFrame with the mapped column appended.
- A learning model might take a DataFrame, read the column containing feature vectors, predict the label for each feature vector, and output a new DataFrame with predicted labels appended as a column.
Estimators
An Estimator abstracts the concept of a learning algorithm or any algorithm that fits or trains on data.
Technically, an Estimator implements a method fit(), which accepts a DataFrame and produces a Model, which is a Transformer.
For example, a learning algorithm such as LogisticRegression is an Estimator, and calling fit() trains a LogisticRegressionModel, which is a Model and hence a Transformer.
display(trainingData.select("duration", "tempo", "loudness", "features").limit(5)) // see features in more detail
Demonstration of the standard algorithm
(1) (2)
(3)
(4)
- k initial "means" (in this case k=3) are randomly generated within the data domain (shown in color).
- k clusters are created by associating every observation with the nearest mean. The partitions here represent the Voronoi diagram generated by the means.
- The centroid of each of the k clusters becomes the new mean.
- Steps 2 and 3 are repeated until local convergence has been reached.
The "assignment" step 2 is also referred to as expectation step, the "update step" 3 as maximization step, making this algorithm a variant of the generalized expectation-maximization algorithm.
**Caveats: ** As k-means is a heuristic algorithm, there is no guarantee that it will converge to the global optimum, and the result may depend on the initial clusters. As the algorithm is usually very fast, it is common to run it multiple times with different starting conditions. However, in the worst case, k-means can be very slow to converge. For more details see https://en.wikipedia.org/wiki/K-means_clustering that is also embedded in-place below.
CAUTION!
Iris flower data set, clustered using
- k-means (left) and
- true species in the data set (right).
k-means clustering result for the Iris flower data set and actual species visualized using ELKI. Cluster means are marked using larger, semi-transparent symbols.
Note that k-means is non-determinicstic, so results vary. Cluster means are visualized using larger, semi-transparent markers. The visualization was generated using ELKI.
With some cautionary tales we go ahead with applying k-means to our dataset next.
We can now pass this new DataFrame to the KMeans model and ask it to categorize different rows in our data to two different classes (setK(2)). We place the model in a immutable value named model.
Note: This command performs multiple spark jobs (one job per iteration in the KMeans algorithm). You will see the progress bar starting over and over again.
import org.apache.spark.ml.clustering.KMeans
val model = new KMeans().setK(2).fit(trainingData) // 37 seconds in 5 workers cluster
import org.apache.spark.ml.clustering.KMeans
model: org.apache.spark.ml.clustering.KMeansModel = KMeansModel: uid=kmeans_bcf31957c6b5, k=2, distanceMeasure=euclidean, numFeatures=3
//model. // uncomment and place cursor next to . and hit Tab to see all methods on model
model.clusterCenters // get cluster centres
res5: Array[org.apache.spark.ml.linalg.Vector] = Array([208.72069181761958,124.38376303683945,-9.986137113920135], [441.1413218945454,123.00973381818183,-10.560375818181816])
val modelTransformed = model.transform(trainingData) // to get predictions as last column
modelTransformed: org.apache.spark.sql.DataFrame = [artist_id: string, artist_latitude: double ... 20 more fields]
Remember that ML Pipelines works with DataFrames. So, our trainingData and modelTransformed are both DataFrames
trainingData.printSchema
root
|-- artist_id: string (nullable = true)
|-- artist_latitude: double (nullable = false)
|-- artist_longitude: double (nullable = false)
|-- artist_location: string (nullable = true)
|-- artist_name: string (nullable = true)
|-- duration: double (nullable = false)
|-- end_of_fade_in: double (nullable = false)
|-- key: integer (nullable = false)
|-- key_confidence: double (nullable = false)
|-- loudness: double (nullable = false)
|-- release: string (nullable = true)
|-- song_hotness: double (nullable = false)
|-- song_id: string (nullable = true)
|-- start_of_fade_out: double (nullable = false)
|-- tempo: double (nullable = false)
|-- time_signature: double (nullable = false)
|-- time_signature_confidence: double (nullable = false)
|-- title: string (nullable = true)
|-- year: double (nullable = false)
|-- partial_sequence: integer (nullable = false)
|-- features: vector (nullable = true)
modelTransformed.printSchema
root
|-- artist_id: string (nullable = true)
|-- artist_latitude: double (nullable = false)
|-- artist_longitude: double (nullable = false)
|-- artist_location: string (nullable = true)
|-- artist_name: string (nullable = true)
|-- duration: double (nullable = false)
|-- end_of_fade_in: double (nullable = false)
|-- key: integer (nullable = false)
|-- key_confidence: double (nullable = false)
|-- loudness: double (nullable = false)
|-- release: string (nullable = true)
|-- song_hotness: double (nullable = false)
|-- song_id: string (nullable = true)
|-- start_of_fade_out: double (nullable = false)
|-- tempo: double (nullable = false)
|-- time_signature: double (nullable = false)
|-- time_signature_confidence: double (nullable = false)
|-- title: string (nullable = true)
|-- year: double (nullable = false)
|-- partial_sequence: integer (nullable = false)
|-- features: vector (nullable = true)
|-- prediction: integer (nullable = false)
- The column
featuresthat we specified as output column to ourVectorAssemblercontains the features - The new column
predictionin modelTransformed contains the predicted output
val transformed = modelTransformed.select("duration", "tempo", "loudness", "prediction")
transformed: org.apache.spark.sql.DataFrame = [duration: double, tempo: double ... 2 more fields]
To comfortably visualize the data we produce a random sample. Remember the display() function? We can use it to produce a nicely rendered table of transformed DataFrame.
// just sampling the fraction 0.005 of all the rows at random,
// 'false' argument to sample is for sampling without replacement
display(transformed.sample(false, fraction = 0.005, 12988934L))
| duration | tempo | loudness | prediction |
|---|---|---|---|
| 265.97832 | 129.987 | -5.065 | 0.0 |
| 306.36363 | 127.529 | -8.273 | 0.0 |
| 195.73506 | 115.191 | -6.327 | 0.0 |
| 257.82812 | 92.892 | -13.621 | 0.0 |
| 177.55383 | 149.853 | -10.969 | 0.0 |
| 224.9922 | 97.98 | -5.921 | 0.0 |
| 528.09098 | 160.022 | -5.956 | 1.0 |
| 213.68118 | 100.219 | -4.528 | 0.0 |
| 35.52608 | 121.349 | -18.721 | 0.0 |
| 213.68118 | 120.581 | -10.633 | 0.0 |
| 300.53832 | 139.017 | -13.319 | 0.0 |
| 138.81424 | 160.044 | -11.138 | 0.0 |
| 413.962 | 89.588 | -13.143 | 1.0 |
| 292.17914 | 188.543 | -5.389 | 0.0 |
| 419.34322 | 140.008 | -5.389 | 1.0 |
| 261.61587 | 152.951 | -12.447 | 0.0 |
| 168.64608 | 99.617 | -8.164 | 0.0 |
| 446.27546 | 196.005 | -6.621 | 1.0 |
| 343.92771 | 122.877 | -10.264 | 1.0 |
| 261.69424 | 145.129 | -4.733 | 0.0 |
| 408.47628 | 61.259 | -11.244 | 1.0 |
| 243.3824 | 161.778 | -6.88 | 0.0 |
| 184.00608 | 111.461 | -9.788 | 0.0 |
| 67.39546 | 188.723 | -3.239 | 0.0 |
| 165.51138 | 81.125 | -18.692 | 0.0 |
| 214.85669 | 144.174 | -17.989 | 0.0 |
| 185.96526 | 186.897 | -6.041 | 0.0 |
| 170.05669 | 75.356 | -16.486 | 0.0 |
| 193.72363 | 117.131 | -9.822 | 0.0 |
| 227.23873 | 91.953 | -11.221 | 0.0 |
| 177.6322 | 150.962 | -7.288 | 0.0 |
| 117.21098 | 100.021 | -10.782 | 0.0 |
| 422.42567 | 91.214 | -8.872 | 1.0 |
| 145.162 | 95.388 | -10.166 | 0.0 |
| 242.93832 | 112.015 | -7.135 | 0.0 |
| 366.00118 | 65.05 | -13.768 | 1.0 |
| 601.15546 | 70.623 | -11.179 | 1.0 |
| 196.17914 | 121.563 | -5.434 | 0.0 |
| 399.35955 | 124.963 | -3.415 | 1.0 |
| 287.73832 | 161.648 | -14.721 | 0.0 |
| 285.09995 | 132.86 | -10.23 | 0.0 |
| 358.32118 | 132.874 | -6.071 | 1.0 |
| 407.71873 | 85.996 | -10.103 | 1.0 |
| 463.25506 | 95.083 | -6.4 | 1.0 |
| 295.91465 | 93.515 | -8.987 | 0.0 |
| 273.8673 | 190.044 | -10.556 | 0.0 |
| 280.73751 | 113.363 | -6.893 | 0.0 |
| 142.65424 | 206.067 | -7.996 | 0.0 |
| 197.38077 | 84.023 | -10.964 | 0.0 |
| 226.24608 | 166.768 | -5.941 | 0.0 |
| 152.47628 | 153.528 | -7.393 | 0.0 |
| 315.32363 | 139.919 | -7.438 | 0.0 |
| 329.03791 | 180.059 | -6.473 | 1.0 |
| 296.88118 | 137.976 | -5.49 | 0.0 |
| 250.77506 | 84.543 | -18.058 | 0.0 |
| 48.14322 | 52.814 | -11.617 | 0.0 |
| 290.16771 | 141.24 | -14.009 | 0.0 |
| 406.04689 | 133.997 | -9.685 | 1.0 |
| 124.57751 | 161.237 | -12.314 | 0.0 |
| 280.45016 | 154.003 | -8.094 | 0.0 |
| 472.31955 | 219.371 | -6.08 | 1.0 |
| 267.4673 | 152.123 | -18.457 | 0.0 |
| 216.21506 | 88.022 | -7.096 | 0.0 |
| 31.00689 | 237.737 | -11.538 | 0.0 |
| 283.21914 | 132.934 | -11.153 | 0.0 |
| 284.21179 | 110.585 | -15.384 | 0.0 |
| 154.87955 | 157.881 | -4.377 | 0.0 |
| 431.46404 | 113.905 | -8.651 | 1.0 |
| 215.87546 | 124.572 | -7.67 | 0.0 |
| 161.43628 | 43.884 | -14.936 | 0.0 |
| 29.09995 | 135.146 | -1.837 | 0.0 |
| 301.68771 | 123.672 | -9.293 | 0.0 |
| 170.10893 | 124.022 | -9.589 | 0.0 |
| 281.96526 | 63.544 | -8.25 | 0.0 |
| 484.54485 | 149.565 | -5.501 | 1.0 |
| 135.1571 | 92.749 | -7.881 | 0.0 |
| 180.45342 | 137.899 | -5.246 | 0.0 |
| 142.44526 | 85.406 | -11.305 | 0.0 |
| 209.3971 | 93.233 | -13.079 | 0.0 |
| 319.9473 | 122.936 | -5.98 | 0.0 |
| 55.82322 | 249.325 | -8.656 | 0.0 |
| 192.88771 | 124.006 | -5.745 | 0.0 |
| 624.14322 | 128.988 | -7.872 | 1.0 |
| 188.49914 | 107.264 | -13.24 | 0.0 |
| 148.32281 | 150.075 | -5.793 | 0.0 |
| 195.00363 | 49.31 | -14.54 | 0.0 |
| 130.61179 | 100.208 | -10.487 | 0.0 |
| 212.11383 | 111.048 | -4.749 | 0.0 |
| 204.56444 | 92.076 | -7.906 | 0.0 |
| 292.04853 | 135.932 | -9.004 | 0.0 |
| 377.83465 | 73.336 | -13.457 | 1.0 |
| 246.96118 | 116.761 | -8.117 | 0.0 |
| 90.30485 | 39.3 | -15.444 | 0.0 |
| 201.92608 | 123.558 | -9.857 | 0.0 |
| 282.06975 | 136.09 | -6.202 | 0.0 |
| 194.55955 | 119.488 | -16.454 | 0.0 |
| 318.4322 | 140.467 | -6.375 | 0.0 |
| 55.45751 | 121.476 | -7.891 | 0.0 |
| 275.30404 | 90.024 | -6.191 | 0.0 |
| 422.24281 | 131.994 | -8.786 | 1.0 |
| 269.47873 | 88.921 | -4.419 | 0.0 |
| 186.01751 | 55.271 | -16.153 | 0.0 |
| 221.17832 | 94.967 | -6.591 | 0.0 |
| 275.66975 | 94.999 | -16.843 | 0.0 |
| 335.28118 | 90.129 | -13.33 | 1.0 |
| 288.88771 | 102.96 | -10.967 | 0.0 |
| 190.71955 | 88.269 | -13.167 | 0.0 |
| 211.17342 | 126.741 | -10.398 | 0.0 |
| 116.92363 | 140.09 | -7.578 | 0.0 |
| 310.282 | 181.753 | -17.61 | 0.0 |
| 184.21506 | 112.336 | -8.764 | 0.0 |
| 248.21506 | 101.988 | -6.487 | 0.0 |
| 116.00934 | 84.793 | -9.057 | 0.0 |
| 299.88526 | 143.66 | -7.279 | 0.0 |
| 1376.78322 | 107.066 | -7.893 | 1.0 |
| 182.85669 | 191.559 | -10.367 | 0.0 |
| 238.2624 | 48.592 | -9.687 | 0.0 |
| 239.5424 | 105.47 | -9.258 | 0.0 |
| 195.16036 | 127.911 | -7.054 | 0.0 |
| 149.73342 | 118.643 | -10.526 | 0.0 |
| 424.30649 | 62.8 | -22.35 | 1.0 |
| 221.77914 | 104.983 | -7.908 | 0.0 |
| 407.7971 | 126.983 | -11.675 | 1.0 |
| 175.04608 | 120.542 | -17.484 | 0.0 |
| 306.65098 | 120.0 | -18.973 | 0.0 |
| 315.0624 | 85.82 | -11.858 | 0.0 |
| 88.5024 | 211.429 | -11.543 | 0.0 |
| 101.61587 | 19.215 | -26.402 | 0.0 |
| 132.04853 | 186.123 | -7.21 | 0.0 |
| 146.25914 | 200.16 | -15.88 | 0.0 |
| 147.3824 | 104.658 | -8.836 | 0.0 |
| 156.26404 | 135.353 | -8.245 | 0.0 |
| 66.2722 | 115.129 | -16.416 | 0.0 |
| 247.43138 | 124.991 | -11.79 | 0.0 |
| 282.90567 | 115.472 | -12.448 | 0.0 |
| 401.05751 | 125.015 | -9.465 | 1.0 |
| 156.08118 | 88.128 | -16.916 | 0.0 |
| 198.08608 | 133.961 | -8.055 | 0.0 |
| 214.09914 | 84.353 | -12.991 | 0.0 |
| 269.73995 | 161.953 | -3.091 | 0.0 |
| 199.44444 | 169.922 | -6.241 | 0.0 |
| 149.65506 | 190.802 | -30.719 | 0.0 |
| 227.23873 | 120.14 | -12.39 | 0.0 |
To generate a scatter plot matrix, click on the plot button bellow the table and select scatter. That will transform your table to a scatter plot matrix. It automatically picks all numeric columns as values. To include predicted clusters, click on Plot Options and drag prediction to the list of Keys. You will get the following plot. On the diagonal panels you see the PDF of marginal distribution of each variable. Non-diagonal panels show a scatter plot between variables of the two variables of the row and column. For example the top right panel shows the scatter plot between duration and loudness. Each point is colored according to the cluster it is assigned to.
display(transformed.sample(false, fraction = 0.01)) // try fraction=1.0 as this dataset is small
| duration | tempo | loudness | prediction |
|---|---|---|---|
| 189.93587 | 134.957 | -12.934 | 0.0 |
| 206.44526 | 135.26 | -11.146 | 0.0 |
| 295.65342 | 137.904 | -7.308 | 0.0 |
| 158.1971 | 174.831 | -12.544 | 0.0 |
| 259.99628 | 171.094 | -12.057 | 0.0 |
| 277.002 | 88.145 | -6.039 | 0.0 |
| 1518.65424 | 65.003 | -19.182 | 1.0 |
| 121.73016 | 85.041 | -8.193 | 0.0 |
| 205.97506 | 106.952 | -6.572 | 0.0 |
| 206.18404 | 89.447 | -9.535 | 0.0 |
| 202.52689 | 134.13 | -6.535 | 0.0 |
| 259.02975 | 97.948 | -11.276 | 0.0 |
| 308.53179 | 80.017 | -12.277 | 0.0 |
| 151.71873 | 117.179 | -12.394 | 0.0 |
| 165.56363 | 99.187 | -11.414 | 0.0 |
| 370.83383 | 67.495 | -20.449 | 1.0 |
| 238.65424 | 100.071 | -9.276 | 0.0 |
| 163.7873 | 119.924 | -5.941 | 0.0 |
| 279.32689 | 67.371 | -26.67 | 0.0 |
| 353.85424 | 68.405 | -8.105 | 1.0 |
| 227.23873 | 117.62 | -7.874 | 0.0 |
| 195.49995 | 85.916 | -8.828 | 0.0 |
| 322.45506 | 135.826 | -5.034 | 0.0 |
| 235.88526 | 109.773 | -10.919 | 0.0 |
| 235.15383 | 163.924 | -9.666 | 0.0 |
| 253.67465 | 125.014 | -7.171 | 0.0 |
| 415.03302 | 71.991 | -15.224 | 1.0 |
| 190.85016 | 152.805 | -2.871 | 0.0 |
| 290.76853 | 84.841 | -6.161 | 0.0 |
| 346.8273 | 129.981 | -10.377 | 1.0 |
| 174.70649 | 143.446 | -11.994 | 0.0 |
| 170.84036 | 70.033 | -7.782 | 0.0 |
| 517.66812 | 151.948 | -12.363 | 1.0 |
| 62.06649 | 154.406 | -9.387 | 0.0 |
| 505.96526 | 90.59 | -19.848 | 1.0 |
| 480.86159 | 0.0 | -11.707 | 1.0 |
| 330.13506 | 204.534 | -9.85 | 1.0 |
| 223.99955 | 110.3 | -12.339 | 0.0 |
| 200.98567 | 89.215 | -3.858 | 0.0 |
| 158.09261 | 178.826 | -13.681 | 0.0 |
| 186.46159 | 110.102 | -16.477 | 0.0 |
| 399.46404 | 125.03 | -10.786 | 1.0 |
| 118.64771 | 85.747 | -34.461 | 0.0 |
| 352.49587 | 91.139 | -9.13 | 1.0 |
| 150.15138 | 106.364 | -7.337 | 0.0 |
| 158.98077 | 124.54 | -17.405 | 0.0 |
| 122.17424 | 170.018 | -9.047 | 0.0 |
| 299.38893 | 91.976 | -6.266 | 0.0 |
| 197.95546 | 142.744 | -8.189 | 0.0 |
| 332.38159 | 89.071 | -5.08 | 1.0 |
| 309.78567 | 136.05 | -15.105 | 0.0 |
| 198.47791 | 82.546 | -12.594 | 0.0 |
| 254.17098 | 144.01 | -9.0 | 0.0 |
| 155.81995 | 233.022 | -8.978 | 0.0 |
| 176.53506 | 136.255 | -9.675 | 0.0 |
| 331.54567 | 221.511 | -13.778 | 1.0 |
| 140.72118 | 162.44 | -10.654 | 0.0 |
| 210.9122 | 120.039 | -5.391 | 0.0 |
| 197.27628 | 119.396 | -22.599 | 0.0 |
| 339.25179 | 96.842 | -16.207 | 1.0 |
| 355.97016 | 142.777 | -5.118 | 1.0 |
| 170.23955 | 55.107 | -17.654 | 0.0 |
| 360.01914 | 119.997 | -7.53 | 1.0 |
| 241.76281 | 101.938 | -3.765 | 0.0 |
| 84.92363 | 236.863 | -15.846 | 0.0 |
| 361.09016 | 120.121 | -5.861 | 1.0 |
| 262.37342 | 89.924 | -4.819 | 0.0 |
| 225.74975 | 139.994 | -11.505 | 0.0 |
| 246.9873 | 172.147 | -16.273 | 0.0 |
| 159.242 | 166.855 | -4.771 | 0.0 |
| 192.86159 | 148.297 | -13.423 | 0.0 |
| 140.90404 | 92.602 | -12.604 | 0.0 |
| 224.20853 | 71.879 | -16.615 | 0.0 |
| 235.78077 | 180.366 | -3.918 | 0.0 |
| 244.45342 | 105.077 | -5.004 | 0.0 |
| 210.57261 | 200.044 | -11.381 | 0.0 |
| 327.3922 | 112.023 | -11.341 | 1.0 |
| 168.38485 | 99.991 | -4.1 | 0.0 |
| 287.13751 | 137.154 | -12.948 | 0.0 |
| 148.63628 | 121.773 | -11.432 | 0.0 |
| 214.7522 | 124.437 | -5.834 | 0.0 |
| 197.43302 | 92.959 | -4.948 | 0.0 |
| 150.17751 | 112.0 | -5.968 | 0.0 |
| 152.86812 | 120.884 | -15.693 | 0.0 |
| 173.322 | 120.827 | -7.96 | 0.0 |
| 235.15383 | 85.519 | -8.172 | 0.0 |
| 299.41506 | 94.68 | -15.486 | 0.0 |
| 222.35383 | 156.046 | -8.885 | 0.0 |
| 235.85914 | 130.055 | -5.341 | 0.0 |
| 276.71465 | 170.059 | -2.926 | 0.0 |
| 136.07138 | 168.895 | -6.971 | 0.0 |
| 205.7922 | 129.57 | -6.113 | 0.0 |
| 268.72118 | 126.894 | -7.142 | 0.0 |
| 390.63465 | 232.08 | -5.493 | 1.0 |
| 242.59873 | 124.034 | -11.24 | 0.0 |
| 274.80771 | 108.178 | -21.456 | 0.0 |
| 157.25669 | 151.108 | -11.678 | 0.0 |
| 203.67628 | 140.02 | -5.939 | 0.0 |
| 302.47138 | 148.2 | -10.428 | 0.0 |
| 292.64934 | 106.006 | -3.538 | 0.0 |
| 245.002 | 135.997 | -10.6 | 0.0 |
| 181.57669 | 132.89 | -5.429 | 0.0 |
| 36.38812 | 65.132 | -12.621 | 0.0 |
| 656.14322 | 95.469 | -8.588 | 1.0 |
| 262.50404 | 88.025 | -4.359 | 0.0 |
| 261.95546 | 117.93 | -5.5 | 0.0 |
| 337.57995 | 122.08 | -6.658 | 1.0 |
| 373.08036 | 126.846 | -10.115 | 1.0 |
| 220.36853 | 157.934 | -13.192 | 0.0 |
| 357.22404 | 199.889 | -9.832 | 1.0 |
| 663.61424 | 143.681 | -9.066 | 1.0 |
| 155.08853 | 201.152 | -7.43 | 0.0 |
| 264.56771 | 133.7 | -11.767 | 0.0 |
| 262.53016 | 105.041 | -3.933 | 0.0 |
| 386.61179 | 132.644 | -8.34 | 1.0 |
| 224.41751 | 93.333 | -12.001 | 0.0 |
| 264.25424 | 117.547 | -6.134 | 0.0 |
| 12.82567 | 99.229 | -29.527 | 0.0 |
| 470.25587 | 135.99 | -7.403 | 1.0 |
| 213.7073 | 92.584 | -8.398 | 0.0 |
| 50.59873 | 108.989 | -10.006 | 0.0 |
| 273.162 | 114.014 | -8.527 | 0.0 |
| 285.1522 | 101.877 | -9.361 | 0.0 |
| 267.88526 | 123.903 | -10.177 | 0.0 |
| 334.54975 | 138.386 | -7.389 | 1.0 |
| 174.0273 | 95.084 | -21.944 | 0.0 |
| 242.15465 | 87.196 | -9.749 | 0.0 |
| 188.96934 | 188.967 | -3.288 | 0.0 |
| 480.67873 | 127.989 | -7.243 | 1.0 |
| 182.54322 | 72.894 | -17.837 | 0.0 |
| 231.1571 | 126.959 | -5.632 | 0.0 |
| 105.37751 | 136.156 | -13.607 | 0.0 |
| 125.88363 | 93.022 | -13.496 | 0.0 |
| 216.99873 | 151.866 | -7.47 | 0.0 |
| 176.50893 | 142.601 | -3.881 | 0.0 |
| 245.57669 | 123.186 | -3.609 | 0.0 |
| 191.37261 | 145.008 | -14.3 | 0.0 |
| 62.11873 | 80.961 | -12.453 | 0.0 |
| 274.80771 | 83.505 | -10.032 | 0.0 |
| 148.29669 | 92.154 | -13.542 | 0.0 |
| 241.47546 | 115.886 | -11.948 | 0.0 |
| 250.40934 | 100.001 | -18.335 | 0.0 |
| 154.56608 | 96.464 | -12.133 | 0.0 |
| 485.14567 | 84.633 | -8.812 | 1.0 |
| 307.22567 | 63.498 | -9.047 | 0.0 |
| 167.78404 | 122.92 | -13.528 | 0.0 |
| 345.10322 | 130.003 | -7.858 | 1.0 |
| 178.9122 | 111.13 | -7.33 | 0.0 |
| 237.29587 | 111.244 | -15.209 | 0.0 |
| 230.16444 | 74.335 | -12.244 | 0.0 |
| 230.50404 | 98.933 | -12.616 | 0.0 |
| 195.16036 | 131.037 | -12.769 | 0.0 |
| 130.01098 | 99.176 | -11.082 | 0.0 |
| 150.02077 | 97.501 | -2.945 | 0.0 |
| 239.64689 | 89.99 | -9.224 | 0.0 |
| 91.6371 | 71.187 | -16.523 | 0.0 |
| 136.88118 | 121.19 | -12.181 | 0.0 |
| 265.29914 | 167.132 | -13.859 | 0.0 |
| 166.84363 | 76.298 | -7.692 | 0.0 |
| 34.79465 | 109.386 | -13.591 | 0.0 |
| 344.29342 | 138.826 | -12.746 | 1.0 |
| 294.71302 | 123.963 | -15.225 | 0.0 |
| 232.9073 | 100.264 | -7.171 | 0.0 |
| 501.68118 | 126.947 | -8.972 | 1.0 |
| 163.00363 | 154.296 | -19.387 | 0.0 |
| 186.51383 | 89.989 | -5.338 | 0.0 |
| 279.30077 | 130.522 | -11.813 | 0.0 |
| 202.78812 | 79.515 | -26.44 | 0.0 |
| 123.45424 | 129.987 | -14.751 | 0.0 |
| 237.08689 | 82.135 | -6.347 | 0.0 |
| 239.75138 | 169.111 | -7.915 | 0.0 |
| 105.87383 | 145.028 | -2.414 | 0.0 |
| 250.46159 | 123.306 | -5.113 | 0.0 |
| 227.68281 | 132.51 | -6.661 | 0.0 |
| 487.44444 | 100.065 | -16.668 | 1.0 |
| 247.17016 | 165.886 | -8.476 | 0.0 |
| 194.92526 | 99.414 | -7.516 | 0.0 |
| 174.602 | 194.079 | -4.611 | 0.0 |
| 199.96689 | 148.78 | -9.657 | 0.0 |
| 278.7522 | 185.291 | -10.58 | 0.0 |
| 147.82649 | 88.634 | -3.83 | 0.0 |
| 166.1122 | 106.748 | -10.875 | 0.0 |
| 150.67383 | 120.367 | -14.628 | 0.0 |
| 237.53098 | 127.24 | -27.291 | 0.0 |
| 174.10567 | 124.185 | -6.983 | 0.0 |
| 84.84526 | 98.321 | -4.167 | 0.0 |
| 287.50322 | 121.821 | -9.951 | 0.0 |
| 142.23628 | 184.994 | -16.834 | 0.0 |
| 85.91628 | 180.788 | -9.972 | 0.0 |
| 217.23383 | 108.892 | -7.211 | 0.0 |
| 233.63873 | 113.878 | -6.394 | 0.0 |
| 291.94404 | 91.976 | -6.998 | 0.0 |
| 502.5171 | 140.015 | -5.285 | 1.0 |
| 316.02893 | 97.508 | -10.52 | 0.0 |
| 408.58077 | 176.977 | -15.701 | 1.0 |
| 244.13995 | 156.187 | -5.985 | 0.0 |
| 146.46812 | 140.032 | -13.347 | 0.0 |
| 131.05587 | 114.198 | -23.084 | 0.0 |
| 175.80363 | 137.052 | -7.147 | 0.0 |
| 230.53016 | 115.496 | -3.953 | 0.0 |
| 247.92771 | 90.092 | -10.103 | 0.0 |
| 207.04608 | 132.94 | -7.325 | 0.0 |
| 163.70893 | 77.698 | -18.806 | 0.0 |
| 179.722 | 81.884 | -40.036 | 0.0 |
| 214.02077 | 87.003 | -4.724 | 0.0 |
| 379.68934 | 127.986 | -8.715 | 1.0 |
| 173.322 | 74.929 | -3.089 | 0.0 |
| 263.81016 | 131.945 | -6.911 | 0.0 |
| 244.21832 | 109.004 | -3.762 | 0.0 |
| 220.1073 | 99.948 | -14.54 | 0.0 |
| 298.37016 | 87.591 | -31.42 | 0.0 |
| 273.97179 | 169.372 | -10.764 | 0.0 |
| 229.74649 | 117.184 | -6.174 | 0.0 |
| 383.68608 | 127.261 | -3.975 | 1.0 |
| 278.02077 | 47.779 | -4.486 | 0.0 |
| 135.75791 | 164.977 | -5.974 | 0.0 |
| 373.75955 | 129.057 | -8.391 | 1.0 |
| 272.40444 | 162.882 | -14.916 | 0.0 |
| 164.93669 | 120.317 | -11.476 | 0.0 |
| 242.99057 | 103.45 | -16.493 | 0.0 |
| 248.24118 | 116.026 | -6.973 | 0.0 |
| 304.61342 | 212.051 | -17.131 | 0.0 |
| 228.91057 | 91.879 | -23.314 | 0.0 |
| 308.50567 | 118.188 | -7.159 | 0.0 |
| 419.83955 | 125.007 | -11.017 | 1.0 |
| 189.67465 | 193.129 | -8.674 | 0.0 |
| 242.28526 | 69.129 | -16.121 | 0.0 |
| 213.02812 | 126.919 | -8.439 | 0.0 |
| 290.16771 | 135.96 | -7.305 | 0.0 |
| 143.12444 | 100.706 | -12.15 | 0.0 |
| 278.22975 | 103.227 | -7.35 | 0.0 |
| 260.30975 | 190.015 | -5.148 | 0.0 |
| 112.53506 | 128.054 | -18.969 | 0.0 |
| 248.78975 | 102.113 | -7.149 | 0.0 |
| 178.33751 | 147.659 | -3.494 | 0.0 |
| 242.83383 | 72.278 | -11.75 | 0.0 |
| 226.97751 | 100.012 | -4.935 | 0.0 |
| 172.61669 | 102.747 | -9.067 | 0.0 |
| 188.96934 | 110.208 | -7.915 | 0.0 |
| 129.35791 | 183.983 | -7.223 | 0.0 |
| 169.45587 | 91.872 | -5.507 | 0.0 |
| 57.67791 | 104.328 | -9.213 | 0.0 |
| 294.47791 | 107.29 | -6.285 | 0.0 |
| 372.79302 | 124.018 | -8.312 | 1.0 |
| 226.82077 | 132.001 | -5.636 | 0.0 |
| 171.25832 | 111.129 | -10.311 | 0.0 |
| 130.61179 | 204.936 | -9.571 | 0.0 |
| 123.48036 | 171.624 | -7.069 | 0.0 |
| 230.86975 | 154.048 | -4.541 | 0.0 |
| 181.26322 | 120.07 | -3.623 | 0.0 |
| 102.79138 | 113.374 | -13.804 | 0.0 |
| 385.64526 | 116.331 | -6.748 | 1.0 |
| 193.69751 | 112.542 | -4.375 | 0.0 |
| 249.20771 | 146.99 | -7.306 | 0.0 |
| 172.64281 | 119.993 | -9.333 | 0.0 |
| 152.45016 | 95.981 | -12.144 | 0.0 |
| 142.75873 | 86.66 | -14.115 | 0.0 |
| 413.20444 | 123.564 | -10.417 | 1.0 |
| 208.87465 | 134.331 | -14.099 | 0.0 |
| 200.51546 | 165.825 | -12.445 | 0.0 |
| 337.6322 | 115.649 | -11.181 | 1.0 |
| 222.9024 | 75.527 | -15.198 | 0.0 |
| 221.77914 | 104.983 | -7.908 | 0.0 |
| 262.922 | 156.13 | -13.043 | 0.0 |
| 206.602 | 236.05 | -3.612 | 0.0 |
| 277.9424 | 147.967 | -8.768 | 0.0 |
| 139.4673 | 159.421 | -18.781 | 0.0 |
| 167.73179 | 161.62 | -11.271 | 0.0 |
| 205.71383 | 190.11 | -3.171 | 0.0 |
| 308.55791 | 92.008 | -11.944 | 0.0 |
| 184.89424 | 111.551 | -11.777 | 0.0 |
| 162.42893 | 83.324 | -15.155 | 0.0 |
| 150.04689 | 119.14 | -23.358 | 0.0 |
| 217.15546 | 128.463 | -3.67 | 0.0 |
| 156.49914 | 155.053 | -3.135 | 0.0 |
| 720.74404 | 113.527 | -11.96 | 1.0 |
| 429.84444 | 146.456 | -10.661 | 1.0 |
| 258.61179 | 92.829 | -8.928 | 0.0 |
| 210.31138 | 86.073 | -16.77 | 0.0 |
| 263.02649 | 98.531 | -27.825 | 0.0 |
| 167.49669 | 90.052 | -10.736 | 0.0 |
| 358.08608 | 63.867 | -12.322 | 1.0 |
| 326.19057 | 140.026 | -4.9 | 1.0 |
| 418.42893 | 85.351 | -7.587 | 1.0 |
| 192.13016 | 147.428 | -8.522 | 0.0 |
| 180.61016 | 89.582 | -12.25 | 0.0 |
| 194.21995 | 137.415 | -21.924 | 0.0 |
| 194.5073 | 169.757 | -10.502 | 0.0 |
| 260.64934 | 193.875 | -7.698 | 0.0 |
| 246.38649 | 142.081 | -7.517 | 0.0 |
| 237.40036 | 196.665 | -9.895 | 0.0 |
| 183.71873 | 124.706 | -28.112 | 0.0 |
| 125.64853 | 94.467 | -4.691 | 0.0 |
| 217.0771 | 98.966 | -4.5 | 0.0 |
| 297.22077 | 107.963 | -16.542 | 0.0 |
| 153.57342 | 93.931 | -15.579 | 0.0 |
| 258.35057 | 145.774 | -7.775 | 0.0 |
| 213.13261 | 86.633 | -7.306 | 0.0 |
| 215.66649 | 111.961 | -5.529 | 0.0 |
| 84.4273 | 74.848 | -22.231 | 0.0 |
| 215.87546 | 105.793 | -8.988 | 0.0 |
| 165.48526 | 127.001 | -13.785 | 0.0 |
| 399.33342 | 161.057 | -3.206 | 1.0 |
| 367.93424 | 119.991 | -6.123 | 1.0 |
| 313.44281 | 129.342 | -10.202 | 0.0 |
| 295.67955 | 114.641 | -9.913 | 0.0 |
| 196.5971 | 96.798 | -14.001 | 0.0 |
| 191.4771 | 100.248 | -5.114 | 0.0 |
| 313.44281 | 102.699 | -7.444 | 0.0 |
| 205.11302 | 93.238 | -6.034 | 0.0 |
| 81.42322 | 207.494 | -24.804 | 0.0 |
| 188.78649 | 194.439 | -9.626 | 0.0 |
| 308.24444 | 145.975 | -6.359 | 0.0 |
| 411.92444 | 237.466 | -9.633 | 1.0 |
| 256.86159 | 131.837 | -11.147 | 0.0 |
| 38.45179 | 73.214 | -14.797 | 0.0 |
| 264.77669 | 130.674 | -4.721 | 0.0 |
| 212.4273 | 116.029 | -2.106 | 0.0 |
displayHTML(frameIt("https://en.wikipedia.org/wiki/Euclidean_space",500)) // make sure you run the cell with first wikipedia article!
Do you see the problem in our clusters based on the plot?
As you can see there is very little "separation" (in the sense of being separable into two point clouds, that represent our two identifed clusters, such that they have minimal overlay of these two features, i.e. tempo and loudness. NOTE that this sense of "pairwise separation" is a 2D projection of all three features in 3D Euclidean Space, i.e. loudness, tempo and duration, that depends directly on their two-dimensional visually sense-making projection of perhaps two important song features, as depicted in the corresponding 2D-scatter-plot of tempo versus loudness within the 2D scatter plot matrix that is helping us to partially visualize in the 2D-plane all of the three features in the three dimensional real-valued feature space that was the input to our K-Means algorithm) between loudness, and tempo and generated clusters. To see that, focus on the panels in the first and second columns of the scatter plot matrix. For varying values of loudness and tempo prediction does not change. Instead, duration of a song alone predicts what cluster it belongs to. Why is that?
To see the reason, let's take a look at the marginal distribution of duration in the next cell.
To produce this plot, we have picked histogram from the plots menu and in Plot Options we chose prediction as key and duration as value. The histogram plot shows significant skew in duration. Basically there are a few very long songs. These data points have large leverage on the mean function (what KMeans uses for clustering).
display(transformed.sample(false, fraction = 1.0).select("duration", "prediction")) // plotting over all results
| duration | prediction |
|---|---|
| 213.7073 | 0.0 |
| 133.25016 | 0.0 |
| 247.32689 | 0.0 |
| 337.05751 | 1.0 |
| 430.23628 | 1.0 |
| 186.80118 | 0.0 |
| 361.89995 | 1.0 |
| 220.00281 | 0.0 |
| 156.86485 | 0.0 |
| 256.67873 | 0.0 |
| 204.64281 | 0.0 |
| 112.48281 | 0.0 |
| 170.39628 | 0.0 |
| 215.95383 | 0.0 |
| 303.62077 | 0.0 |
| 266.60526 | 0.0 |
| 326.19057 | 1.0 |
| 51.04281 | 0.0 |
| 129.4624 | 0.0 |
| 253.33506 | 0.0 |
| 237.76608 | 0.0 |
| 132.96281 | 0.0 |
| 399.35955 | 1.0 |
| 168.75057 | 0.0 |
| 396.042 | 1.0 |
| 192.10404 | 0.0 |
| 12.2771 | 0.0 |
| 367.56853 | 1.0 |
| 189.93587 | 0.0 |
| 233.50812 | 0.0 |
| 462.68036 | 1.0 |
| 202.60526 | 0.0 |
| 241.52771 | 0.0 |
| 275.64363 | 0.0 |
| 350.69342 | 1.0 |
| 166.55628 | 0.0 |
| 249.49506 | 0.0 |
| 53.86404 | 0.0 |
| 233.76934 | 0.0 |
| 275.12118 | 0.0 |
| 191.13751 | 0.0 |
| 299.07546 | 0.0 |
| 468.74077 | 1.0 |
| 110.34077 | 0.0 |
| 234.78812 | 0.0 |
| 705.25342 | 1.0 |
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| 194.48118 | 0.0 |
| 241.65832 | 0.0 |
| 245.15873 | 0.0 |
| 262.66077 | 0.0 |
| 307.46077 | 0.0 |
| 295.20934 | 0.0 |
| 259.52608 | 0.0 |
| 347.19302 | 1.0 |
| 206.91546 | 0.0 |
| 399.51628 | 1.0 |
| 271.25506 | 0.0 |
| 172.7473 | 0.0 |
| 231.65342 | 0.0 |
| 208.1171 | 0.0 |
| 195.76118 | 0.0 |
| 723.27791 | 1.0 |
| 282.95791 | 0.0 |
| 153.12934 | 0.0 |
| 207.15057 | 0.0 |
| 174.41914 | 0.0 |
| 269.29587 | 0.0 |
| 275.3824 | 0.0 |
| 149.41995 | 0.0 |
| 108.35546 | 0.0 |
| 243.69587 | 0.0 |
| 308.27057 | 0.0 |
| 204.90404 | 0.0 |
| 311.24853 | 0.0 |
| 164.77995 | 0.0 |
| 449.51465 | 1.0 |
| 140.93016 | 0.0 |
| 165.22404 | 0.0 |
| 53.26322 | 0.0 |
| 218.80118 | 0.0 |
| 300.85179 | 0.0 |
| 388.75383 | 1.0 |
| 150.77832 | 0.0 |
| 293.11955 | 0.0 |
| 177.71057 | 0.0 |
| 184.11057 | 0.0 |
| 225.17506 | 0.0 |
| 272.19546 | 0.0 |
| 157.67465 | 0.0 |
| 204.61669 | 0.0 |
| 93.98812 | 0.0 |
| 204.45995 | 0.0 |
| 307.1473 | 0.0 |
| 347.0624 | 1.0 |
| 184.73751 | 0.0 |
| 146.65098 | 0.0 |
| 513.90649 | 1.0 |
| 293.85098 | 0.0 |
| 121.73016 | 0.0 |
| 86.72608 | 0.0 |
| 171.25832 | 0.0 |
| 264.95955 | 0.0 |
| 411.68934 | 1.0 |
| 190.79791 | 0.0 |
| 159.65995 | 0.0 |
| 162.89914 | 0.0 |
| 205.97506 | 0.0 |
| 204.59057 | 0.0 |
| 117.02812 | 0.0 |
| 135.28771 | 0.0 |
| 163.65669 | 0.0 |
| 254.95465 | 0.0 |
| 178.31138 | 0.0 |
| 150.77832 | 0.0 |
| 410.53995 | 1.0 |
| 222.30159 | 0.0 |
| 314.74893 | 0.0 |
| 233.11628 | 0.0 |
| 226.21995 | 0.0 |
| 441.67791 | 1.0 |
| 120.99873 | 0.0 |
| 157.75302 | 0.0 |
| 203.65016 | 0.0 |
| 287.73832 | 0.0 |
| 226.7424 | 0.0 |
| 69.56363 | 0.0 |
| 174.52363 | 0.0 |
| 363.67628 | 1.0 |
| 136.48934 | 0.0 |
| 390.60853 | 1.0 |
| 284.60363 | 0.0 |
| 291.81342 | 0.0 |
| 502.7522 | 1.0 |
| 197.27628 | 0.0 |
| 329.53424 | 1.0 |
| 340.1922 | 1.0 |
| 170.94485 | 0.0 |
| 113.57995 | 0.0 |
| 205.24363 | 0.0 |
| 169.22077 | 0.0 |
| 285.70077 | 0.0 |
| 221.23057 | 0.0 |
| 310.38649 | 0.0 |
| 353.48853 | 1.0 |
| 415.92118 | 1.0 |
| 150.59546 | 0.0 |
| 236.90404 | 0.0 |
| 227.42159 | 0.0 |
| 229.8771 | 0.0 |
| 359.3922 | 1.0 |
| 403.17342 | 1.0 |
| 296.59383 | 0.0 |
| 117.65506 | 0.0 |
| 241.3971 | 0.0 |
| 34.92526 | 0.0 |
| 188.31628 | 0.0 |
| 409.02485 | 1.0 |
| 335.5424 | 1.0 |
| 354.63791 | 1.0 |
| 213.31546 | 0.0 |
| 238.62812 | 0.0 |
| 193.33179 | 0.0 |
| 225.33179 | 0.0 |
| 166.84363 | 0.0 |
| 79.96036 | 0.0 |
| 158.69342 | 0.0 |
| 176.53506 | 0.0 |
| 347.61098 | 1.0 |
| 106.39628 | 0.0 |
| 147.93098 | 0.0 |
| 446.92853 | 1.0 |
| 360.22812 | 1.0 |
| 214.56934 | 0.0 |
| 325.35465 | 1.0 |
| 413.23057 | 1.0 |
| 218.04363 | 0.0 |
| 215.30077 | 0.0 |
| 57.44281 | 0.0 |
| 247.48363 | 0.0 |
| 793.25995 | 1.0 |
| 467.3824 | 1.0 |
| 327.00036 | 1.0 |
| 232.72444 | 0.0 |
| 251.68934 | 0.0 |
| 197.3024 | 0.0 |
| 193.88036 | 0.0 |
| 383.32036 | 1.0 |
| 269.71383 | 0.0 |
| 255.05914 | 0.0 |
| 337.18812 | 1.0 |
| 240.92689 | 0.0 |
| 206.18404 | 0.0 |
| 143.22893 | 0.0 |
| 244.27057 | 0.0 |
| 83.56526 | 0.0 |
| 428.40771 | 1.0 |
| 261.11955 | 0.0 |
| 208.37832 | 0.0 |
| 369.78893 | 1.0 |
| 47.17669 | 0.0 |
| 239.3073 | 0.0 |
| 17.37098 | 0.0 |
| 257.04444 | 0.0 |
| 198.63465 | 0.0 |
| 208.40444 | 0.0 |
| 338.28526 | 1.0 |
| 175.15057 | 0.0 |
| 234.97098 | 0.0 |
| 275.06893 | 0.0 |
| 186.46159 | 0.0 |
| 201.74322 | 0.0 |
| 237.58322 | 0.0 |
| 219.402 | 0.0 |
| 461.29587 | 1.0 |
| 196.67546 | 0.0 |
| 290.63791 | 0.0 |
| 328.22812 | 1.0 |
| 260.64934 | 0.0 |
| 245.83791 | 0.0 |
| 97.54077 | 0.0 |
| 248.0322 | 0.0 |
| 175.33342 | 0.0 |
| 199.57506 | 0.0 |
| 229.45914 | 0.0 |
| 902.26893 | 1.0 |
| 271.12444 | 0.0 |
| 211.17342 | 0.0 |
| 179.3824 | 0.0 |
| 156.96934 | 0.0 |
| 281.0771 | 0.0 |
| 291.97016 | 0.0 |
| 392.85506 | 1.0 |
| 223.00689 | 0.0 |
| 269.94893 | 0.0 |
| 36.64934 | 0.0 |
| 309.26322 | 0.0 |
| 178.41587 | 0.0 |
| 206.75873 | 0.0 |
| 155.68934 | 0.0 |
| 254.71955 | 0.0 |
| 133.11955 | 0.0 |
| 260.362 | 0.0 |
| 135.28771 | 0.0 |
| 158.27546 | 0.0 |
| 154.93179 | 0.0 |
| 205.84444 | 0.0 |
| 276.21832 | 0.0 |
| 193.61914 | 0.0 |
| 153.73016 | 0.0 |
| 389.11955 | 1.0 |
| 195.23873 | 0.0 |
| 210.72934 | 0.0 |
| 336.06485 | 1.0 |
| 263.02649 | 0.0 |
| 230.26893 | 0.0 |
| 40.6722 | 0.0 |
| 255.92118 | 0.0 |
| 305.60608 | 0.0 |
| 177.8673 | 0.0 |
| 361.11628 | 1.0 |
| 357.66812 | 1.0 |
| 196.49261 | 0.0 |
| 218.40934 | 0.0 |
| 91.58485 | 0.0 |
| 185.25995 | 0.0 |
| 282.80118 | 0.0 |
| 244.68853 | 0.0 |
| 215.40526 | 0.0 |
| 211.19955 | 0.0 |
| 327.75791 | 1.0 |
| 510.40608 | 1.0 |
| 212.74077 | 0.0 |
| 120.86812 | 0.0 |
| 507.08853 | 1.0 |
| 265.11628 | 0.0 |
| 183.06567 | 0.0 |
| 199.54893 | 0.0 |
| 41.92608 | 0.0 |
| 164.75383 | 0.0 |
| 267.33669 | 0.0 |
| 208.74404 | 0.0 |
| 253.09995 | 0.0 |
| 244.50567 | 0.0 |
| 195.73506 | 0.0 |
| 160.07791 | 0.0 |
| 327.70567 | 1.0 |
| 174.86322 | 0.0 |
| 272.92689 | 0.0 |
| 251.53261 | 0.0 |
| 216.99873 | 0.0 |
| 195.3171 | 0.0 |
| 247.11791 | 0.0 |
| 101.3024 | 0.0 |
| 315.97669 | 0.0 |
| 449.67138 | 1.0 |
| 173.16526 | 0.0 |
| 394.44853 | 1.0 |
| 226.69016 | 0.0 |
| 219.11465 | 0.0 |
| 240.92689 | 0.0 |
| 227.91791 | 0.0 |
| 119.84934 | 0.0 |
| 109.92281 | 0.0 |
| 116.08771 | 0.0 |
| 187.71546 | 0.0 |
| 191.65995 | 0.0 |
| 116.32281 | 0.0 |
| 482.45506 | 1.0 |
| 262.71302 | 0.0 |
| 208.97914 | 0.0 |
| 209.81506 | 0.0 |
| 129.85424 | 0.0 |
| 219.0624 | 0.0 |
| 500.4273 | 1.0 |
| 224.7571 | 0.0 |
| 274.85995 | 0.0 |
| 145.162 | 0.0 |
| 211.27791 | 0.0 |
| 167.49669 | 0.0 |
| 415.7122 | 1.0 |
| 346.33098 | 1.0 |
| 399.69914 | 1.0 |
| 136.56771 | 0.0 |
There are known techniques for dealing with skewed features. A simple technique is applying a power transformation. We are going to use the simplest and most common power transformation: logarithm.
In following cell we repeat the clustering experiment with a transformed DataFrame that includes a new column called log_duration.
val df = table("songsTable").selectExpr("tempo", "loudness", "log(duration) as log_duration")
val trainingData2 = new VectorAssembler().
setInputCols(Array("log_duration", "tempo", "loudness")).
setOutputCol("features").
transform(df)
val model2 = new KMeans().setK(2).fit(trainingData2)
val transformed2 = model2.transform(trainingData2).select("log_duration", "tempo", "loudness", "prediction")
display(transformed2.sample(false, fraction = 0.1))
| log_duration | tempo | loudness | prediction |
|---|---|---|---|
| 5.510710902946994 | 171.278 | -4.896 | 0.0 |
| 5.989862138937489 | 120.959 | -18.853 | 1.0 |
| 5.128421707524712 | 149.709 | -9.086 | 0.0 |
| 5.4869842077111874 | 170.281 | -10.174 | 0.0 |
| 5.617211655152324 | 121.983 | -5.784 | 1.0 |
| 6.15004988642409 | 137.925 | -7.233 | 0.0 |
| 6.078741524239438 | 106.342 | -8.283 | 1.0 |
| 5.630042029827832 | 114.493 | -5.989 | 1.0 |
| 5.2087027312927825 | 169.892 | -11.187 | 0.0 |
| 5.492162232587351 | 84.753 | -6.803 | 1.0 |
| 5.748033527231058 | 160.362 | -10.409 | 0.0 |
| 6.192813859984513 | 128.003 | -9.91 | 1.0 |
| 4.002817550537711 | 246.365 | -8.539 | 0.0 |
| 3.855006628559293 | 109.798 | -9.377 | 1.0 |
| 6.115636993601915 | 137.939 | -13.916 | 0.0 |
| 5.550772233170746 | 160.201 | -10.988 | 0.0 |
| 6.0606239613925785 | 149.834 | -11.885 | 0.0 |
| 5.3567535248358675 | 132.017 | -6.202 | 0.0 |
| 5.233952750288136 | 91.973 | -3.627 | 1.0 |
| 5.388640906431825 | 177.04 | -7.881 | 0.0 |
| 5.1442402340161095 | 154.258 | -6.563 | 0.0 |
| 5.336216341205094 | 112.48 | -6.547 | 1.0 |
| 5.1587617503861605 | 104.546 | -17.661 | 1.0 |
| 5.428297623848528 | 121.984 | -7.489 | 1.0 |
| 5.612071234226773 | 213.981 | -13.702 | 0.0 |
| 5.280491882415535 | 35.359 | -22.812 | 1.0 |
| 6.3218768342692515 | 95.209 | -17.88 | 1.0 |
| 5.451425331508477 | 81.996 | -5.243 | 1.0 |
| 5.322669238165966 | 178.637 | -5.65 | 0.0 |
| 5.879897616998539 | 80.011 | -20.412 | 1.0 |
| 4.557517519622661 | 73.125 | -26.798 | 1.0 |
| 4.943992088552383 | 180.034 | -8.643 | 0.0 |
| 5.7347838604690065 | 71.782 | -21.615 | 1.0 |
| 5.56086826744402 | 151.315 | -18.856 | 0.0 |
| 4.938017431690172 | 106.593 | -11.141 | 1.0 |
| 4.559159964195757 | 83.994 | -5.297 | 1.0 |
| 5.752687806796722 | 90.654 | -7.9 | 1.0 |
| 6.06451668527941 | 127.987 | -6.943 | 1.0 |
| 5.347719391111762 | 191.267 | -9.878 | 0.0 |
| 5.444452738015107 | 195.744 | -13.774 | 0.0 |
| 6.175688106447764 | 140.039 | -8.091 | 0.0 |
| 6.019697811017462 | 162.481 | -4.921 | 0.0 |
| 5.528506876691428 | 88.953 | -7.371 | 1.0 |
| 5.212267799598076 | 126.594 | -11.339 | 1.0 |
| 6.075264280620769 | 135.875 | -7.215 | 0.0 |
| 5.029575588906314 | 174.223 | -10.233 | 0.0 |
| 5.199229414941118 | 177.536 | -6.373 | 0.0 |
| 5.3263590476693095 | 93.956 | -6.288 | 1.0 |
| 5.981915928807521 | 125.981 | -15.151 | 1.0 |
| 5.498169527101874 | 236.083 | -15.563 | 0.0 |
| 5.198075008525097 | 145.247 | -2.387 | 0.0 |
| 5.030771068507152 | 122.206 | -4.552 | 1.0 |
| 5.545277712890018 | 135.0 | -7.225 | 0.0 |
| 5.705662173581409 | 197.927 | -6.549 | 0.0 |
| 6.255973203031458 | 131.82 | -11.789 | 0.0 |
| 6.037879583369097 | 126.004 | -8.087 | 1.0 |
| 5.117526637439253 | 93.552 | -12.528 | 1.0 |
| 5.414904042406895 | 115.039 | -3.915 | 1.0 |
| 5.7697063794565135 | 97.137 | -5.046 | 1.0 |
| 5.435838800858464 | 133.185 | -7.199 | 0.0 |
| 5.232977190837952 | 221.782 | -14.667 | 0.0 |
| 5.00955410982713 | 190.69 | -8.562 | 0.0 |
| 5.893314804558613 | 136.812 | -10.41 | 0.0 |
| 5.404387405434715 | 213.22 | -6.547 | 0.0 |
| 5.185434070040715 | 156.088 | -8.234 | 0.0 |
| 7.325579853249155 | 65.003 | -19.182 | 1.0 |
| 5.162060147947414 | 107.495 | -11.603 | 1.0 |
| 6.365001448317746 | 152.002 | -13.448 | 0.0 |
| 4.835980274163295 | 132.337 | -8.451 | 0.0 |
| 5.5738424918586675 | 108.817 | -18.442 | 1.0 |
| 5.849880878897477 | 105.999 | -6.79 | 1.0 |
| 6.583793532810955 | 153.889 | -12.067 | 0.0 |
| 5.333445920148879 | 108.063 | -13.16 | 1.0 |
| 6.090580906031972 | 144.492 | -17.963 | 0.0 |
| 5.896264131279372 | 121.989 | -9.937 | 1.0 |
| 5.651097440317911 | 140.057 | -6.712 | 0.0 |
| 6.030495771155491 | 115.095 | -18.929 | 1.0 |
| 5.437544818282431 | 103.032 | -9.638 | 1.0 |
| 5.486443295335265 | 136.035 | -4.405 | 0.0 |
| 5.815748326966135 | 122.224 | -9.521 | 1.0 |
| 5.173519496873623 | 129.39 | -5.085 | 1.0 |
| 6.10239869368692 | 88.652 | -5.985 | 1.0 |
| 5.597361508030061 | 131.324 | -12.262 | 0.0 |
| 5.820640994190337 | 204.628 | -8.183 | 0.0 |
| 5.498276504367628 | 85.533 | -11.401 | 1.0 |
| 5.477748500090494 | 75.502 | -11.612 | 1.0 |
| 5.823889504042276 | 127.869 | -5.024 | 1.0 |
| 5.4992384988300875 | 132.654 | -5.809 | 0.0 |
| 5.075660634218234 | 104.067 | -6.125 | 1.0 |
| 6.108517049323111 | 124.932 | -9.901 | 1.0 |
| 5.4844935264715495 | 125.023 | -12.544 | 1.0 |
| 6.215462333459528 | 140.038 | -7.024 | 0.0 |
| 5.153340899640642 | 113.128 | -18.089 | 1.0 |
| 5.993844263335907 | 44.347 | -18.049 | 1.0 |
| 5.515452503437732 | 118.309 | -6.553 | 1.0 |
| 4.12946795048469 | 194.298 | -17.978 | 0.0 |
| 4.812691543803788 | 139.898 | -9.898 | 0.0 |
| 6.323752308870225 | 138.436 | -10.424 | 0.0 |
| 5.4150202635090405 | 123.052 | -9.409 | 1.0 |
| 5.567376439918615 | 102.157 | -14.595 | 1.0 |
| 5.258852627622889 | 134.132 | -6.095 | 0.0 |
| 6.594784635725493 | 227.875 | -11.714 | 0.0 |
| 5.552293161360369 | 92.892 | -13.621 | 1.0 |
| 5.7084394806176935 | 109.992 | -13.039 | 1.0 |
| 5.154397225836696 | 143.19 | -9.351 | 0.0 |
| 5.691835054279943 | 102.005 | -6.091 | 1.0 |
| 5.214400777576161 | 179.99 | -4.908 | 0.0 |
| 4.895164582955741 | 91.431 | -19.612 | 1.0 |
| 5.167285191133977 | 107.498 | -11.699 | 1.0 |
| 5.330414836847097 | 117.99 | -12.271 | 1.0 |
| 5.17425907635271 | 119.505 | -7.758 | 1.0 |
| 5.299718184943652 | 140.086 | -3.907 | 0.0 |
| 5.5420073860872145 | 106.033 | -4.204 | 1.0 |
| 6.3220176214299535 | 130.011 | -10.252 | 1.0 |
| 5.291467207691819 | 141.057 | -4.902 | 0.0 |
| 6.072798927904806 | 48.365 | -18.563 | 1.0 |
| 4.98662944562941 | 144.985 | -10.351 | 0.0 |
| 5.404034973441198 | 94.998 | -3.961 | 1.0 |
| 5.2087027312927825 | 157.139 | -7.1 | 0.0 |
| 5.118465138450936 | 131.871 | -15.363 | 0.0 |
| 6.281510311338044 | 151.874 | -11.933 | 0.0 |
| 5.619961385101719 | 96.944 | -14.183 | 1.0 |
| 5.260753028847223 | 111.351 | -15.393 | 1.0 |
| 5.53687601242964 | 169.593 | -9.765 | 0.0 |
| 5.32952902537022 | 141.64 | -5.4 | 0.0 |
| 5.325469634631027 | 95.979 | -7.698 | 1.0 |
| 5.598136029235467 | 165.314 | -4.397 | 0.0 |
| 5.29775986117079 | 178.307 | -8.545 | 0.0 |
| 5.134594536181753 | 81.843 | -22.412 | 1.0 |
| 5.84141839052181 | 95.947 | -8.133 | 1.0 |
| 5.109039953782471 | 122.446 | -12.571 | 1.0 |
| 5.481453008551355 | 168.109 | -5.271 | 0.0 |
| 5.01286108185851 | 92.251 | -11.857 | 1.0 |
| 5.378927989850037 | 224.17 | -5.477 | 0.0 |
| 5.850632972880582 | 108.969 | -8.918 | 1.0 |
| 5.49849034271394 | 99.93 | -17.013 | 1.0 |
| 5.806205058816614 | 132.383 | -9.016 | 0.0 |
| 5.096333303440499 | 101.57 | -17.726 | 1.0 |
| 5.871391892272962 | 132.009 | -5.77 | 0.0 |
| 5.708612823050197 | 91.47 | -7.755 | 1.0 |
| 5.568174489309459 | 73.128 | -7.331 | 1.0 |
| 5.2462738434408624 | 124.492 | -18.87 | 1.0 |
| 5.534816059624022 | 110.544 | -9.495 | 1.0 |
| 5.886882506137869 | 138.103 | -20.544 | 0.0 |
| 6.230024079221992 | 120.967 | -10.793 | 1.0 |
| 5.580069994537434 | 143.953 | -8.811 | 0.0 |
| 4.789716765581881 | 82.896 | -3.165 | 1.0 |
| 5.669468286133506 | 98.789 | -12.12 | 1.0 |
| 4.515553089786215 | 150.619 | -6.503 | 0.0 |
| 5.699559805056049 | 150.075 | -15.161 | 0.0 |
| 5.996381912027348 | 146.091 | -10.571 | 0.0 |
| 4.99621589916939 | 116.335 | -3.457 | 1.0 |
| 5.021683965446997 | 66.09 | -11.861 | 1.0 |
| 5.312161690119503 | 137.979 | -3.415 | 0.0 |
| 4.681794411148828 | 169.54 | -1.874 | 0.0 |
| 5.183531347846387 | 197.23 | -4.448 | 0.0 |
| 5.46908758781452 | 123.999 | -5.905 | 1.0 |
| 5.619961385101719 | 114.147 | -7.992 | 1.0 |
| 5.679242859527788 | 125.603 | -5.693 | 1.0 |
| 4.752093295454806 | 157.508 | -8.268 | 0.0 |
| 4.850188255964137 | 189.007 | -9.814 | 0.0 |
| 5.829587529187939 | 149.847 | -6.714 | 0.0 |
| 5.314349167717073 | 149.229 | -10.206 | 0.0 |
| 6.460487398673489 | 139.249 | -10.829 | 0.0 |
| 5.326486028234502 | 100.017 | -8.595 | 1.0 |
| 5.211840646603853 | 74.093 | -18.627 | 1.0 |
| 5.5224708477113715 | 168.112 | -11.212 | 0.0 |
| 5.557346228586187 | 153.035 | -10.108 | 0.0 |
| 4.149031749168673 | 244.729 | -12.307 | 0.0 |
| 5.4296729876474386 | 99.998 | -9.958 | 1.0 |
| 5.538212699980942 | 150.085 | -8.181 | 0.0 |
| 5.5347129603785135 | 92.352 | -10.665 | 1.0 |
| 6.927688701645798 | 152.117 | -13.286 | 0.0 |
| 5.310098495714278 | 92.01 | -11.466 | 1.0 |
| 5.388282834245317 | 92.715 | -5.977 | 1.0 |
| 5.522888298669394 | 182.007 | -11.471 | 0.0 |
| 5.4750158098978945 | 100.071 | -9.276 | 1.0 |
| 5.825972289884403 | 87.919 | -6.932 | 1.0 |
| 5.782745645993859 | 88.875 | -4.121 | 1.0 |
| 5.337724264290968 | 108.533 | -16.344 | 1.0 |
| 5.428985520263439 | 90.144 | -10.259 | 1.0 |
| 5.284472770879013 | 143.009 | -4.705 | 0.0 |
| -0.46762246646534256 | 0.0 | -11.501 | 1.0 |
| 5.484818758978618 | 133.085 | -4.901 | 0.0 |
| 6.0213471318777625 | 107.925 | -13.962 | 1.0 |
| 5.625626608000267 | 138.285 | -9.929 | 0.0 |
| 5.269260268431927 | 108.332 | -21.9 | 1.0 |
| 5.3677804141515395 | 219.981 | -3.461 | 0.0 |
| 5.5071134027489 | 147.963 | -3.674 | 0.0 |
| 5.180009168017768 | 142.4 | -11.379 | 0.0 |
| 5.451537384872765 | 137.901 | -7.268 | 0.0 |
| 4.969175040376371 | 124.642 | -4.763 | 1.0 |
| 4.775718205898383 | 147.092 | -18.009 | 0.0 |
| 5.6024815595101325 | 100.083 | -7.415 | 1.0 |
| 5.594451717639801 | 104.971 | -6.516 | 1.0 |
| 6.120757401055048 | 93.972 | -16.714 | 1.0 |
| 5.395893794652538 | 138.047 | -4.429 | 0.0 |
| 6.734419302670816 | 179.835 | -33.438 | 0.0 |
| 5.239647617097909 | 69.147 | -10.243 | 1.0 |
| 5.426805519641665 | 169.076 | -6.329 | 0.0 |
| 5.10935559222606 | 89.513 | -8.42 | 1.0 |
| 5.5075373127990135 | 153.985 | -7.317 | 0.0 |
| 5.446031432894987 | 115.747 | -15.481 | 1.0 |
| 5.680313145216478 | 114.581 | -9.027 | 1.0 |
| 5.399441566678913 | 82.954 | -13.815 | 1.0 |
| 5.338477349987303 | 157.712 | -7.382 | 0.0 |
| 5.447607682491125 | 143.916 | -7.63 | 0.0 |
| 5.563676948262813 | 83.916 | -10.044 | 1.0 |
| 5.608246392671233 | 88.532 | -6.595 | 1.0 |
| 5.287910098314883 | 93.786 | -10.887 | 1.0 |
| 5.140576650244527 | 134.871 | -3.676 | 0.0 |
| 5.591143684650032 | 168.384 | -7.089 | 0.0 |
| 5.208416947565343 | 101.52 | -14.26 | 1.0 |
| 5.417110114495573 | 169.442 | -9.472 | 0.0 |
| 5.021683965446997 | 122.078 | -19.606 | 1.0 |
| 6.199147882794492 | 132.714 | -7.768 | 0.0 |
| 6.108517049323111 | 105.928 | -14.004 | 1.0 |
| 5.4532168516649095 | 170.045 | -5.011 | 0.0 |
| 5.895617468199124 | 131.929 | -12.85 | 0.0 |
| 5.176622106863231 | 106.504 | -17.826 | 1.0 |
| 5.024951066029294 | 126.873 | -15.27 | 1.0 |
| 5.860433682698193 | 109.195 | -4.006 | 1.0 |
| 5.260481741716897 | 153.014 | -10.212 | 0.0 |
| 5.34560373391496 | 92.029 | -8.973 | 1.0 |
| 5.610065025782295 | 106.024 | -13.247 | 1.0 |
| 5.251077896930087 | 144.961 | -10.916 | 0.0 |
| 5.0738640203268615 | 117.048 | -9.627 | 1.0 |
| 5.0548849434899745 | 169.687 | -6.727 | 0.0 |
| 5.204120801444624 | 103.737 | -19.476 | 1.0 |
| 5.4206991450916595 | 67.033 | -8.49 | 1.0 |
| 6.051009975039416 | 197.562 | -13.031 | 0.0 |
| 5.746866580671896 | 87.489 | -15.121 | 1.0 |
| 5.518705926355802 | 98.981 | -11.814 | 1.0 |
| 5.565778351411341 | 129.431 | -4.206 | 1.0 |
| 5.004660748657053 | 74.66 | -12.801 | 1.0 |
| 5.944842273864006 | 109.297 | -8.042 | 1.0 |
| 5.543643886367694 | 140.011 | -3.486 | 0.0 |
| 5.219642706004484 | 101.627 | -4.346 | 1.0 |
| 5.294749559486927 | 165.103 | -5.384 | 0.0 |
| 5.341984382155066 | 159.084 | -11.812 | 0.0 |
| 5.487308631306926 | 82.534 | -13.498 | 1.0 |
| 5.364485091062758 | 100.219 | -4.528 | 1.0 |
| 5.982377358390442 | 132.971 | -11.635 | 0.0 |
| 5.465447406018871 | 121.993 | -5.775 | 1.0 |
| 5.058709646578064 | 81.39 | -16.188 | 1.0 |
| 5.538007190161263 | 113.393 | -6.436 | 1.0 |
| 5.033668384098964 | 100.023 | -12.367 | 1.0 |
| 5.247373958311608 | 223.498 | -3.657 | 0.0 |
| 5.562674758800665 | 123.919 | -5.821 | 1.0 |
| 5.158311171845707 | 86.237 | -15.494 | 1.0 |
| 5.60680828782645 | 115.512 | -7.121 | 1.0 |
| 5.495599413383852 | 95.377 | -6.289 | 1.0 |
| 5.498811055436358 | 101.568 | -12.832 | 1.0 |
| 5.074517725053727 | 173.108 | -6.866 | 0.0 |
| 5.540778250951651 | 97.039 | -9.439 | 1.0 |
| 5.8971974655874355 | 100.293 | -6.871 | 1.0 |
| 5.069932792536647 | 153.945 | -21.009 | 0.0 |
| 5.986979906121274 | 82.237 | -12.805 | 1.0 |
| 5.237845445572235 | 87.652 | -15.15 | 1.0 |
| 5.063841723939238 | 65.628 | -13.89 | 1.0 |
| 4.706410384786853 | 50.121 | -17.47 | 1.0 |
| 6.0878565960262705 | 127.995 | -11.17 | 1.0 |
| 3.504157618955287 | 125.031 | -5.369 | 1.0 |
| 4.7565936933158115 | 90.01 | -7.748 | 1.0 |
| 5.474577883963244 | 110.127 | -4.855 | 1.0 |
| 5.5897783399512715 | 105.667 | -8.454 | 1.0 |
| 5.945457885398698 | 171.963 | -3.563 | 0.0 |
| 5.848826961531335 | 129.981 | -10.377 | 1.0 |
| 5.432760624546931 | 87.715 | -14.454 | 1.0 |
| 4.723496896048981 | 132.015 | -4.98 | 0.0 |
| 5.982575027145929 | 143.82 | -8.604 | 0.0 |
| 5.789561773504737 | 140.121 | -6.537 | 0.0 |
| 5.279826815103747 | 156.397 | -6.984 | 0.0 |
| 5.244207847160986 | 131.95 | -7.106 | 0.0 |
| 4.910680806169572 | 131.109 | -20.422 | 1.0 |
| 5.5619726050421585 | 162.004 | -6.902 | 0.0 |
| 5.42611607745725 | 151.973 | -11.269 | 0.0 |
| 4.50174379869753 | 124.865 | -12.124 | 1.0 |
| 4.941942377618302 | 170.365 | -6.981 | 0.0 |
| 5.011296009109345 | 97.865 | -8.757 | 1.0 |
| 5.485902090214787 | 112.057 | -5.915 | 1.0 |
| 5.3391045444622005 | 67.313 | -20.143 | 1.0 |
| 5.70930582616093 | 107.184 | -12.753 | 1.0 |
| 5.618824478944614 | 111.425 | -19.405 | 1.0 |
| 5.980464366355212 | 131.993 | -7.63 | 0.0 |
| 6.1082846526292505 | 92.303 | -25.428 | 1.0 |
| 5.8513093799300835 | 129.726 | -10.85 | 1.0 |
| 4.878807820405577 | 106.618 | -10.543 | 1.0 |
| 6.044899763442047 | 120.309 | -8.014 | 1.0 |
| 5.116430555238567 | 117.448 | -5.097 | 1.0 |
| 5.147586739998935 | 92.229 | -11.192 | 1.0 |
| 5.4076711086539575 | 150.04 | -6.406 | 0.0 |
| 5.566178113028819 | 149.068 | -9.154 | 0.0 |
| 5.100639848169756 | 132.154 | -7.988 | 0.0 |
| 7.064310602477873 | 75.522 | -8.698 | 1.0 |
| 5.408490363618657 | 149.189 | -14.629 | 0.0 |
| 6.104150615266521 | 130.926 | -5.089 | 0.0 |
| 5.671089054964057 | 206.088 | -13.704 | 0.0 |
| 5.511555500800439 | 97.003 | -9.625 | 1.0 |
| 5.607958929769235 | 160.884 | -10.113 | 0.0 |
| 4.7382403972848515 | 165.221 | -4.938 | 0.0 |
| 5.936597611707141 | 100.006 | -11.279 | 1.0 |
| 4.890462354894079 | 88.17 | -9.01 | 1.0 |
| 5.482105347616156 | 133.828 | -7.421 | 0.0 |
| 5.0043102665494 | 86.863 | -12.931 | 1.0 |
| 5.486443295335265 | 102.293 | -9.785 | 1.0 |
| 4.800088568576119 | 221.302 | -17.832 | 0.0 |
| 5.986061074609641 | 108.706 | -10.16 | 1.0 |
| 5.283147534265708 | 190.149 | -8.206 | 0.0 |
| 4.6327014973652 | 74.23 | -15.089 | 1.0 |
| 6.009616032607714 | 120.021 | -4.022 | 1.0 |
| 4.046688679512158 | 110.863 | -11.584 | 1.0 |
| 5.6538472051037845 | 110.506 | -6.696 | 1.0 |
| 5.172927427877826 | 185.86 | -24.732 | 0.0 |
| 5.57404080623269 | 90.029 | -5.921 | 1.0 |
| 5.490007979677294 | 96.037 | -6.355 | 1.0 |
| 5.083624941157512 | 132.199 | -12.669 | 0.0 |
| 5.751526282061471 | 156.92 | -6.253 | 0.0 |
| 5.328895842919929 | 142.536 | -4.303 | 0.0 |
| 5.483191579208695 | 107.992 | -13.702 | 1.0 |
| 5.5472144309132965 | 80.345 | -11.443 | 1.0 |
| 6.197873655430997 | 154.718 | -10.505 | 0.0 |
| 5.270469694487864 | 154.34 | -12.876 | 0.0 |
| 5.821724993890699 | 83.845 | -13.803 | 1.0 |
| 6.017854961519274 | 88.639 | -9.695 | 1.0 |
| 5.7608632558531845 | 96.159 | -15.306 | 1.0 |
| 5.805182768978867 | 118.885 | -12.696 | 1.0 |
| 5.33860284917152 | 127.972 | -3.162 | 1.0 |
| 5.666038065895797 | 88.983 | -11.514 | 1.0 |
| 5.722211446501281 | 96.153 | -4.929 | 1.0 |
| 5.417342075453478 | 95.817 | -14.332 | 1.0 |
| 5.206414390626549 | 51.149 | -15.564 | 1.0 |
| 5.508913770598034 | 115.147 | -15.988 | 1.0 |
| 6.460119626844432 | 128.001 | -2.999 | 1.0 |
| 5.754758628760087 | 130.036 | -4.706 | 1.0 |
| 5.2897561234655734 | 84.075 | -9.918 | 1.0 |
| 5.533165036662569 | 122.851 | -8.173 | 1.0 |
| 5.307772277671411 | 128.666 | -10.686 | 1.0 |
| 5.124544227693321 | 146.426 | -10.195 | 0.0 |
| 4.841770152276834 | 107.686 | -12.882 | 1.0 |
| 4.370691635610005 | 89.638 | -6.059 | 1.0 |
| 5.417805791645294 | 75.976 | -7.941 | 1.0 |
| 4.8971173002048465 | 84.637 | -5.956 | 1.0 |
| 5.669468286133506 | 114.008 | -6.23 | 1.0 |
| 5.543950442019833 | 67.374 | -16.341 | 1.0 |
| 5.094413293795442 | 45.955 | -12.685 | 1.0 |
| 5.929744237745122 | 125.005 | -11.71 | 1.0 |
| 5.447382637575409 | 114.04 | -5.444 | 1.0 |
| 5.139199353255555 | 149.999 | -11.615 | 0.0 |
| 5.377601632831448 | 111.976 | -6.128 | 1.0 |
| 5.449293713507458 | 133.935 | -7.701 | 0.0 |
| 6.044714039392183 | 104.12 | -9.679 | 1.0 |
| 4.544834553660172 | 100.041 | -21.365 | 1.0 |
| 5.368511209912809 | 119.56 | -7.649 | 1.0 |
| 5.5776034158343775 | 159.59 | -6.029 | 0.0 |
| 5.40026758875988 | 133.742 | -5.443 | 0.0 |
| 5.963349420015804 | 167.592 | -8.413 | 0.0 |
| 5.3085483015925226 | 138.169 | -12.368 | 0.0 |
| 5.51943911539636 | 94.183 | -5.847 | 1.0 |
| 5.261566345193816 | 86.159 | -10.834 | 1.0 |
| 5.461350168990491 | 147.965 | -4.758 | 0.0 |
| 5.489360777153861 | 100.992 | -3.3 | 1.0 |
| 5.011643985196295 | 106.364 | -7.337 | 1.0 |
| 5.3578617345120865 | 121.875 | -6.827 | 1.0 |
| 5.2234505988894995 | 142.658 | -7.304 | 0.0 |
| 5.354039496648361 | 45.604 | -13.906 | 1.0 |
| 5.9023510257967615 | 85.911 | -11.271 | 1.0 |
| 5.9798699133794955 | 131.997 | -3.879 | 0.0 |
| 6.158927002222738 | 125.589 | -8.6 | 1.0 |
| 5.646036441142801 | 140.666 | -13.852 | 0.0 |
| 5.177948847658558 | 93.574 | -5.806 | 1.0 |
| 6.0009853055100955 | 0.0 | -10.183 | 1.0 |
| 5.899705992808433 | 87.9 | -15.899 | 1.0 |
| 5.658504560309888 | 106.681 | -15.668 | 1.0 |
| 5.559460924718209 | 150.033 | -6.73 | 0.0 |
| 5.334706140852812 | 218.141 | -5.645 | 0.0 |
| 5.472275631642377 | 112.281 | -5.896 | 1.0 |
| 4.2823579453514045 | 49.981 | -25.588 | 1.0 |
| 5.599586650851792 | 136.022 | -5.896 | 0.0 |
| 5.111089674329477 | 100.027 | -5.311 | 1.0 |
| 6.602400072652759 | 115.18 | -14.568 | 1.0 |
| 5.409542680605578 | 108.054 | -8.994 | 1.0 |
| 5.468867325822424 | 110.042 | -4.603 | 1.0 |
| 5.848299586027214 | 202.441 | -13.184 | 0.0 |
| 5.658595655633918 | 124.621 | -10.015 | 1.0 |
| 3.5614042570882756 | 152.794 | -4.403 | 0.0 |
| 5.193734048793662 | 127.836 | -10.722 | 1.0 |
| 5.217804085672296 | 101.949 | -6.382 | 1.0 |
| 5.413042487099622 | 84.978 | -9.372 | 1.0 |
| 5.604022100743859 | 144.111 | -6.032 | 0.0 |
| 5.197352841018148 | 109.324 | -12.569 | 1.0 |
| 5.537287494271165 | 184.26 | -4.943 | 0.0 |
| 5.5876290729248534 | 181.989 | -57.871 | 0.0 |
| 5.788362289459809 | 171.408 | -13.348 | 0.0 |
| 5.569171223003255 | 110.066 | -9.176 | 1.0 |
| 5.560968705190409 | 138.494 | -6.407 | 0.0 |
| 5.530372729442885 | 127.804 | -4.33 | 1.0 |
| 5.05521807996722 | 112.929 | -16.082 | 1.0 |
| 5.639279330670629 | 128.078 | -5.433 | 1.0 |
| 3.9130257025593167 | 86.301 | -23.716 | 1.0 |
| 5.539957987406313 | 175.214 | -8.771 | 0.0 |
| 6.39178918226169 | 119.462 | -10.856 | 1.0 |
| 5.653755676169645 | 81.32 | -12.222 | 1.0 |
| 5.639186458532492 | 120.161 | -13.72 | 1.0 |
| 5.573346590758997 | 81.476 | -15.363 | 1.0 |
| 5.294880617686237 | 137.992 | -4.168 | 0.0 |
| 4.713702027671486 | 85.71 | -12.635 | 1.0 |
| 5.609491062398614 | 84.97 | -7.176 | 1.0 |
| 5.925009523986467 | 130.022 | -4.689 | 1.0 |
| 5.478948537110532 | 95.746 | -6.306 | 1.0 |
| 5.15981242631456 | 112.042 | -9.297 | 1.0 |
| 5.696757691877665 | 89.34 | -6.331 | 1.0 |
| 5.248747375892955 | 93.116 | -7.831 | 1.0 |
| 5.327374538139593 | 154.805 | -6.684 | 0.0 |
| 5.6010351712128035 | 116.092 | -5.004 | 1.0 |
| 5.248198224678815 | 119.855 | -5.583 | 1.0 |
| 5.569768756207467 | 96.71 | -8.455 | 1.0 |
| 5.417921676011282 | 88.226 | -7.401 | 1.0 |
| 5.302583553418002 | 125.01 | -11.412 | 1.0 |
| 5.373248571181468 | 121.6 | -8.669 | 1.0 |
| 5.4768748480907865 | 190.177 | -6.363 | 0.0 |
| 5.290941028132201 | 53.175 | -20.641 | 1.0 |
| 5.553002142155581 | 103.143 | -11.409 | 1.0 |
| 5.426460857965772 | 174.544 | -10.371 | 0.0 |
| 5.197641748488691 | 158.665 | -6.705 | 0.0 |
| 5.314220636516537 | 116.358 | -10.622 | 1.0 |
| 5.777744424244385 | 199.958 | -9.977 | 0.0 |
| 5.706965005029758 | 180.668 | -12.834 | 0.0 |
| 5.069604460127078 | 84.134 | -9.14 | 1.0 |
| 6.690179312364173 | 83.095 | -12.133 | 1.0 |
| 4.7525442132277 | 156.604 | -15.224 | 0.0 |
| 5.971244033509483 | 90.654 | -6.785 | 1.0 |
| 5.594548838511711 | 170.612 | -13.813 | 0.0 |
| 4.014674535094541 | 109.158 | -23.73 | 1.0 |
| 5.770846990281751 | 135.322 | -7.431 | 0.0 |
| 5.119871270445839 | 122.053 | -11.468 | 1.0 |
| 5.476547021251197 | 197.455 | -10.545 | 0.0 |
| 5.3500785889402005 | 147.991 | -11.104 | 0.0 |
| 4.7875424081652005 | 94.696 | -20.435 | 1.0 |
| 5.240063058317482 | 164.438 | -9.956 | 0.0 |
| 5.516188080858318 | 92.03 | -13.267 | 1.0 |
| 5.53202836519056 | 110.088 | -10.689 | 1.0 |
| 4.880792784407153 | 207.2 | -6.791 | 0.0 |
| 5.496778190223019 | 97.241 | -7.526 | 1.0 |
| 5.45679029199923 | 155.982 | -6.263 | 0.0 |
| 5.713195037175519 | 134.129 | -8.611 | 0.0 |
| 5.630135755083509 | 190.063 | -7.253 | 0.0 |
| 6.122305620943067 | 131.987 | -9.198 | 0.0 |
| 4.9978047333603985 | 149.952 | -12.247 | 0.0 |
| 6.5302317923969815 | 123.571 | -12.814 | 1.0 |
| 5.404504900132244 | 121.517 | -15.794 | 1.0 |
| 6.195799582033983 | 159.918 | -10.965 | 0.0 |
| 5.362649647669598 | 143.937 | -14.138 | 0.0 |
| 4.752769595889496 | 82.014 | -23.293 | 1.0 |
| 5.241446546643105 | 92.462 | -34.256 | 1.0 |
| 5.417805791645294 | 105.151 | -7.58 | 1.0 |
| 4.409874508091659 | 121.958 | -11.344 | 1.0 |
| 5.438794009985275 | 169.927 | -8.45 | 0.0 |
| 6.2486276164729 | 117.655 | -16.783 | 1.0 |
| 4.517265864988051 | 188.052 | -6.88 | 0.0 |
| 5.494204452408169 | 105.99 | -5.787 | 1.0 |
| 4.874227540136429 | 173.551 | -10.198 | 0.0 |
| 5.969843589651059 | 123.998 | -6.37 | 1.0 |
| 5.507007407297921 | 125.824 | -13.586 | 1.0 |
| 5.765949435867272 | 108.686 | -6.846 | 1.0 |
| 5.303103644025734 | 176.046 | -8.14 | 0.0 |
| 4.786017489938488 | 93.215 | -11.675 | 1.0 |
| 6.575998134820792 | 97.952 | -10.883 | 1.0 |
| 5.37106486725144 | 130.003 | -5.82 | 1.0 |
| 4.669132853853989 | 99.792 | -5.716 | 1.0 |
| 5.476984085886269 | 89.121 | -9.853 | 1.0 |
| 5.716295558454968 | 205.646 | -10.852 | 0.0 |
| 5.9901237486393155 | 140.025 | -3.333 | 0.0 |
| 5.995536744446201 | 90.218 | -16.787 | 1.0 |
| 5.649719699672337 | 149.789 | -12.721 | 0.0 |
| 4.9578568974901085 | 156.095 | -4.053 | 0.0 |
| 5.874333080384451 | 91.002 | -7.096 | 1.0 |
| 5.23978609901084 | 90.019 | -15.565 | 1.0 |
| 5.408841273925989 | 119.996 | -5.901 | 1.0 |
| 5.699734693432106 | 90.024 | -6.646 | 1.0 |
| 5.20469469210529 | 119.894 | -11.582 | 1.0 |
| 4.1794787478344135 | 152.689 | -17.367 | 0.0 |
| 5.931548684999535 | 137.134 | -5.965 | 0.0 |
| 4.98965732035097 | 82.679 | -9.038 | 1.0 |
| 6.426326551147688 | 89.058 | -20.967 | 1.0 |
| 5.426690616297435 | 122.034 | -3.082 | 1.0 |
| 5.534403559364284 | 149.604 | -17.91 | 0.0 |
| 5.245034726302448 | 113.111 | -8.918 | 1.0 |
| 5.850106549213573 | 89.831 | -13.384 | 1.0 |
| 5.665766747215943 | 122.321 | -11.821 | 1.0 |
| 4.707825866876277 | 170.711 | -12.47 | 0.0 |
| 6.245339882354565 | 94.557 | -7.916 | 1.0 |
| 5.09232913021438 | 211.055 | -12.421 | 0.0 |
| 5.134286824551301 | 170.602 | -9.905 | 0.0 |
| 5.3780841438485965 | 161.908 | -7.685 | 0.0 |
| 6.561368275095364 | 127.745 | -12.886 | 1.0 |
| 5.447607682491125 | 130.943 | -6.45 | 0.0 |
| 6.051686570245384 | 120.992 | -9.311 | 1.0 |
| 4.718378239019314 | 103.102 | -6.576 | 1.0 |
| 5.618540041253813 | 106.485 | -9.268 | 1.0 |
| 5.576417288984766 | 126.003 | -10.223 | 1.0 |
| 4.852026748288437 | 151.788 | -14.945 | 0.0 |
| 5.377480980239354 | 98.18 | -12.404 | 1.0 |
| 5.45556335097355 | 133.997 | -4.746 | 0.0 |
| 5.948462130318859 | 134.692 | -18.794 | 0.0 |
| 5.734952664000064 | 133.04 | -7.916 | 0.0 |
| 5.453328704474266 | 99.637 | -12.155 | 1.0 |
| 5.256676338794948 | 106.661 | -12.468 | 1.0 |
| 6.104267290560383 | 125.242 | -9.434 | 1.0 |
| 5.448170008744669 | 170.037 | -8.649 | 0.0 |
| 5.087341549690884 | 68.702 | -22.903 | 1.0 |
| 5.453776033444273 | 115.875 | -6.211 | 1.0 |
| 5.218935952671551 | 153.253 | -22.082 | 0.0 |
| 5.384695180378907 | 145.41 | -10.554 | 0.0 |
| 5.279161356150083 | 95.018 | -15.424 | 1.0 |
| 5.767993004510512 | 72.212 | -7.587 | 1.0 |
| 5.7792797289295015 | 170.005 | -3.889 | 0.0 |
| 6.032628899017258 | 159.372 | -9.756 | 0.0 |
| 5.400503444004186 | 103.989 | -9.456 | 1.0 |
| 5.3355873802467535 | 110.372 | -20.244 | 1.0 |
| 4.844652647359633 | 126.318 | -7.571 | 1.0 |
| 5.542519070382163 | 92.921 | -2.786 | 1.0 |
| 5.57066444420481 | 87.009 | -6.488 | 1.0 |
| 6.188747496998347 | 129.979 | -8.072 | 1.0 |
| 4.8704278758447845 | 95.721 | -11.533 | 1.0 |
| 5.2727501941416905 | 141.364 | -11.059 | 0.0 |
| 5.09024055225655 | 86.848 | -8.704 | 1.0 |
| 5.254904623100747 | 196.936 | -4.971 | 0.0 |
| 5.48676785302265 | 135.165 | -5.155 | 0.0 |
| 6.000855909886411 | 73.509 | -13.008 | 1.0 |
| 4.355383461601689 | 71.295 | -11.861 | 1.0 |
| 4.7754979371223625 | 140.092 | -18.953 | 0.0 |
| 4.996392545987193 | 108.872 | -10.247 | 1.0 |
| 4.969537983902341 | 86.353 | -9.653 | 1.0 |
| 5.486443295335265 | 122.439 | -10.655 | 1.0 |
| 5.364240537989767 | 143.027 | -5.313 | 0.0 |
| 5.679421302966559 | 81.362 | -7.025 | 1.0 |
| 5.333824124190991 | 79.702 | -17.71 | 1.0 |
| 5.403447265615902 | 84.366 | -12.872 | 1.0 |
| 6.074122570322013 | 125.007 | -10.765 | 1.0 |
| 5.472385372975611 | 102.721 | -7.164 | 1.0 |
| 5.1453062704818135 | 160.143 | -9.545 | 0.0 |
| 5.947780126418685 | 126.014 | -7.45 | 1.0 |
| 5.2564039954429544 | 225.306 | -15.196 | 0.0 |
| 5.68280603270908 | 89.046 | -5.752 | 1.0 |
| 5.60815059311142 | 95.313 | -6.54 | 1.0 |
| 5.283147534265708 | 98.903 | -12.336 | 1.0 |
| 5.042816351013717 | 210.493 | -10.655 | 0.0 |
| 5.564978348428669 | 166.102 | -4.211 | 0.0 |
| 5.157710026925304 | 110.469 | -15.571 | 1.0 |
| 5.133979018205255 | 107.188 | -7.923 | 1.0 |
| 5.182065308347365 | 106.091 | -19.624 | 1.0 |
| 5.545277712890018 | 100.173 | -3.09 | 1.0 |
| 4.968630308451178 | 100.309 | -4.012 | 1.0 |
| 5.52497294347421 | 31.096 | -21.819 | 1.0 |
| 5.826742575045937 | 96.842 | -16.207 | 1.0 |
| 5.174850357678343 | 157.956 | -11.123 | 0.0 |
| 5.703138567392408 | 146.388 | -7.098 | 0.0 |
| 6.208758281558591 | 183.824 | -7.48 | 0.0 |
| 5.584396467427088 | 140.496 | -14.859 | 0.0 |
| 5.572949685143726 | 62.083 | -4.533 | 1.0 |
| 5.487741018652274 | 159.984 | -9.817 | 0.0 |
| 5.937631926094691 | 126.041 | -7.954 | 1.0 |
| 4.585079513210795 | 106.927 | -18.665 | 1.0 |
| 5.758640225480897 | 112.205 | -10.926 | 1.0 |
| 5.891223432702917 | 207.899 | -10.47 | 0.0 |
| 5.883323391273292 | 73.209 | -20.003 | 1.0 |
| 4.153966014365387 | 93.167 | -7.499 | 1.0 |
| 5.634811317219212 | 150.033 | -5.412 | 0.0 |
| 5.229205416542948 | 89.99 | -7.618 | 1.0 |
| 4.941942377618302 | 81.417 | -13.292 | 1.0 |
| 6.127278052481187 | 128.005 | -11.899 | 1.0 |
| 4.92101980206768 | 117.12 | -13.798 | 1.0 |
| 5.389952711501166 | 127.897 | -6.24 | 1.0 |
| 5.558051637150017 | 91.59 | -9.476 | 1.0 |
| 5.769380247088984 | 160.109 | -9.505 | 0.0 |
| 5.557749374612166 | 178.073 | -6.63 | 0.0 |
| 5.507007407297921 | 179.858 | -11.375 | 0.0 |
| 5.092810434211056 | 129.327 | -10.429 | 1.0 |
| 5.461350168990491 | 167.918 | -6.244 | 0.0 |
| 5.5424167623007925 | 156.889 | -7.987 | 0.0 |
| 5.639557930889243 | 180.013 | -4.423 | 0.0 |
| 5.4874167353256125 | 120.019 | -8.924 | 1.0 |
| 5.281023587458628 | 121.364 | -14.483 | 1.0 |
| 4.978390025199081 | 179.44 | -10.658 | 0.0 |
| 5.892738309215478 | 196.909 | -22.759 | 0.0 |
| 4.791669669744817 | 93.707 | -17.84 | 1.0 |
| 5.211698239890913 | 99.971 | -6.146 | 1.0 |
| 5.634624696715785 | 133.31 | -4.637 | 0.0 |
| 4.848551250414092 | 149.893 | -12.846 | 0.0 |
| 5.8619215616314975 | 175.06 | -7.078 | 0.0 |
| 5.452433445776701 | 114.964 | -7.285 | 1.0 |
| 5.3639959719175305 | 146.789 | -15.522 | 0.0 |
| 4.113365838591499 | 105.722 | -15.85 | 1.0 |
| 5.047191241965447 | 106.108 | -7.656 | 1.0 |
| 5.08135591660589 | 140.245 | -5.012 | 0.0 |
| 5.073536976343666 | 131.66 | -14.776 | 0.0 |
| 5.1257866625447654 | 152.27 | -3.664 | 0.0 |
| 5.881502627878266 | 102.993 | -15.453 | 1.0 |
| 5.4624591684756965 | 125.754 | -11.187 | 1.0 |
| 5.089436123265493 | 142.636 | -10.52 | 0.0 |
| 6.069784155714005 | 132.612 | -11.188 | 0.0 |
| 5.964490624933571 | 126.997 | -10.568 | 1.0 |
| 5.4260011388685205 | 169.261 | -9.47 | 0.0 |
| 5.49699239016298 | 123.973 | -9.416 | 1.0 |
| 6.077124311080133 | 136.171 | -7.213 | 0.0 |
| 4.133662706526249 | 167.928 | -11.738 | 0.0 |
| 6.1946809857780325 | 126.989 | -13.678 | 1.0 |
| 5.481779231277037 | 114.653 | -11.645 | 1.0 |
| 6.121560489073806 | 144.971 | -6.83 | 0.0 |
| 5.262649773580425 | 126.761 | -19.334 | 1.0 |
| 5.363261820908567 | 122.936 | -9.6 | 1.0 |
| 5.7159515304366515 | 136.241 | -10.343 | 0.0 |
| 5.156205552886701 | 62.968 | -8.561 | 1.0 |
| 6.130692977625778 | 125.984 | -10.339 | 1.0 |
| 5.505628321155564 | 166.233 | -12.606 | 0.0 |
| 5.3281354555602105 | 99.184 | -5.945 | 1.0 |
| 5.287249998541103 | 229.902 | -7.619 | 0.0 |
| 5.308419022584635 | 92.062 | -7.396 | 1.0 |
| 5.553002142155581 | 146.02 | -2.758 | 0.0 |
| 5.459350805751235 | 143.933 | -6.128 | 0.0 |
| 5.567176799358838 | 145.129 | -4.733 | 0.0 |
| 5.81348808669541 | 156.268 | -7.379 | 0.0 |
| 5.740675641430166 | 116.564 | -12.468 | 1.0 |
| 5.578886791487193 | 122.987 | -6.711 | 1.0 |
| 5.478948537110532 | 105.101 | -9.257 | 1.0 |
| 5.100799041585248 | 128.458 | -7.83 | 1.0 |
| 5.217095977001863 | 203.536 | -5.012 | 0.0 |
| 5.002204992080364 | 183.682 | -13.115 | 0.0 |
| 5.331931526930796 | 133.262 | -12.529 | 0.0 |
| 5.473262954307411 | 171.967 | -14.198 | 0.0 |
| 5.869032725696973 | 130.033 | -5.078 | 1.0 |
| 5.938389745726143 | 126.896 | -8.268 | 1.0 |
| 5.4494060059680445 | 195.438 | -11.615 | 0.0 |
| 5.281820589901206 | 131.663 | -8.523 | 0.0 |
| 5.376998178031043 | 132.682 | -12.438 | 0.0 |
| 5.443888273735132 | 171.456 | -14.974 | 0.0 |
| 5.363751299185053 | 114.203 | -6.931 | 1.0 |
| 5.216954316773034 | 119.26 | -12.515 | 1.0 |
| 5.5502647555781275 | 85.61 | -5.781 | 1.0 |
| 5.488173219119607 | 163.748 | -19.01 | 0.0 |
| 5.30414301446121 | 129.8 | -9.067 | 1.0 |
| 5.746949972332082 | 128.572 | -10.454 | 1.0 |
| 5.117213567985459 | 186.056 | -4.145 | 0.0 |
| 5.6087252895126944 | 103.505 | -10.694 | 1.0 |
| 5.383376455730054 | 162.933 | -4.809 | 0.0 |
| 5.347595030917553 | 119.679 | -5.597 | 1.0 |
| 5.629667005038476 | 81.091 | -16.247 | 1.0 |
| 4.675727153638656 | 85.001 | -8.597 | 1.0 |
| 5.2261216034182745 | 116.854 | -11.529 | 1.0 |
| 6.027098474853351 | 135.055 | -6.416 | 0.0 |
| 5.6603250660364255 | 110.539 | -21.517 | 1.0 |
| 5.219218692301358 | 121.962 | -8.005 | 1.0 |
| 5.4450168406772255 | 126.994 | -13.544 | 1.0 |
| 4.625048398662918 | 109.869 | -7.206 | 1.0 |
| 5.19460377174902 | 171.625 | -17.449 | 0.0 |
| 5.031112334447694 | 104.284 | -18.534 | 1.0 |
| 6.091113075382214 | 0.0 | -3.765 | 1.0 |
| 5.673694744556976 | 105.17 | -4.582 | 1.0 |
| 6.148711492838604 | 124.97 | -11.179 | 1.0 |
| 5.195906884148954 | 91.961 | -13.027 | 1.0 |
| 5.585181091745024 | 124.172 | -5.218 | 1.0 |
| 3.462451138864747 | 107.748 | -14.744 | 1.0 |
| 5.5875312401931 | 116.656 | -6.006 | 1.0 |
| 4.908561519331269 | 214.522 | -3.418 | 0.0 |
| 5.380132256824099 | 94.733 | -16.722 | 1.0 |
| 5.49699239016298 | 122.094 | -8.941 | 1.0 |
| 5.263191047910029 | 158.84 | -7.591 | 0.0 |
| 5.280092905292474 | 137.932 | -5.419 | 0.0 |
| 5.314092039576155 | 121.998 | -7.371 | 1.0 |
| 5.54894409174871 | 129.777 | -24.142 | 1.0 |
| 5.552799610886144 | 168.348 | -6.607 | 0.0 |
| 5.8116918591861175 | 140.085 | -17.522 | 0.0 |
| 5.094893657240654 | 72.891 | -9.243 | 1.0 |
| 3.3452804321804512 | 103.988 | -10.668 | 1.0 |
| 5.3080310357356755 | 111.98 | -10.477 | 1.0 |
| 7.319039310933789 | 60.94 | -21.661 | 1.0 |
| 5.21070071550793 | 90.069 | -9.896 | 1.0 |
| 5.201390300287031 | 130.051 | -8.735 | 1.0 |
| 4.65307710306677 | 127.79 | -15.249 | 1.0 |
| 5.871465548643589 | 76.905 | -7.595 | 1.0 |
| 4.964261877239046 | 101.104 | -4.278 | 1.0 |
| 5.562775015280007 | 103.544 | -15.525 | 1.0 |
| 4.731356393668162 | 106.842 | -10.388 | 1.0 |
| 5.054384966781395 | 81.619 | -16.783 | 1.0 |
| 5.028207592920682 | 170.216 | -3.905 | 0.0 |
| 5.397787518023055 | 126.006 | -5.971 | 1.0 |
| 5.396012246148784 | 63.958 | -9.75 | 1.0 |
| 5.199661987799504 | 104.909 | -17.026 | 1.0 |
| 5.903706582400177 | 99.805 | -13.637 | 1.0 |
| 5.124544227693321 | 150.761 | -5.896 | 0.0 |
| 5.2732860303472995 | 165.046 | -5.07 | 0.0 |
| 5.403329646578874 | 65.586 | -5.689 | 1.0 |
| 5.971976788235537 | 127.943 | -8.425 | 1.0 |
| 5.182798596755828 | 84.671 | -7.545 | 1.0 |
| 5.643265084590237 | 112.902 | -9.906 | 1.0 |
| 5.648616145884805 | 127.601 | -11.989 | 1.0 |
| 5.4215077560794285 | 171.999 | -5.555 | 0.0 |
| 3.270746192716828 | 98.219 | -12.139 | 1.0 |
| 3.6828088150144107 | 93.888 | -11.561 | 1.0 |
| 5.141035347524807 | 119.04 | -9.104 | 1.0 |
| 5.602577903676948 | 83.991 | -7.362 | 1.0 |
| 5.95281586631299 | 137.976 | -7.193 | 0.0 |
| 6.126022996504088 | 188.91 | -6.231 | 0.0 |
| 5.498704176263897 | 130.67 | -7.891 | 1.0 |
| 5.457904357110395 | 106.693 | -11.026 | 1.0 |
| 5.32088303587057 | 117.367 | -9.743 | 1.0 |
| 5.191411212562029 | 126.723 | -8.691 | 1.0 |
| 4.327844566531178 | 111.022 | -11.143 | 1.0 |
| 5.627601868901141 | 120.112 | -4.516 | 1.0 |
| 5.5326485423577845 | 119.976 | -7.908 | 1.0 |
| 5.40026758875988 | 102.654 | -4.451 | 1.0 |
| 5.340232427453506 | 86.07 | -6.841 | 1.0 |
| 5.575031674861896 | 77.899 | -21.361 | 1.0 |
| 5.5552270510359225 | 98.978 | -7.177 | 1.0 |
| 5.474796891858358 | 125.077 | -7.702 | 1.0 |
| 5.517657614464372 | 66.393 | -11.003 | 1.0 |
| 3.6662465002195974 | 144.766 | -6.59 | 0.0 |
| 5.21070071550793 | 125.97 | -3.778 | 1.0 |
| 5.369849670944156 | 132.111 | -9.783 | 0.0 |
| 5.9388716981387315 | 120.224 | -16.514 | 1.0 |
| 5.49945213158597 | 160.129 | -6.031 | 0.0 |
| 5.556438565006127 | 94.009 | -4.173 | 1.0 |
| 6.093002859946301 | 97.994 | -6.121 | 1.0 |
| 5.611880320928216 | 91.978 | -5.309 | 1.0 |
| 5.110301789545726 | 203.314 | -20.934 | 0.0 |
| 5.690159332949762 | 122.922 | -4.283 | 1.0 |
| 5.404622336068872 | 167.079 | -8.043 | 0.0 |
| 5.534197225661299 | 142.956 | -10.356 | 0.0 |
| 5.226402372674139 | 90.301 | -11.586 | 1.0 |
| 5.095213730551448 | 103.179 | -12.914 | 1.0 |
| 5.412925991311518 | 87.754 | -12.384 | 1.0 |
| 5.537390328439192 | 157.07 | -6.331 | 0.0 |
| 5.553204593664214 | 161.207 | -22.064 | 0.0 |
| 5.220631354684124 | 144.042 | -7.393 | 0.0 |
| 5.295273739410578 | 95.797 | -20.803 | 1.0 |
| 4.813752393650261 | 119.291 | -4.694 | 1.0 |
| 5.500199568748536 | 127.232 | -5.098 | 1.0 |
| 5.249570511440754 | 173.229 | -7.802 | 0.0 |
| 5.577405806789707 | 159.836 | -5.142 | 0.0 |
| 5.248472811704074 | 125.033 | -7.126 | 1.0 |
| 6.931088636496423 | 131.07 | -13.389 | 0.0 |
| 5.197641748488691 | 174.069 | -13.854 | 0.0 |
| 5.539547603183485 | 92.331 | -14.759 | 1.0 |
| 5.39530128123997 | 157.772 | -11.69 | 0.0 |
| 4.931818468056029 | 90.969 | -11.836 | 1.0 |
| 5.106353269528519 | 95.792 | -16.014 | 1.0 |
| 5.508066937648978 | 123.614 | -9.07 | 1.0 |
| 5.32585091546435 | 109.537 | -7.016 | 1.0 |
| 4.949190790435204 | 125.057 | -4.888 | 1.0 |
| 4.937080676505961 | 65.753 | -14.4 | 1.0 |
| 5.604214520046329 | 40.925 | -29.594 | 1.0 |
| 3.290393187145132 | 147.79 | -13.411 | 0.0 |
| 5.157710026925304 | 151.961 | -14.262 | 0.0 |
| 6.245846402132603 | 134.647 | -9.373 | 0.0 |
| 5.304013114932819 | 160.12 | -6.469 | 0.0 |
| 5.64815598094485 | 173.833 | -5.775 | 0.0 |
| 5.516398163722808 | 146.013 | -12.798 | 0.0 |
| 6.118459335104977 | 147.906 | -7.705 | 0.0 |
| 5.363751299185053 | 96.92 | -11.98 | 1.0 |
| 5.77174229272717 | 125.981 | -8.059 | 1.0 |
| 5.366683148121869 | 67.969 | -8.551 | 1.0 |
| 5.212125453705929 | 148.935 | -7.582 | 0.0 |
| 5.562975536463055 | 101.633 | -14.515 | 1.0 |
| 5.324961050170624 | 130.293 | -12.07 | 1.0 |
| 5.848224232203182 | 140.004 | -9.647 | 0.0 |
| 5.483734294400877 | 147.954 | -7.107 | 0.0 |
| 5.576812820943777 | 85.975 | -3.637 | 1.0 |
| 5.757898115806847 | 112.013 | -5.394 | 1.0 |
| 6.065608777532936 | 96.9 | -7.988 | 1.0 |
| 5.194313929642153 | 97.061 | -11.452 | 1.0 |
| 5.4761097655071005 | 201.127 | -15.761 | 0.0 |
| 5.194024058998781 | 170.826 | -6.644 | 0.0 |
| 5.3187098198626455 | 200.082 | -6.721 | 0.0 |
| 5.216670936098296 | 86.813 | -12.724 | 1.0 |
| 5.433901812715114 | 110.794 | -4.318 | 1.0 |
| 5.150316537563171 | 186.188 | -3.021 | 0.0 |
| 5.874479927008053 | 85.333 | -5.563 | 1.0 |
| 5.513347896122512 | 100.014 | -8.775 | 1.0 |
| 5.814891615261906 | 110.284 | -13.965 | 1.0 |
| 5.887100007523505 | 86.721 | -8.199 | 1.0 |
| 5.704357675862931 | 155.973 | -11.175 | 0.0 |
| 5.355644132882576 | 71.137 | -3.495 | 1.0 |
| 5.730638858425511 | 106.163 | -9.899 | 1.0 |
| 5.739836062373653 | 100.074 | -7.242 | 1.0 |
| 4.084327895835447 | 105.908 | -10.25 | 1.0 |
| 4.929932413000636 | 178.874 | -4.06 | 0.0 |
| 6.173240788577892 | 125.038 | -10.784 | 1.0 |
| 5.38421584950076 | 84.995 | -1.375 | 1.0 |
| 5.471946293334769 | 159.676 | -12.258 | 0.0 |
| 5.6278837410715505 | 134.777 | -7.182 | 0.0 |
| 5.439361356314339 | 163.879 | -11.664 | 0.0 |
| 5.472714566733009 | 144.105 | -11.073 | 0.0 |
| 5.238538964711458 | 197.982 | -8.41 | 0.0 |
| 5.353421649883508 | 131.86 | -8.159 | 0.0 |
| 4.894969041473027 | 104.252 | -11.052 | 1.0 |
| 5.593771532879778 | 96.243 | -13.505 | 1.0 |
| 5.629198035163789 | 157.475 | -7.023 | 0.0 |
| 5.541290564401247 | 242.155 | -4.502 | 0.0 |
| 5.305959292509338 | 95.283 | -13.174 | 1.0 |
| 5.5601648436577875 | 95.693 | -10.348 | 1.0 |
| 6.022740611873921 | 90.211 | -20.61 | 1.0 |
| 5.528403124883772 | 102.96 | -8.022 | 1.0 |
| 5.3715505418928675 | 146.009 | -8.978 | 0.0 |
| 5.045006188946529 | 98.712 | -9.802 | 1.0 |
| 3.8275305623499376 | 136.001 | -4.704 | 0.0 |
| 5.123455814595105 | 99.085 | -6.819 | 1.0 |
| 5.328769168005321 | 92.969 | -5.793 | 1.0 |
| 5.450864833344368 | 136.07 | -7.055 | 0.0 |
| 5.488929089755756 | 159.955 | -6.646 | 0.0 |
| 5.290941028132201 | 167.109 | -18.289 | 0.0 |
| 5.284870004703428 | 121.731 | -13.675 | 1.0 |
| 6.000661797437479 | 79.956 | -13.695 | 1.0 |
| 5.365218298120349 | 162.8 | -5.875 | 0.0 |
| 5.544971563912613 | 144.994 | -5.085 | 0.0 |
| 5.7510280497364406 | 99.958 | -10.789 | 1.0 |
| 5.544665321179308 | 163.354 | -6.879 | 0.0 |
| 5.3403576586423345 | 85.97 | -9.445 | 1.0 |
| 6.684349905895599 | 121.502 | -26.048 | 1.0 |
| 5.636302886150193 | 132.04 | -4.807 | 0.0 |
| 5.6588689266910235 | 108.959 | -7.65 | 1.0 |
| 5.541085686205205 | 97.965 | -14.568 | 1.0 |
| 5.974836133162007 | 110.274 | -3.67 | 1.0 |
| 5.806440832772488 | 103.119 | -13.376 | 1.0 |
| 6.131715174405037 | 76.523 | -24.41 | 1.0 |
| 5.1625090980936195 | 80.727 | -14.976 | 1.0 |
| 5.611784868881943 | 131.994 | -15.196 | 0.0 |
| 5.401799789693681 | 69.275 | -10.303 | 1.0 |
| 5.6151206090866115 | 170.048 | -4.124 | 0.0 |
| 5.69086523855581 | 105.556 | -14.885 | 1.0 |
| 5.155151136154312 | 120.827 | -7.96 | 1.0 |
| 5.024779393055371 | 135.828 | -6.784 | 0.0 |
| 5.286192911498709 | 130.468 | -21.354 | 1.0 |
| 6.026909390629277 | 126.015 | -9.087 | 1.0 |
| 5.182211997821176 | 127.867 | -8.983 | 1.0 |
| 5.258308999276678 | 123.01 | -10.522 | 1.0 |
| 5.077942665926383 | 133.784 | -7.888 | 0.0 |
| 5.467875651544134 | 56.4 | -17.921 | 1.0 |
| 5.490223593289953 | 88.975 | -6.627 | 1.0 |
| 5.538931730551451 | 120.05 | -6.744 | 1.0 |
| 5.793310850788608 | 81.411 | -13.636 | 1.0 |
| 6.270850218148435 | 128.053 | -11.982 | 1.0 |
| 5.410477154675121 | 120.752 | -14.078 | 1.0 |
| 6.113269307001353 | 159.989 | -8.969 | 0.0 |
| 6.128702349999979 | 126.998 | -9.481 | 1.0 |
| 5.883323391273292 | 97.304 | -6.351 | 1.0 |
| 5.209844940928293 | 114.727 | -7.032 | 1.0 |
| 4.333687525014068 | 195.044 | -18.68 | 0.0 |
| 5.171000793293108 | 71.14 | -9.353 | 1.0 |
| 5.436749033194309 | 149.868 | -6.009 | 0.0 |
| 4.97928895783396 | 115.552 | -10.938 | 1.0 |
| 5.77911821422353 | 114.132 | -19.171 | 1.0 |
| 5.1254761688016615 | 140.422 | -14.278 | 0.0 |
| 5.659597261406425 | 182.034 | -4.588 | 0.0 |
| 5.179715120430374 | 150.962 | -7.288 | 0.0 |
| 5.735543316647046 | 132.017 | -9.809 | 0.0 |
| 5.762260448974631 | 101.048 | -17.318 | 1.0 |
| 5.68856923797772 | 146.001 | -4.621 | 0.0 |
| 5.3433587987078415 | 92.99 | -8.994 | 1.0 |
| 5.8521354947724085 | 125.933 | -11.969 | 1.0 |
| 5.314349167717073 | 175.157 | -5.746 | 0.0 |
| 4.84032586198911 | 85.65 | -12.166 | 1.0 |
| 5.094893657240654 | 145.501 | -10.071 | 0.0 |
| 5.699122500489121 | 137.842 | -6.325 | 0.0 |
| 1.806004196311159 | 0.0 | -12.711 | 1.0 |
| 5.194024058998781 | 127.951 | -4.831 | 1.0 |
| 5.464673526949484 | 88.954 | -19.225 | 1.0 |
| 5.592215106652138 | 191.122 | -7.913 | 0.0 |
| 5.925707203507987 | 126.01 | -7.652 | 1.0 |
| 5.0889531304187505 | 111.985 | -16.016 | 1.0 |
| 5.871023641818345 | 199.853 | -5.702 | 0.0 |
| 3.359906179859155 | 75.705 | -13.318 | 1.0 |
| 5.469307759160471 | 119.99 | -13.474 | 1.0 |
| 5.075660634218234 | 121.229 | -26.343 | 1.0 |
| 5.51881068910637 | 142.751 | -15.649 | 0.0 |
| 5.742017477797979 | 153.876 | -11.65 | 0.0 |
| 5.588313409703403 | 86.415 | -12.559 | 1.0 |
| 5.853260913688496 | 142.404 | -6.016 | 0.0 |
| 5.642617315108899 | 192.017 | -8.033 | 0.0 |
| 5.514716424815377 | 137.923 | -1.57 | 0.0 |
| 5.646313165434467 | 112.86 | -24.362 | 1.0 |
| 5.367902242765602 | 100.017 | -6.444 | 1.0 |
| 5.079569452748531 | 119.939 | -10.924 | 1.0 |
| 5.28592849035852 | 106.065 | -9.715 | 1.0 |
| 5.1000030038631206 | 143.285 | -7.32 | 0.0 |
| 5.662868184210141 | 94.1 | -13.102 | 1.0 |
| 5.3397312978391485 | 149.98 | -7.199 | 0.0 |
| 5.6135017591989005 | 98.375 | -7.397 | 1.0 |
| 5.72885716058872 | 131.971 | -7.201 | 0.0 |
| 5.69086523855581 | 80.249 | -6.368 | 1.0 |
| 3.9840351543063433 | 47.267 | -24.92 | 1.0 |
| 5.312290486214457 | 102.06 | -5.513 | 1.0 |
| 5.659870224098342 | 171.523 | -9.491 | 0.0 |
| 5.196630096256507 | 112.52 | -22.935 | 1.0 |
| 5.4739206562390805 | 110.972 | -15.204 | 1.0 |
| 5.413508200907366 | 121.09 | -3.423 | 1.0 |
| 4.496806944435535 | 119.976 | -21.471 | 1.0 |
| 5.6077672663776985 | 75.419 | -16.783 | 1.0 |
| 5.276628504428926 | 67.183 | -17.95 | 1.0 |
| 5.820408544989948 | 120.628 | -12.015 | 1.0 |
| 5.90833013587393 | 110.021 | -7.56 | 1.0 |
| 5.474139766165444 | 68.242 | -8.931 | 1.0 |
| 5.654670658762399 | 97.449 | -7.023 | 1.0 |
| 4.702389088476283 | 117.524 | -15.236 | 1.0 |
| 5.221195796527836 | 142.009 | -5.467 | 0.0 |
| 5.558957838446139 | 100.078 | -16.375 | 1.0 |
| 5.532855156274038 | 133.005 | -5.409 | 0.0 |
| 6.406946584511655 | 130.966 | -5.15 | 0.0 |
| 5.286985856806291 | 99.735 | -6.961 | 1.0 |
| 5.650454726204791 | 157.764 | -7.415 | 0.0 |
| 5.966969938663496 | 116.631 | -19.261 | 1.0 |
| 5.491516423250961 | 105.498 | -11.555 | 1.0 |
| 6.15494206076712 | 122.988 | -6.45 | 1.0 |
| 5.272884218579013 | 123.695 | -7.73 | 1.0 |
| 5.303363563055256 | 91.859 | -10.302 | 1.0 |
| 5.825972289884403 | 120.975 | -7.997 | 1.0 |
| 5.35082247888753 | 109.04 | -7.087 | 1.0 |
| 5.276361541785093 | 70.659 | -9.568 | 1.0 |
| 5.3404828741502754 | 178.45 | -4.755 | 0.0 |
| 5.818624686622703 | 127.309 | -9.626 | 1.0 |
| 4.152734689413843 | 81.537 | -10.999 | 1.0 |
| 5.728772213831804 | 70.392 | -4.359 | 1.0 |
| 4.979827847289115 | 102.228 | -8.359 | 1.0 |
| 5.7723929130249285 | 178.003 | -6.501 | 0.0 |
| 5.302323381743563 | 130.182 | -6.988 | 1.0 |
| 5.6789751004809705 | 106.006 | -3.538 | 1.0 |
| 4.630666378060994 | 114.869 | -7.877 | 1.0 |
| 5.48871319681963 | 151.234 | -8.228 | 0.0 |
| 5.378686990009553 | 69.781 | -10.723 | 1.0 |
| 5.8151253204408455 | 149.834 | -5.619 | 0.0 |
| 5.447945090356273 | 91.234 | -3.797 | 1.0 |
| 5.255859033956585 | 160.063 | -9.434 | 0.0 |
| 5.667032248668259 | 155.903 | -7.195 | 0.0 |
| 6.193721177492078 | 124.97 | -6.011 | 1.0 |
| 5.432988983869239 | 129.021 | -5.115 | 1.0 |
| 5.730808363040245 | 65.2 | -12.336 | 1.0 |
| 5.4532168516649095 | 149.567 | -7.211 | 0.0 |
| 5.902922007577961 | 119.583 | -12.599 | 1.0 |
| 5.364485091062758 | 149.641 | -5.086 | 0.0 |
| 5.22990500510395 | 141.041 | -15.369 | 0.0 |
| 5.74878302284854 | 126.157 | -10.84 | 1.0 |
| 5.4470449967586525 | 146.513 | -11.084 | 0.0 |
| 5.29919633014633 | 90.942 | -17.839 | 1.0 |
| 5.530062022086704 | 139.0 | -8.338 | 0.0 |
| 5.548028766220414 | 120.103 | -7.479 | 1.0 |
| 5.233534762291271 | 138.964 | -7.313 | 0.0 |
| 6.019697811017462 | 109.364 | -13.932 | 1.0 |
| 5.543132777446875 | 150.946 | -13.532 | 0.0 |
| 5.2600746989522325 | 129.349 | -5.46 | 1.0 |
| 5.138892996898216 | 68.997 | -18.497 | 1.0 |
| 4.631938885919433 | 119.993 | -12.572 | 1.0 |
| 5.9872422708317945 | 144.006 | -3.026 | 0.0 |
| 6.017536890358929 | 160.061 | -2.812 | 0.0 |
| 5.682361338280549 | 151.912 | -10.075 | 0.0 |
| 5.0334981361160445 | 77.19 | -10.41 | 1.0 |
| 5.026152083629726 | 128.743 | -7.801 | 1.0 |
| 6.016199866103495 | 100.303 | -12.487 | 1.0 |
| 5.68795016919094 | 133.272 | -8.348 | 0.0 |
| 5.476000432155799 | 95.321 | -4.322 | 1.0 |
| 4.7352630988107265 | 114.697 | -4.575 | 1.0 |
| 5.847621139373283 | 90.653 | -7.491 | 1.0 |
| 5.137666749490677 | 143.18 | -11.579 | 0.0 |
| 5.660415995679294 | 149.604 | -12.398 | 0.0 |
| 5.269260268431927 | 86.524 | -16.072 | 1.0 |
| 5.575724721914124 | 92.492 | -6.912 | 1.0 |
| 4.843829936073812 | 93.818 | -13.681 | 1.0 |
| 5.516608162267177 | 160.037 | -17.505 | 0.0 |
| 5.354039496648361 | 91.21 | -11.495 | 1.0 |
| 6.126251307678329 | 85.668 | -29.17 | 1.0 |
| 5.52538935145276 | 88.999 | -7.108 | 1.0 |
| 5.062519840326649 | 172.201 | -4.75 | 0.0 |
| 6.546896021572621 | 175.921 | -14.61 | 0.0 |
| 5.330541303481353 | 100.578 | -13.527 | 1.0 |
| 5.436976483643578 | 134.374 | -15.897 | 0.0 |
| 5.559561503907327 | 70.53 | -20.149 | 1.0 |
| 5.312419265723114 | 84.159 | -5.629 | 1.0 |
| 5.301282117327987 | 94.965 | -8.903 | 1.0 |
| 5.65283982054155 | 132.86 | -10.23 | 0.0 |
| 5.467875651544134 | 95.224 | -3.903 | 1.0 |
| 5.485902090214787 | 99.96 | -5.828 | 1.0 |
| 5.505309787728392 | 119.988 | -7.907 | 1.0 |
| 5.326612992677677 | 110.175 | -12.853 | 1.0 |
| 5.4400416865479375 | 193.315 | -9.28 | 0.0 |
| 5.689011180946999 | 82.231 | -10.715 | 1.0 |
| 5.230324459215954 | 111.427 | -10.227 | 1.0 |
| 4.656557165987008 | 100.884 | -13.622 | 1.0 |
| 5.811300920307404 | 96.936 | -12.364 | 1.0 |
| 5.272080007701286 | 178.346 | -9.121 | 0.0 |
| 5.90413425563417 | 118.954 | -11.949 | 1.0 |
| 5.623930390479518 | 93.039 | -9.164 | 1.0 |
| 6.035007636090424 | 98.209 | -7.722 | 1.0 |
| 5.172927427877826 | 113.95 | -11.716 | 1.0 |
| 5.409075115901 | 130.816 | -7.251 | 0.0 |
| 5.586748495886647 | 58.68 | -13.852 | 1.0 |
| 5.53749315203344 | 174.995 | -6.466 | 0.0 |
| 5.982509141902109 | 139.895 | -7.423 | 0.0 |
| 5.654579205170516 | 197.992 | -7.655 | 0.0 |
| 4.9389533818061935 | 190.038 | -6.257 | 0.0 |
| 5.696757691877665 | 141.007 | -24.066 | 0.0 |
| 5.139811725963339 | 139.774 | -5.324 | 0.0 |
The new clustering model makes much more sense. Songs with high tempo and loudness are put in one cluster and song duration does not affect song categories.
To really understand how the points in 3D behave you need to see them in 3D interactively and understand the limits of its three 2D projections. For this let us spend some time and play in sageMath Worksheet in CoCalc (it is free for light-weight use and perhaps worth the 7 USD a month if you need more serious computing in mathmeatics, statistics, etc. in multiple languages!).
Let us take a look at this sageMath Worksheet published here:
- https://cocalc.com/share/ee9392a2-c83b-4eed-9468-767bb90fd12a/3DEuclideanSpace_1MSongsKMeansClustering.sagews?viewer=share
- and the accompanying datasets (downloaded from the
displays in this notebook and uploaded to CoCalc as CSV files):- https://cocalc.com/projects/ee9392a2-c83b-4eed-9468-767bb90fd12a/files/KMeansClusters10003DFeaturesloudness-tempologDurationOf1MSongsKMeansfor015sds2-2.csv
- https://cocalc.com/projects/ee9392a2-c83b-4eed-9468-767bb90fd12a/files/KMeansClusters10003DFeaturesloudness-tempoDurationOf1MSongsKMeansfor015sds2-2.csv
The point of the above little example is that you need to be able to tell a sensible story with your data science process and not just blindly apply a heuristic, but highly scalable, algorithm which depends on the notion of nearest neighborhoods defined by the metric (Euclidean distances in 3-dimensional real-valued spaces in this example) induced by the features you have engineered or have the power to re/re/...-engineer to increase the meaningfullness of the problem at hand.
Determining Optimal K
There are methods to find the optimal number of clusters. This partly depends on the purpose of the unsupervised learning task.
See here for some metrics in scala for the Irish dataset.
Supervised Clustering with Decision Trees
Visual Introduction to decision trees and application to hand-written digit recognition
SOURCE: This is just a couple of decorations on a notebook published in databricks community edition in 2016.
Decision Trees for handwritten digit recognition
This notebook demonstrates learning a Decision Tree using Spark's distributed implementation. It gives the reader a better understanding of some critical hyperparameters for the tree learning algorithm, using examples to demonstrate how tuning the hyperparameters can improve accuracy.
Background: To learn more about Decision Trees, check out the resources at the end of this notebook. The visual description of ML and Decision Trees provides nice intuition helpful to understand this notebook, and Wikipedia gives lots of details.
Data: We use the classic MNIST handwritten digit recognition dataset. It is from LeCun et al. (1998) and may be found under "mnist" at the LibSVM dataset page.
Goal: Our goal for our data is to learn how to recognize digits (0 - 9) from images of handwriting. However, we will focus on understanding trees, not on this particular learning problem.
Takeaways: Decision Trees take several hyperparameters which can affect the accuracy of the learned model. There is no one "best" setting for these for all datasets. To get the optimal accuracy, we need to tune these hyperparameters based on our data.
Let's Build Intuition for Learning with Decision Trees
- Right-click and open the following link in a new Tab:
- The visual description of ML and Decision Trees which was nominated for a NSF Vizzie award.
Load MNIST training and test datasets
Our datasets are vectors of pixels representing images of handwritten digits. For example:

These datasets are stored in the popular LibSVM dataset format. We will load them using MLlib's LibSVM dataset reader utility.
//-----------------------------------------------------------------------------------------------------------------
// using RDD-based MLlib - ok for Spark 1.x
// MLUtils.loadLibSVMFile returns an RDD.
//import org.apache.spark.mllib.util.MLUtils
//val trainingRDD = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
//val testRDD = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-test.txt")
// We convert the RDDs to DataFrames to use with ML Pipelines.
//val training = trainingRDD.toDF()
//val test = testRDD.toDF()
// Note: In Spark 1.6 and later versions, Spark SQL has a LibSVM data source. The above lines can be simplified to:
//// val training = sqlContext.read.format("libsvm").load("/mnt/mllib/mnist-digits-csv/mnist-digits-train.txt")
//// val test = sqlContext.read.format("libsvm").load("/mnt/mllib/mnist-digits-csv/mnist-digits-test.txt")
//-----------------------------------------------------------------------------------------------------------------
val training = spark.read.format("libsvm")
.option("numFeatures", "780")
.load("/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
val test = spark.read.format("libsvm")
.option("numFeatures", "780")
.load("/databricks-datasets/mnist-digits/data-001/mnist-digits-test.txt")
// Cache data for multiple uses.
training.cache()
test.cache()
println(s"We have ${training.count} training images and ${test.count} test images.")
We have 60000 training images and 10000 test images.
training: org.apache.spark.sql.DataFrame = [label: double, features: vector]
test: org.apache.spark.sql.DataFrame = [label: double, features: vector]
Display our data. Each image has the true label (the label column) and a vector of features which represent pixel intensities (see below for details of what is in training).
training.printSchema()
root
|-- label: double (nullable = true)
|-- features: vector (nullable = true)
training.show(3) // replace 'true' by 'false' to see the whole row hidden by '...'
+-----+--------------------+
|label| features|
+-----+--------------------+
| 5.0|(780,[152,153,154...|
| 0.0|(780,[127,128,129...|
| 4.0|(780,[160,161,162...|
+-----+--------------------+
only showing top 3 rows
display(training) // this is databricks-specific for interactive visual convenience
The pixel intensities are represented in features as a sparse vector, for example the first observation, as seen in row 1 of the output to display(training) or training.show(2,false) above, has label as 5, i.e. the hand-written image is for the number 5. And this hand-written image is the following sparse vector (just click the triangle to the left of the feature in first row to see the following):
type: 0
size: 780
indices: [152,153,155,...,682,683]
values: [3, 18, 18,18,126,...,132,16]
Here,
type: 0says we have a sparse vector that only represents non-zero entries (as opposed to a dense vector where every entry is represented).size: 780says the vector has 780 indices in total- these indices from 0,...,779 are a unidimensional indexing of the two-dimensional array of pixels in the image
indices: [152,153,155,...,682,683]are the indices from the[0,1,...,779]possible indices with non-zero values- a value is an integer encoding the gray-level at the pixel index
values: [3, 18, 18,18,126,...,132,16]are the actual gray level values, for example:- at pixed index
152the gray-level value is3, - at index
153the gray-level value is18, - ..., and finally at
- at index
683the gray-level value is18
- at pixed index
Train a Decision Tree
We begin by training a decision tree using the default settings. Before training, we want to tell the algorithm that the labels are categories 0-9, rather than continuous values. We use the StringIndexer class to do this. We tie this feature preprocessing together with the tree algorithm using a Pipeline. ML Pipelines are tools Spark provides for piecing together Machine Learning algorithms into workflows. To learn more about Pipelines, check out other ML example notebooks in Databricks and the ML Pipelines user guide. Also See mllib-decision-tree.html#basic-algorithm.
// Import the ML algorithms we will use.
import org.apache.spark.ml.classification.{DecisionTreeClassifier, DecisionTreeClassificationModel}
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.{DecisionTreeClassifier, DecisionTreeClassificationModel}
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.ml.Pipeline
// StringIndexer: Read input column "label" (digits) and annotate them as categorical values.
val indexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel")
// DecisionTreeClassifier: Learn to predict column "indexedLabel" using the "features" column.
val dtc = new DecisionTreeClassifier().setLabelCol("indexedLabel")
// Chain indexer + dtc together into a single ML Pipeline.
val pipeline = new Pipeline().setStages(Array(indexer, dtc))
indexer: org.apache.spark.ml.feature.StringIndexer = strIdx_5e4c4bafd052
dtc: org.apache.spark.ml.classification.DecisionTreeClassifier = dtc_6916f99c66b5
pipeline: org.apache.spark.ml.Pipeline = pipeline_7c1c5306008c
Now, let's fit a model to our data.
val model = pipeline.fit(training)
model: org.apache.spark.ml.PipelineModel = pipeline_7c1c5306008c
We can inspect the learned tree by displaying it using Databricks ML visualization. (Visualization is available for several but not all models.)
// The tree is the last stage of the Pipeline. Display it!
val tree = model.stages.last.asInstanceOf[DecisionTreeClassificationModel]
display(tree)
| treeNode |
|---|
| {"index":31,"featureType":"continuous","prediction":null,"threshold":141.5,"categories":null,"feature":350,"overflow":false} |
| {"index":15,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":568,"overflow":false} |
| {"index":7,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":430,"overflow":false} |
| {"index":3,"featureType":"continuous","prediction":null,"threshold":2.5,"categories":null,"feature":405,"overflow":false} |
| {"index":1,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":484,"overflow":false} |
| {"index":0,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":2,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":5,"featureType":"continuous","prediction":null,"threshold":14.5,"categories":null,"feature":516,"overflow":false} |
| {"index":4,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":6,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":11,"featureType":"continuous","prediction":null,"threshold":7.5,"categories":null,"feature":211,"overflow":false} |
| {"index":9,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":98,"overflow":false} |
| {"index":8,"featureType":null,"prediction":8.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":10,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":13,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":156,"overflow":false} |
| {"index":12,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":14,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":23,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":435,"overflow":false} |
| {"index":19,"featureType":"continuous","prediction":null,"threshold":10.5,"categories":null,"feature":489,"overflow":false} |
| {"index":17,"featureType":"continuous","prediction":null,"threshold":12.5,"categories":null,"feature":351,"overflow":false} |
| {"index":16,"featureType":null,"prediction":5.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":18,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":21,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":320,"overflow":false} |
| {"index":20,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":22,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":27,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":347,"overflow":false} |
| {"index":25,"featureType":"continuous","prediction":null,"threshold":1.5,"categories":null,"feature":344,"overflow":false} |
| {"index":24,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":26,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":29,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":655,"overflow":false} |
| {"index":28,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":30,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":47,"featureType":"continuous","prediction":null,"threshold":44.5,"categories":null,"feature":489,"overflow":false} |
| {"index":39,"featureType":"continuous","prediction":null,"threshold":41.5,"categories":null,"feature":290,"overflow":false} |
| {"index":35,"featureType":"continuous","prediction":null,"threshold":77.5,"categories":null,"feature":486,"overflow":false} |
| {"index":33,"featureType":"continuous","prediction":null,"threshold":113.5,"categories":null,"feature":490,"overflow":false} |
| {"index":32,"featureType":null,"prediction":2.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":34,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":37,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":656,"overflow":false} |
| {"index":36,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":38,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":43,"featureType":"continuous","prediction":null,"threshold":5.5,"categories":null,"feature":297,"overflow":false} |
| {"index":41,"featureType":"continuous","prediction":null,"threshold":178.5,"categories":null,"feature":486,"overflow":false} |
| {"index":40,"featureType":null,"prediction":9.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":42,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":45,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":514,"overflow":false} |
| {"index":44,"featureType":null,"prediction":4.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":46,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":55,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":234,"overflow":false} |
| {"index":51,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":402,"overflow":false} |
| {"index":49,"featureType":"continuous","prediction":null,"threshold":5.5,"categories":null,"feature":300,"overflow":false} |
| {"index":48,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":50,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":53,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":103,"overflow":false} |
| {"index":52,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":54,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":59,"featureType":"continuous","prediction":null,"threshold":0.5,"categories":null,"feature":658,"overflow":false} |
| {"index":57,"featureType":"continuous","prediction":null,"threshold":24.5,"categories":null,"feature":345,"overflow":false} |
| {"index":56,"featureType":null,"prediction":3.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":58,"featureType":null,"prediction":6.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":60,"featureType":null,"prediction":7.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
Above, we can see how the tree makes predictions. When classifying a new example, the tree starts at the "root" node (at the top). Each tree node tests a pixel value and goes either left or right. At the bottom "leaf" nodes, the tree predicts a digit as the image's label.
Hyperparameter Tuning
Run the next cell and come back into hyper-parameter tuning for a couple minutes.
Exploring "maxDepth": training trees of different sizes
In this section, we test tuning a single hyperparameter maxDepth, which determines how deep (and large) the tree can be. We will train trees at varying depths and see how it affects the accuracy on our held-out test set.
Note: The next cell can take about 1 minute to run since it is training several trees which get deeper and deeper.
val variedMaxDepthModels = (0 until 8).map { maxDepth =>
// For this setting of maxDepth, learn a decision tree.
dtc.setMaxDepth(maxDepth)
// Create a Pipeline with our feature processing stage (indexer) plus the tree algorithm
val pipeline = new Pipeline().setStages(Array(indexer, dtc))
// Run the ML Pipeline to learn a tree.
pipeline.fit(training)
}
variedMaxDepthModels: scala.collection.immutable.IndexedSeq[org.apache.spark.ml.PipelineModel] = Vector(pipeline_310f2d2f2acc, pipeline_cf295c016b8e, pipeline_54e998e43742, pipeline_043069dd1cac, pipeline_d1c3a168b215, pipeline_7440ffd2443d, pipeline_ea992a1dcc18, pipeline_048b83baaf8e)
We will use the default metric to evaluate the performance of our classifier:
// Define an evaluation metric. In this case, we will use "accuracy".
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
val evaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setMetricName("f1") // default MetricName
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
evaluator: org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator = MulticlassClassificationEvaluator: uid=mcEval_763f80cd3db6, metricName=f1, metricLabel=0.0, beta=1.0, eps=1.0E-15
// For each maxDepth setting, make predictions on the test data, and compute the classifier's f1 performance metric.
val f1MetricPerformanceMeasures = (0 until 8).map { maxDepth =>
val model = variedMaxDepthModels(maxDepth)
// Calling transform() on the test set runs the fitted pipeline.
// The learned model makes predictions on each test example.
val predictions = model.transform(test)
// Calling evaluate() on the predictions DataFrame computes our performance metric.
(maxDepth, evaluator.evaluate(predictions))
}.toDF("maxDepth", "f1")
f1MetricPerformanceMeasures: org.apache.spark.sql.DataFrame = [maxDepth: int, f1: double]
We can display our accuracy results and see immediately that deeper, larger trees are more powerful classifiers, achieving higher accuracies.
Note: When you run f1MetricPerformanceMeasures.show(), you will get a table with f1 score getting better (i.e., approaching 1) with depth.
f1MetricPerformanceMeasures.show()
+--------+-------------------+
|maxDepth| f1|
+--------+-------------------+
| 0| 0.023138302649304|
| 1|0.07724539080137968|
| 2|0.21450119544051488|
| 3| 0.4328937409827488|
| 4| 0.5851464721123918|
| 5| 0.680614417707604|
| 6| 0.752106383232933|
| 7| 0.7875867636754111|
+--------+-------------------+
Even though deeper trees are more powerful, they are not always better (recall from the SF/NYC city classification from house features at The visual description of ML and Decision Trees). If we kept increasing the depth on a rich enough dataset, training would take longer and longer. We also might risk overfitting (fitting the training data so well that our predictions get worse on test data); it is important to tune parameters based on held-out data to prevent overfitting. This will ensure that the fitted model generalizes well to yet unseen data, i.e. minimizes generalization error in a mathematical statistical sense.
Exploring "maxBins": discretization for efficient distributed computing
This section explores a more expert-level setting maxBins. For efficient distributed training of Decision Trees, Spark and most other libraries discretize (or "bin") continuous features (such as pixel values) into a finite number of values. This is an important step for the distributed implementation, but it introduces a tradeoff: Larger maxBins mean your data will be more accurately represented, but it will also mean more communication (and slower training).
The default value of maxBins generally works, but it is interesting to explore on our handwritten digit dataset. Remember our digit image from above:

It is grayscale. But if we set maxBins = 2, then we are effectively making it a black-and-white image, not grayscale. Will that affect the accuracy of our model? Let's see!
Note: The next cell can take about 35 seconds to run since it trains several trees. Read the details on maxBins at mllib-decision-tree.html#split-candidates.
dtc.setMaxDepth(6) // Set maxDepth to a reasonable value.
// now try the maxBins "hyper-parameter" which actually acts as a "coarsener"
// mathematical researchers should note that it is a sub-algebra of the finite
// algebra of observable pixel images at the finest resolution available to us
// giving a compression of the image to fewer coarsely represented pixels
val f1MetricPerformanceMeasures = Seq(2, 4, 8, 16, 32).map { case maxBins =>
// For this value of maxBins, learn a tree.
dtc.setMaxBins(maxBins)
val pipeline = new Pipeline().setStages(Array(indexer, dtc))
val model = pipeline.fit(training)
// Make predictions on test data, and compute accuracy.
val predictions = model.transform(test)
(maxBins, evaluator.evaluate(predictions))
}.toDF("maxBins", "f1")
f1MetricPerformanceMeasures: org.apache.spark.sql.DataFrame = [maxBins: int, f1: double]
f1MetricPerformanceMeasures.show()
+-------+------------------+
|maxBins| f1|
+-------+------------------+
| 2|0.7400788627816512|
| 4|0.7389031841129898|
| 8|0.7442844661661937|
| 16|0.7447853737964406|
| 32| 0.752106383232933|
+-------+------------------+
We can see that extreme discretization (black and white) hurts performance as measured by F1-error, but only a bit. Using more bins increases the accuracy (but also makes learning more costly).
What's next?
- Explore: Try out tuning other parameters of trees---or even ensembles like Random Forests or Gradient-Boosted Trees.
- Automated tuning: This type of tuning does not have to be done by hand. (We did it by hand here to show the effects of tuning in detail.) MLlib provides automated tuning functionality via
CrossValidator. Check out the other Databricks ML Pipeline guides or the Spark ML user guide for details.
Resources
If you are interested in learning more on these topics, these resources can get you started:
- Excellent visual description of Machine Learning and Decision Trees
- This gives an intuitive visual explanation of ML, decision trees, overfitting, and more.
- Blog post on MLlib Random Forests and Gradient-Boosted Trees
- Random Forests and Gradient-Boosted Trees combine many trees into more powerful ensemble models. This is the original post describing MLlib's forest and GBT implementations.
- Wikipedia
Linear Algebra Review / re-Introduction
by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning
This is a breeze'y scalarific break-down of:
- Ameet's Linear Algebra Review in CS190.1x Scalable Machine Learning Course that is archived in edX from 2015 and
- Home Work: read this! https://github.com/scalanlp/breeze/wiki/Quickstart
Using the above resources we'll provide a review of basic linear algebra concepts that will recur throughout the course. These concepts include:
- Matrices
- Vectors
- Arithmetic operations with vectors and matrices
We will see the accompanying Scala computations in the local or non-distributed setting.
Let's get a quick visual geometric interpretation for vectors, matrices and matrix-vector multiplications, eigen systems with real and comples Eigen values NOW from the following interactive visual-cognitive aid at:
- http://setosa.io/ev/eigenvectors-and-eigenvalues/ just focus on geometric interpretation of vectors and matrices in Cartesian coordinates.
Matrix by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning
(watch now 0-61 seconds):
Breeze is a linear algebra package in Scala. First, let us import it as follows:
import breeze.linalg._
import breeze.linalg._
1. Matrix: creation and element-access
A matrix is a two-dimensional array.
Let us denote matrices via bold uppercase letters as follows:
For instance, the matrix below is denoted with \(\mathbf{A}\), a capital bold A.
\[ \mathbf{A} = \begin{pmatrix} a_{11} & a_{12} & a_{13} \ a_{21} & a_{22} & a_{23} \ a_{31} & a_{32} & a_{33} \end{pmatrix} \]
We usually put commas between the row and column indexing sub-scripts, to make the possibly multi-digit indices distinguishable as follows:
\[ \mathbf{A} = \begin{pmatrix} a_{1,1} & a_{1,2} & a_{1,3} \ a_{2,1} & a_{2,2} & a_{2,3} \ a_{3,1} & a_{3,2} & a_{3,3} \end{pmatrix} \]
- \(\mathbf{A}_{i,j}\) denotes the entry in \(i\)-th row and \(j\)-th column of the matrix \(\mathbf{A}\).
- So for instance,
- the first entry, the top left entry, is denoted by \(\mathbf{A}_{1,1}\).
- And the entry in the third row and second column is denoted by \(\mathbf{A}_{3,2}\).
- We say that a matrix with n rows and m columns is an \(n\) by \(m\) matrix and written as \(n \times m\)
- The matrix \(\mathbf{A}\) shown above is a generic \(3 \times 3\) (pronounced 3-by-3) matrix.
- And the matrix in Ameet's example in the video above, having 4 rows and 3 columns, is a 4 by 3 matrix.
- If a matrix \(\mathbf{A}\) is \(n \times m\), we write:
- \(\mathbf{A} \in \mathbb{R}^{n \times m}\) and say that \(\mathbf{A}\) is an \(\mathbb{R}\) to the power of the n times m,
- where, \(\mathbb{R}\) here denotes the set of all real numbers in the line given by the open interval: \((-\infty,+\infty)\).
- \(\mathbf{A} \in \mathbb{R}^{n \times m}\) and say that \(\mathbf{A}\) is an \(\mathbb{R}\) to the power of the n times m,
Let us created a matrix A as a val (that is immutable) in scala. The matrix we want to create is mathematically notated as follows:
\[ \mathbf{A} = \begin{pmatrix} a_{1,1} & a_{1,2} & a_{1,3} \ a_{2,1} & a_{2,2} & a_{2,3} \end{pmatrix}
\begin{pmatrix} 1 & 2 & 3 \ 4 & 5 & 6 \end{pmatrix} \]
val A = DenseMatrix((1, 2, 3), (4, 5, 6)) // let's create this 2 by 3 matrix
A: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
A.size
res0: Int = 6
A.rows // number of rows
res1: Int = 2
A.size / A.rows // num of columns
res2: Int = 3
A.cols // also say
res3: Int = 3
Now, let's access the element \(a_{1,1}\), i.e., the element from the first row and first column of \(\mathbf{A}\), which in our val A matrix is the integer of type Int equalling 1.
A(0, 0) // Remember elements are indexed by zero in scala
res4: Int = 1
Gotcha: indices in breeze matrices start at 0 as in numpy of python and not at 1 as in MATLAB!
Of course if you assign the same dense matrix to a mutable var B then its entries can be modified as follows:
var B = DenseMatrix((1, 2, 3), (4, 5, 6))
B: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
B(0,0)=999; B(1,1)=969; B(0,2)=666
B
res5: breeze.linalg.DenseMatrix[Int] =
999 2 666
4 969 6
-
A vector is a matrix with many rows and one column.
-
We'll denote a vector by bold lowercase letters: \[\mathbf{a} = \begin{pmatrix} 3.3 \ 1.0 \ 6.3 \ 3.6 \end{pmatrix}\]
So, the vector above is denoted by \(\mathbf{a}\), the lowercase, bold a.
-
\(a_i\) denotes the i-th entry of a vector. So for instance:
- \(a_2\) denotes the second entry of the vector and it is 1.0 for our vector.
-
If a vector is m-dimensional, then we say that \(\mathbf{a}\) is in \(\mathbb{R}^m\) and write \(\mathbf{a} \in \ \mathbb{R}^m\).
- So our \(\mathbf{a} \in \ \mathbb{R}^4\).
val a = DenseVector(3.3, 1.0, 6.3, 3.6) // these are row vectors
a: breeze.linalg.DenseVector[Double] = DenseVector(3.3, 1.0, 6.3, 3.6)
a.size // a is a column vector of size 4
res6: Int = 4
a(1) // the second element of a is indexed by 1 as the first element is indexed by 0
res7: Double = 1.0
val a = DenseVector[Double](5, 4, -1) // this makes a vector of Doubles from input Int
a: breeze.linalg.DenseVector[Double] = DenseVector(5.0, 4.0, -1.0)
val a = DenseVector(5.0, 4.0, -1.0) // this makes a vector of Doubles from type inference . NOTE "5.0" is needed not just "5."
a: breeze.linalg.DenseVector[Double] = DenseVector(5.0, 4.0, -1.0)
val x = DenseVector.zeros[Double](5) // this will output x: breeze.linalg.DenseVector[Double] = DenseVector(0.0, 0.0, 0.0, 0.0, 0.0)
x: breeze.linalg.DenseVector[Double] = DenseVector(0.0, 0.0, 0.0, 0.0, 0.0)
Suggested Home Work for the linear-algebraically rusty (LiAlRusty): watch again and take notes.
val A = DenseMatrix((1, 4), (6, 1), (3, 5)) // let's create this 2 by 3 matrix
A: breeze.linalg.DenseMatrix[Int] =
1 4
6 1
3 5
A.t // transpose of A
res8: breeze.linalg.DenseMatrix[Int] =
1 6 3
4 1 5
val a = DenseVector(3.0, 4.0, 1.0)
a: breeze.linalg.DenseVector[Double] = DenseVector(3.0, 4.0, 1.0)
a.t
res9: breeze.linalg.Transpose[breeze.linalg.DenseVector[Double]] = Transpose(DenseVector(3.0, 4.0, 1.0))
Suggested Home Work for LiAlRusty: watch again and take notes.
Pop Quiz:
- what is a natural geometric interpretation of vector addition, subtraction, matric addition and subtraction?
val A = DenseMatrix((1, 4), (6, 1), (3, 5))
A: breeze.linalg.DenseMatrix[Int] =
1 4
6 1
3 5
val B = -A
B: breeze.linalg.DenseMatrix[Int] =
-1 -4
-6 -1
-3 -5
A + B // should be A-A=0
res10: breeze.linalg.DenseMatrix[Int] =
0 0
0 0
0 0
A - B // should be A+A=2A
res11: breeze.linalg.DenseMatrix[Int] =
2 8
12 2
6 10
B - A // should be -A-A=-2A
res12: breeze.linalg.DenseMatrix[Int] =
-2 -8
-12 -2
-6 -10
Operators
All Tensors support a set of operators, similar to those used in Matlab or Numpy.
For HOMEWORK see: Workspace -> scalable-data-science -> xtraResources -> LinearAlgebra -> LAlgCheatSheet for a list of most of the operators and various operations.
Some of the basic ones are reproduced here, to give you an idea.
| Operation | Breeze | Matlab | Numpy |
|---|---|---|---|
| Elementwise addition | a + b | a + b | a + b |
| Elementwise multiplication | a :* b | a .* b | a * b |
| Elementwise comparison | a :< b | a < b (gives matrix of 1/0 instead of true/false) | a < b |
| Inplace addition | a :+= 1.0 | a += 1 | a += 1 |
| Inplace elementwise multiplication | a :*= 2.0 | a *= 2 | a *= 2 |
| Vector dot product | a dot b,a.t * b† | dot(a,b) | dot(a,b) |
| Elementwise sum | sum(a) | sum(sum(a)) | a.sum() |
| Elementwise max | a.max | max(a) | a.max() |
| Elementwise argmax | argmax(a) | argmax(a) | a.argmax() |
| Ceiling | ceil(a) | ceil(a) | ceil(a) |
| Floor | floor(a) | floor(a) | floor(a) |
Scalar multiplication, Dot Product and Matrix-Vector multiplication
(watch now 2:26 = 218-377 seconds):
Suggested Home Work for LiAlRusty: watch again and take notes.
Pop Quiz:
- what is a natural geometric interpretation of scalar multiplication of a vector or a matrix and what about vector matrix multiplication?
Let's get a quick visual geometric interpretation for vectors, matrices and matrix-vector multiplications from the first interactive visual-cognitive aid at:
val A = DenseMatrix((1, 4), (6, 1), (3, 5))
A: breeze.linalg.DenseMatrix[Int] =
1 4
6 1
3 5
5 * A
res13: breeze.linalg.DenseMatrix[Int] =
5 20
30 5
15 25
A * 5
res14: breeze.linalg.DenseMatrix[Int] =
5 20
30 5
15 25
val A = DenseMatrix((1, 4), (6, 1), (3, 5))
A: breeze.linalg.DenseMatrix[Int] =
1 4
6 1
3 5
val B = DenseMatrix((3, 1), (2, 2), (1, 3))
B: breeze.linalg.DenseMatrix[Int] =
3 1
2 2
1 3
A *:* B // element-wise multiplication
res15: breeze.linalg.DenseMatrix[Int] =
3 4
12 2
3 15
val A = DenseMatrix((1, 4), (3, 1))
A: breeze.linalg.DenseMatrix[Int] =
1 4
3 1
val a = DenseVector(1, -1) // is a column vector
a: breeze.linalg.DenseVector[Int] = DenseVector(1, -1)
a.size // a is a column vector of size 2
res16: Int = 2
A * a
res17: breeze.linalg.DenseVector[Int] = DenseVector(-3, 2)
val A = DenseMatrix((1,2,3),(1,1,1))
A: breeze.linalg.DenseMatrix[Int] =
1 2 3
1 1 1
val B = DenseMatrix((4, 1), (9, 2), (8, 9))
B: breeze.linalg.DenseMatrix[Int] =
4 1
9 2
8 9
A*B // 4+18+14
res18: breeze.linalg.DenseMatrix[Int] =
46 32
21 12
1*4 + 2*9 + 3*8 // checking first entry of A*B
res19: Int = 46
val A = DenseMatrix((1,2,3),(4,5,6))
A: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
DenseMatrix.eye[Int](3)
res20: breeze.linalg.DenseMatrix[Int] =
1 0 0
0 1 0
0 0 1
A * DenseMatrix.eye[Int](3)
res21: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
DenseMatrix.eye[Int](2) * A
res22: breeze.linalg.DenseMatrix[Int] =
1 2 3
4 5 6
val D = DenseMatrix((2.0, 3.0), (4.0, 5.0))
val Dinv = inv(D)
D: breeze.linalg.DenseMatrix[Double] =
2.0 3.0
4.0 5.0
Dinv: breeze.linalg.DenseMatrix[Double] =
-2.5 1.5
2.0 -1.0
D * Dinv
res23: breeze.linalg.DenseMatrix[Double] =
1.0 0.0
0.0 1.0
Dinv * D
res24: breeze.linalg.DenseMatrix[Double] =
1.0 0.0
0.0 1.0
val b = DenseVector(4, 3)
norm(b)
b: breeze.linalg.DenseVector[Int] = DenseVector(4, 3)
res25: Double = 5.0
Math.sqrt(4*4 + 3*3) // check
res26: Double = 5.0
HOMEWORK: read this! https://github.com/scalanlp/breeze/wiki/Quickstart
It is here in markdown'd via wget and pandoc for your convenience.
Scala / nlp / breeze / Quickstart
David Hall edited this page on 24 Dec 2015
Breeze is modeled on Scala, and so if you're familiar with it, you'll be familiar with Breeze. First, import the linear algebra package:
scala> import breeze.linalg._
Let's create a vector:
scala> val x = DenseVector.zeros[Double](5)
x: breeze.linalg.DenseVector[Double] = DenseVector(0.0, 0.0, 0.0, 0.0, 0.0)
Here we make a column vector of zeros of type Double. And there are other ways we could create the vector - such as with a literal DenseVector(1,2,3) or with a call to fill or tabulate. The vector is "dense" because it is backed by an Array[Double], but could as well have created a SparseVector.zeros[Double](5), which would not allocate memory for zeros.
Unlike Scalala, all Vectors are column vectors. Row vectors are represented as Transpose[Vector[T]].
The vector object supports accessing and updating data elements by their index in 0 to x.length-1. Like Numpy, negative indices are supported, with the semantics that for an index i < 0 we operate on the i-th element from the end (x(i) == x(x.length + i)).
scala> x(0)
Double = 0.0
scala> x(1) = 2
scala> x
breeze.linalg.DenseVector[Double] = DenseVector(0.0, 2.0, 0.0, 0.0, 0.0)
Breeze also supports slicing. Note that slices using a Range are much, much faster than those with an arbitrary sequence.
scala> x(3 to 4) := .5
breeze.linalg.DenseVector[Double] = DenseVector(0.5, 0.5)
scala> x
breeze.linalg.DenseVector[Double] = DenseVector(0.0, 2.0, 0.0, 0.5, 0.5)
The slice operator constructs a read-through and write-through view of the given elements in the underlying vector. You set its values using the vectorized-set operator :=. You could as well have set it to a compatibly sized Vector.
scala> x(0 to 1) := DenseVector(.1,.2)
scala> x
breeze.linalg.DenseVector[Double] = DenseVector(0.1, 0.2, 0.0, 0.5, 0.5)
Similarly, a DenseMatrix can be created with a constructor method call, and its elements can be accessed and updated.
scala> val m = DenseMatrix.zeros[Int](5,5)
m: breeze.linalg.DenseMatrix[Int] =
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
The columns of m can be accessed as DenseVectors, and the rows as DenseMatrices.
scala> (m.rows, m.cols)
(Int, Int) = (5,5)
scala> m(::,1)
breeze.linalg.DenseVector[Int] = DenseVector(0, 0, 0, 0, 0)
scala> m(4,::) := DenseVector(1,2,3,4,5).t // transpose to match row shape
breeze.linalg.DenseMatrix[Int] = 1 2 3 4 5
scala> m
breeze.linalg.DenseMatrix[Int] =
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
1 2 3 4 5
Assignments with incompatible cardinality or a larger numeric type won't compile.
scala> m := x
<console>:13: error: could not find implicit value for parameter op: breeze.linalg.operators.BinaryUpdateOp[breeze.linalg.DenseMatrix[Int],breeze.linalg.DenseVector[Double],breeze.linalg.operators.OpSet]
m := x
^
Assignments with incompatible size will throw an exception:
scala> m := DenseMatrix.zeros[Int](3,3)
java.lang.IllegalArgumentException: requirement failed: Matrices must have same number of row
Sub-matrices can be sliced and updated, and literal matrices can be specified using a simple tuple-based syntax. Unlike Scalala, only range slices are supported, and only the columns (or rows for a transposed matrix) can have a Range step size different from 1.
scala> m(0 to 1, 0 to 1) := DenseMatrix((3,1),(-1,-2))
breeze.linalg.DenseMatrix[Int] =
3 1
-1 -2
scala> m
breeze.linalg.DenseMatrix[Int] =
3 1 0 0 0
-1 -2 0 0 0
0 0 0 0 0
0 0 0 0 0
1 2 3 4 5
Broadcasting
Sometimes we want to apply an operation to every row or column of a matrix, as a unit. For instance, you might want to compute the mean of each row, or add a vector to every column. Adapting a matrix so that operations can be applied column-wise or row-wise is called broadcasting. Languages like R and numpy automatically and implicitly do broadcasting, meaning they won't stop you if you accidentally add a matrix and a vector. In Breeze, you have to signal your intent using the broadcasting operator *. The * is meant to evoke "foreach" visually. Here are some examples:
scala> import breeze.stats.mean
scala> val dm = DenseMatrix((1.0,2.0,3.0),
(4.0,5.0,6.0))
scala> val res = dm(::, *) + DenseVector(3.0, 4.0)
breeze.linalg.DenseMatrix[Double] =
4.0 5.0 6.0
8.0 9.0 10.0
scala> res(::, *) := DenseVector(3.0, 4.0)
scala> res
breeze.linalg.DenseMatrix[Double] =
3.0 3.0 3.0
4.0 4.0 4.0
scala> mean(dm(*, ::))
breeze.linalg.DenseVector[Double] = DenseVector(2.0, 5.0)
breeze.stats.distributions
Breeze also provides a fairly large number of probability distributions. These come with access to probability density function for either discrete or continuous distributions. Many distributions also have methods for giving the mean and the variance.
scala> import breeze.stats.distributions._
scala> val poi = new Poisson(3.0);
poi: breeze.stats.distributions.Poisson = <function1>
scala> val s = poi.sample(5);
s: IndexedSeq[Int] = Vector(5, 4, 5, 7, 4)
scala> s map { poi.probabilityOf(_) }
IndexedSeq[Double] = Vector(0.10081881344492458, 0.16803135574154085, 0.10081881344492458, 0.02160403145248382, 0.16803135574154085)
scala> val doublePoi = for(x <- poi) yield x.toDouble // meanAndVariance requires doubles, but Poisson samples over Ints
doublePoi: breeze.stats.distributions.Rand[Double] = breeze.stats.distributions.Rand$$anon$11@1b52e04
scala> breeze.stats.meanAndVariance(doublePoi.samples.take(1000));
breeze.stats.MeanAndVariance = MeanAndVariance(2.9960000000000067,2.9669509509509533,1000)
scala> (poi.mean,poi.variance)
(Double, Double) = (3.0,3.0)
NOTE: Below, there is a possibility of confusion for the term rate in the family of exponential distributions. Breeze parameterizes the distribution with the mean, but refers to it as the rate.
scala> val expo = new Exponential(0.5);
expo: breeze.stats.distributions.Exponential = Exponential(0.5)
scala> expo.rate
Double = 0.5
A characteristic of exponential distributions is its half-life, but we can compute the probability a value falls between any two numbers.
scala> expo.probability(0, log(2) * expo.rate)
Double = 0.5
scala> expo.probability(0.0, 1.5)
Double = 0.950212931632136
This means that approximately 95% of the draws from an exponential distribution fall between 0 and thrice the mean. We could have easily computed this with the cumulative distribution as well
scala> 1 - exp(-3.0)
Double = 0.950212931632136
scala> val samples = expo.sample(2).sorted;
samples: IndexedSeq[Double] = Vector(1.1891135726280517, 2.325607782657507)
scala> expo.probability(samples(0), samples(1));
Double = 0.08316481553047272
scala> breeze.stats.meanAndVariance(expo.samples.take(10000));
breeze.stats.MeanAndVariance = MeanAndVariance(2.029351863973081,4.163267835527843,10000)
scala> (1 / expo.rate, 1 / (expo.rate * expo.rate))
(Double, Double) = (2.0,4.0)
breeze.optimize
TODO: document breeze.optimize.minimize, recommend that instead.
Breeze's optimization package includes several convex optimization routines and a simple linear program solver. Convex optimization routines typically take a DiffFunction[T], which is a Function1 extended to have a gradientAt method, which returns the gradient at a particular point. Most routines will require a breeze.linalg-enabled type: something like a Vector or a Counter.
Here's a simple DiffFunction: a parabola along each vector's coordinate.
scala> import breeze.optimize._
scala> val f = new DiffFunction[DenseVector[Double]] {
| def calculate(x: DenseVector[Double]) = {
| (norm((x - 3d) :^ 2d,1d),(x * 2d) - 6d);
| }
| }
f: java.lang.Object with breeze.optimize.DiffFunction[breeze.linalg.DenseVector[Double]] = $anon$1@617746b2
Note that this function takes its minimum when all values are 3. (It's just a parabola along each coordinate.)
scala> f.valueAt(DenseVector(3,3,3))
Double = 0.0
scala> f.gradientAt(DenseVector(3,0,1))
breeze.linalg.DenseVector[Double] = DenseVector(0.0, -6.0, -4.0)
scala> f.calculate(DenseVector(0,0))
(Double, breeze.linalg.DenseVector[Double]) = (18.0,DenseVector(-6.0, -6.0))
You can also use approximate derivatives, if your function is easy enough to compute:
scala> def g(x: DenseVector[Double]) = (x - 3.0):^ 2.0 sum
scala> g(DenseVector(0.,0.,0.))
Double = 27.0
scala> val diffg = new ApproximateGradientFunction(g)
scala> diffg.gradientAt(DenseVector(3,0,1))
breeze.linalg.DenseVector[Double] = DenseVector(1.000000082740371E-5, -5.999990000127297, -3.999990000025377)
Ok, now let's optimize f. The easiest routine to use is just LBFGS, which is a quasi-Newton method that works well for most problems.
scala> val lbfgs = new LBFGS[DenseVector[Double]](maxIter=100, m=3) // m is the memory. anywhere between 3 and 7 is fine. The larger m, the more memory is needed.
scala> val optimum = lbfgs.minimize(f,DenseVector(0,0,0))
optimum: breeze.linalg.DenseVector[Double] = DenseVector(2.9999999999999973, 2.9999999999999973, 2.9999999999999973)
scala> f(optimum)
Double = 2.129924444096732E-29
That's pretty close to 0! You can also use a configurable optimizer, using FirstOrderMinimizer.OptParams. It takes several parameters:
case class OptParams(batchSize:Int = 512,
regularization: Double = 1.0,
alpha: Double = 0.5,
maxIterations:Int = -1,
useL1: Boolean = false,
tolerance:Double = 1E-4,
useStochastic: Boolean= false) {
// ...
}
batchSize applies to BatchDiffFunctions, which support using small minibatches of a dataset. regularization integrates L2 or L1 (depending on useL1) regularization with constant lambda. alpha controls the initial stepsize for algorithms that need it. maxIterations is the maximum number of gradient steps to be taken (or -1 for until convergence). tolerance controls the sensitivity of the convergence check. Finally, useStochastic determines whether or not batch functions should be optimized using a stochastic gradient algorithm (using small batches), or using LBFGS (using the entire dataset).
OptParams can be controlled using breeze.config.Configuration, which we described earlier.
breeze.optimize.linear
We provide a DSL for solving linear programs, using Apache's Simplex Solver as the backend. This package isn't industrial strength yet by any means, but it's good for simple problems. The DSL is pretty simple:
import breeze.optimize.linear._
val lp = new LinearProgram()
import lp._
val x0 = Real()
val x1 = Real()
val x2 = Real()
val lpp = ( (x0 + x1 * 2 + x2 * 3 )
subjectTo ( x0 * -1 + x1 + x2 <= 20)
subjectTo ( x0 - x1 * 3 + x2 <= 30)
subjectTo ( x0 <= 40 )
)
val result = maximize( lpp)
assert( norm(result.result - DenseVector(40.0,17.5,42.5), 2) < 1E-4)
We also have specialized routines for bipartite matching (KuhnMunkres and CompetitiveLinking) and flow problems.
Breeze-Viz
**This API is highly experimental. It may change greatly. **
Breeze continues most of the functionality of Scalala's plotting facilities, though the API is somewhat different (in particular, more object oriented.) These methods are documented in scaladoc for the traits in the breeze.plot package object. First, let's plot some lines and save the image to file. All the actual plotting work is done by the excellent JFreeChart library.
import breeze.linalg._
import breeze.plot._
val f = Figure()
val p = f.subplot(0)
val x = linspace(0.0,1.0)
p += plot(x, x :^ 2.0)
p += plot(x, x :^ 3.0, '.')
p.xlabel = "x axis"
p.ylabel = "y axis"
f.saveas("lines.png") // save current figure as a .png, eps and pdf also supported

Then we'll add a new subplot and plot a histogram of 100,000 normally distributed random numbers into 100 buckets.
val p2 = f.subplot(2,1,1)
val g = breeze.stats.distributions.Gaussian(0,1)
p2 += hist(g.sample(100000),100)
p2.title = "A normal distribution"
f.saveas("subplots.png")

Breeze also supports the Matlab-like "image" command, here imaging a random matrix.
val f2 = Figure()
f2.subplot(0) += image(DenseMatrix.rand(200,200))
f2.saveas("image.png")

Where to go next?
After reading this quickstart, you can go to other wiki pages, especially Linear Algebra Cheat-Sheet and Data Structures.
Big O Notation for Space and Time Complexity by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning
(watch later 5:52):
Watch this and take notes if you have not experienced big-O and big-Omega and big-Theta notation.
Let us visit an interactive visual cognitive tool for the basics ideas in linear regression:
The following video is a very concise and thorough treatment of linear regression for those who have taken the 200-level linear algebra. Others can fully understand it with some effort and revisiting.
Linear Regression by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning
(watch now 11:13):
Ridge regression has a Bayesian interpretation where the weights have a zero-mean Gaussian prior. See 7.5 in Murphy's Machine Learning: A Probabilistic Perspective for details.
Please take notes in mark-down if you want.
For latex math within markdown you can do the following for in-line maths: \(\mathbf{A}_{i,j} \in \mathbb{R}^1\). And to write maths in display mode do the following:
\[\mathbf{A} \in \mathbb{R}^{m \times d} \]
You will need to write such notes for your final project presentation!
MillonSongs Ridge Regression by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning
(watch later 7:47):
Covers the training, test and validation and grid search... ridger regression...
Take your own notes if you like.
Gradient Descent by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning
(watch later 11:19):
Please take notes if you want to. Most of the above should be review to most of you.
Getting our hands dirty with Darren Wilkinson's blog on regression
Let's follow the exposition in:
- https://darrenjw.wordpress.com/2017/02/08/a-quick-introduction-to-apache-spark-for-statisticians/
- https://darrenjw.wordpress.com/2017/06/21/scala-glm-regression-modelling-in-scala/
You need to scroll down the fitst link embedded in iframe below to get to the section on Analysis of quantitative data with Descriptive statistics and Linear regression sub-sections (you can skip the earlier part that shows you how to run everything locally in spark-shell - try this on your own later).
Let's do this live now...
import breeze.stats.distributions._
def x = Gaussian(1.0,2.0).sample(10000)
val xRdd = sc.parallelize(x)
import breeze.stats.distributions._
x: IndexedSeq[Double]
xRdd: org.apache.spark.rdd.RDD[Double] = ParallelCollectionRDD[752] at parallelize at command-2972105651606193:3
println(xRdd.mean)
println(xRdd.sampleVariance)
1.0205021258164175
3.9857177279359335
val xStats = xRdd.stats
xStats.mean
xStats.sampleVariance
xStats.sum
xStats: org.apache.spark.util.StatCounter = (count: 10000, mean: 1.020502, stdev: 1.996326, max: 8.089040, min: -6.775216)
res1: Double = 10205.021258164175
val x2 = Gaussian(0.0,1.0).sample(10000) // 10,000 Gaussian samples with mean 0.0 and std dev 1.0
val xx = x zip x2 // creating tuples { (x,x2)_1, ... , (x,x2)_10000}
val lp = xx map {p => 2.0*p._1 + 1.0*p._2 + 1.5} // { lp_i := (2.0*x + 1.0 * x2 + 1.5)_i, i=1,...,10000 }
val eps = Gaussian(0.0,1.0).sample(10000) // standrad normal errors
val y = (lp zip eps) map (p => p._1 + p._2)
val yx = (y zip xx) map (p => (p._1, p._2._1, p._2._2))
x2: IndexedSeq[Double] = Vector(-0.39294234164655323, 0.8555415681712383, 0.5047531440104407, -1.7447287479723848, 0.24074263385944478, -2.5060458696190544, -0.3625957248008947, -1.590842317493378, 0.6957126527559235, 0.4443439308434807, 1.0693145537223456, -1.2513129429033423, -0.35885347259074074, -0.4025589013089939, -0.5152842487123, 1.132054878042222, -0.5455845613656002, 0.047570983504133746, -0.49567564166101635, 1.0696261814411365, 0.05742293214978136, -0.34714185801134384, 1.8582008120255742, -0.6693454894738627, -0.24016699291536123, -0.12433010673208071, 0.862434789491131, -1.1316281054134634, -0.10575269488815854, 1.7745918273414025, -1.3061660094876109, -0.06572880649671899, 0.3873544789319996, -0.12210660688891804, -1.165943599251054, -0.36104975279667534, 0.42769260065418174, 2.5268542442818207, -0.4635835110880271, 0.5605742432439522, -0.18424533745017588, -0.6547651770934155, -0.3093631599212501, -1.6992194371388365, 1.0251808317424254, -0.49932778415411466, 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0.2210587647846065, 1.5496999345258775, -0.02008178759544937, -0.7182074798739094, -1.574553120177353, -0.958792980043686, 1.7813255635141554, 0.33371717060253214, 0.409220615969245, 0.864661948971098, 0.45321316725758926, 0.5793640577986269, 0.1929236941299227, 0.8063376087514159, -0.8256218706957177, -1.3545564251072548, -0.0254183713884014, 0.4843489706620019, -0.2547387484777699, 1.413523018000126, -0.25289789625931125, -0.7310514576749854, -0.29989615115264184, -1.8363918121306806, -0.7011073045794324, 0.7618777606468017, 0.21700130623193362, 0.5506648468625105, 1.1297710404205215, 0.2260162334908117, 2.101771770966835, -1.0401885722164608, 0.02137180214574199, -0.7169360439614466, -0.613425386934265, 0.11174082723968021, -0.9886669373791672, -0.4543129014291321, -0.564846245965502, 1.0995727167228742, 1.271378633988257, -0.7702951531569466, 0.7462087643132189, 1.2725942749463925, 0.1262870057515097, -0.045893338153720944, 0.41084773670689295, -1.3024027664479396, -0.9499390632596515, 0.029261740362962387, 0.7045751511355585, 1.4422617585828663, -0.7261733429039657, -1.052342108510518, -1.5648904561390615, 1.3670421409678002, -0.20559374226242771, 0.013590987347403478, -0.3529126981766476, 0.691946010409337, -0.6960770497850841, 1.0458723890175317, 1.8863910225293368, 0.8491253516447187, 1.0689073309368422, 0.7250620374806074, -0.3901714568799757, -1.2526884263329479, 1.5337598160968535, -1.5734284492766968, -0.7626043824264306, 0.5562897446676294, -0.060403799427282315, -0.5429929788309762, -0.6132286530452334, 0.16636225858505, -0.0024321724215160944, -1.5841406068315993, -0.7986448690124527, -0.5033653324197733, 0.8639870077542707, 0.19250807868597403, 0.681426914941135, 1.4510109326483274, -2.042300652862932, -1.712300928957948, 0.10953948453393947, 1.1242791171955329, 0.8834072252776323, -0.05135912367481294, 1.009982993894816, -0.6095932412683162, 1.4956761154363034, 0.18567373438320595, 0.37090426102664004, 0.8311715544823943, 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(-0.3480671912553863,1.03387374103542), (2.8738286816216583,-0.21407256559986743), (1.863514328094054,-0.5812016751411385), (-0.9428403558267164,-0.7583304518772646), (1.754785995282709,-1.7509940657503635), (0.013682513757415804,-0.6818646320163245), (-0.5248686453084859,0.03344337152195518), (6.071914898933014,0.07593147801064193), (-1.958247927497752,0.4280940512841111), (4.708222732187144,1.7068789507250506), (2.945611634833644,0.41457568923249366), (-2.873611006165206,0.7191845635928295), (-0.6308622794536811,-1.0212751896145356), (1.7810775590894088,0.5752383319361283), (1.3389808014307991,1.0226769514361524), (1.810527500961158,0.4821255564311748), (-0.6433026011303964,0.6925122151119577), (2.5056823298048334,-0.49731955088091157), (-2.920132259436649,-0.2964847770300842), (2.037990236118738,-1.7889384504694956), (1.4786574552223295,-1.7308205970757709), (-3.2626435921253814,-0.8978527372381635), (3.202111558516957,1.610161749388444), (3.0775529358503873,-2.3346966778146676), (-0.004858289779320346,-0.14005235243669392), (-0.2356611818482255,-0.2638362330334334), (1.0363597159426476,3.033939148221663), (0.5951880796739928,-0.4723921431007955), (-1.385157144701036,-1.9818612938644127), (3.168627913505496,-1.200180759439612), (1.7659099443795578,-0.1958021131712377), (-2.2099743662353757,1.8451977668782862), (-2.030174316332396,-1.5854513074199188), (1.7759112730502544,-0.6861915752981226), (-1.4724017242345253,-0.8314228441600172), (-3.090438319803951,-0.5338604341531643), (-1.002042900848683,1.781652090388434), (2.6306174515890004,-0.9076613846556846), (-1.908448678609222,-0.6071641904585964), (1.4736263162987941,0.25535155484588434), (0.7736703824491263,1.5909788064424866), (1.7498656979196905,1.2231795468357605), (0.9366265907877988,-1.5369768230738219), (-0.09536435134844679,0.9778022047574955), (0.8177132188563772,-0.11568918189852785), (-3.4121939733497646,-0.43046323075751886), (3.4314196035854083,0.4802026430220832), (1.2692425660828197,2.1631891668981025), (0.9537947988635598,0.4690583697476523), (1.9696982561485377,1.0674375424842637), (3.9301829681795217,0.9208080500687618), (-0.4472709746146637,0.6843434412137375), (-2.253286341939513,-1.6794382154599943), (0.026135514256517056,1.0870090254688949), (2.4177497341772787,1.9248747606955043), (-0.835370698709712,0.6934671953307943), (4.135177415806311,0.627881336513211), (-1.3712161103055798,0.3744662154421754), (1.410688895640302,1.763784024349061), (-1.942154218131955,-0.8493808371900399), (0.38330995987308936,-0.567609188833336), (-1.0334804970971412,-0.2201046369367792), (-1.832625134688632,-1.6873491715309796), (2.5310364388620794,0.2371190560666023), (3.452713263233051,1.075251848522166), (-0.6837517757525475,1.4368687592287923), (-0.2942544269504741,0.8762404262364163), (1.4256959022956015,-0.07711565494502963), (2.7974786021190114,0.17698197228232837), (1.323363871505369,-0.3600776896107076), (2.7466288331322652,1.150261456652663), (-1.413730211824194,-0.5002...
val rddLR = sc.parallelize(yx)
rddLR.take(5)
rddLR: org.apache.spark.rdd.RDD[(Double, Double, Double)] = ParallelCollectionRDD[756] at parallelize at command-2972105651606197:1
res2: Array[(Double, Double, Double)] = Array((-0.6846711453351213,-1.5289467818444833,-0.39294234164655323), (5.497385056408215,1.699419230239045,0.8555415681712383), (3.192289974143172,0.5678191719510146,0.5047531440104407), (9.62629002945858,4.451573214358624,-1.7447287479723848), (2.3798191201086443,-0.1120835302184493,0.24074263385944478))
val dfLR = rddLR.toDF("y","x1","x2")
dfLR.show
dfLR.show(5)
+--------------------+--------------------+--------------------+
| y| x1| x2|
+--------------------+--------------------+--------------------+
| -0.6846711453351213| -1.5289467818444833|-0.39294234164655323|
| 5.497385056408215| 1.699419230239045| 0.8555415681712383|
| 3.192289974143172| 0.5678191719510146| 0.5047531440104407|
| 9.62629002945858| 4.451573214358624| -1.7447287479723848|
| 2.3798191201086443| -0.1120835302184493| 0.24074263385944478|
| 2.8219504009739422| 2.593362084700087| -2.5060458696190544|
| 3.612693960033294| 0.5534949593776934| -0.3625957248008947|
|-0.43223384405620746| 0.610162799267687| -1.590842317493378|
| 2.6768474073335398| 0.6079959554264716| 0.6957126527559235|
| 7.1521815633527535| 3.5570523071163964| 0.4443439308434807|
| -0.9227166193659311| -1.6847794191419774| 1.0693145537223456|
| -2.468553589310911| -0.9209298511507433| -1.2513129429033423|
| 3.898027197661611| 1.7696585529469604|-0.35885347259074074|
| 6.732593845464607| 2.513719756856524| -0.4025589013089939|
| 0.43416427898655346| -0.5101432900051885| -0.5152842487123|
| 4.214978206657414| 0.9531168056474683| 1.132054878042222|
| 1.4665466181948976|-0.18160386853639432| -0.5455845613656002|
| -3.7361996916879003| -1.6720534639191835|0.047570983504133746|
| 0.9602040797406759| -0.1128951628965178|-0.49567564166101635|
| 12.928871086510929| 5.053202646224175| 1.0696261814411365|
+--------------------+--------------------+--------------------+
only showing top 20 rows
+-------------------+-------------------+--------------------+
| y| x1| x2|
+-------------------+-------------------+--------------------+
|-0.6846711453351213|-1.5289467818444833|-0.39294234164655323|
| 5.497385056408215| 1.699419230239045| 0.8555415681712383|
| 3.192289974143172| 0.5678191719510146| 0.5047531440104407|
| 9.62629002945858| 4.451573214358624| -1.7447287479723848|
| 2.3798191201086443|-0.1120835302184493| 0.24074263385944478|
+-------------------+-------------------+--------------------+
only showing top 5 rows
dfLR: org.apache.spark.sql.DataFrame = [y: double, x1: double ... 1 more field]
// importing for regression
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.linalg._
val lm = new LinearRegression
lm.explainParams
lm.getStandardization
lm.setStandardization(false)
lm.getStandardization
lm.explainParams
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.linalg._
lm: org.apache.spark.ml.regression.LinearRegression = linReg_6bc65ac2a454
res5: String =
aggregationDepth: suggested depth for treeAggregate (>= 2) (default: 2)
elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty (default: 0.0)
epsilon: The shape parameter to control the amount of robustness. Must be > 1.0. (default: 1.35)
featuresCol: features column name (default: features)
fitIntercept: whether to fit an intercept term (default: true)
labelCol: label column name (default: label)
loss: The loss function to be optimized. Supported options: squaredError, huber. (Default squaredError) (default: squaredError)
maxIter: maximum number of iterations (>= 0) (default: 100)
predictionCol: prediction column name (default: prediction)
regParam: regularization parameter (>= 0) (default: 0.0)
solver: The solver algorithm for optimization. Supported options: auto, normal, l-bfgs. (Default auto) (default: auto)
standardization: whether to standardize the training features before fitting the model (default: true, current: false)
tol: the convergence tolerance for iterative algorithms (>= 0) (default: 1.0E-6)
weightCol: weight column name. If this is not set or empty, we treat all instance weights as 1.0 (undefined)
// Transform data frame to required format
val dflr = (dfLR map {row => (row.getDouble(0),
Vectors.dense(row.getDouble(1),row.getDouble(2)))}).
toDF("label","features")
dflr.show(5)
+-------------------+--------------------+
| label| features|
+-------------------+--------------------+
|-0.6846711453351213|[-1.5289467818444...|
| 5.497385056408215|[1.69941923023904...|
| 3.192289974143172|[0.56781917195101...|
| 9.62629002945858|[4.45157321435862...|
| 2.3798191201086443|[-0.1120835302184...|
+-------------------+--------------------+
only showing top 5 rows
dflr: org.apache.spark.sql.DataFrame = [label: double, features: vector]
// Fit model
val fit = lm.fit(dflr)
fit.intercept
fit: org.apache.spark.ml.regression.LinearRegressionModel = LinearRegressionModel: uid=linReg_6bc65ac2a454, numFeatures=2
res8: Double = 1.4864717388481623
fit.coefficients
res9: org.apache.spark.ml.linalg.Vector = [2.0076393809346107,1.0168498373858426]
val summ = fit.summary
summ: org.apache.spark.ml.regression.LinearRegressionTrainingSummary = org.apache.spark.ml.regression.LinearRegressionTrainingSummary@3ff7b1a0
summ.r2
res10: Double = 0.9439028933334824
summ.rootMeanSquaredError
res11: Double = 0.9917259206430321
summ.coefficientStandardErrors
res12: Array[Double] = Array(0.005062858724537394, 0.0098766852367649, 0.01122127338207313)
summ.pValues
res13: Array[Double] = Array(0.0, 0.0, 0.0)
summ.tValues
res14: Array[Double] = Array(396.5426432312811, 102.95456552576245, 132.46907799456326)
summ.predictions.show(5)
+-------------------+--------------------+-------------------+
| label| features| prediction|
+-------------------+--------------------+-------------------+
|-0.6846711453351213|[-1.5289467818444...|-1.9826653879413711|
| 5.497385056408215|[1.69941923023904...| 5.768250014665403|
| 3.192289974143172|[0.56781917195101...| 3.1397060221137103|
| 9.62629002945858|[4.45157321435862...| 8.649498287450083|
| 2.3798191201086443|[-0.1120835302184...| 1.5062475377192448|
+-------------------+--------------------+-------------------+
only showing top 5 rows
summ.residuals.show(5)
+--------------------+
| residuals|
+--------------------+
| 1.2979942426062498|
| -0.2708649582571878|
|0.052583952029461756|
| 0.9767917420084977|
| 0.8735715823893995|
+--------------------+
only showing top 5 rows
This gives you more on doing generalised linear modelling in Scala. But let's go back to out pipeline using the power-plant data.
Exercise: Writing your own Spark program for the least squares fit
Your task is to use the syntactic sugar below to write your own linear regression function using reduce and broadcast operations
How would you write your own Spark program to find the least squares fit on the following 1000 data points in RDD rddLR, where the variable y is the response variable, and X1 and X2 are independent variables?
More precisely, find \(w_1, w_2\), such that,
\(\sum_{i=1}^{10} (w_1 X1_i + w_2 X2_i - y_i)^2\) is minimized.
Report \(w_1\), \(w_2\), and the Root Mean Square Error and submit code in Spark. Analyze the resulting algorithm in terms of all-to-all, one-to-all, and all-to-one communication patterns.
rddLR.count
res39: Long = 10000
rddLR.take(10)
res33: Array[(Double, Double, Double)] = Array((-0.6846711453351213,-1.5289467818444833,-0.39294234164655323), (5.497385056408215,1.699419230239045,0.8555415681712383), (3.192289974143172,0.5678191719510146,0.5047531440104407), (9.62629002945858,4.451573214358624,-1.7447287479723848), (2.3798191201086443,-0.1120835302184493,0.24074263385944478), (2.8219504009739422,2.593362084700087,-2.5060458696190544), (3.612693960033294,0.5534949593776934,-0.3625957248008947), (-0.43223384405620746,0.610162799267687,-1.590842317493378), (2.6768474073335398,0.6079959554264716,0.6957126527559235), (7.1521815633527535,3.5570523071163964,0.4443439308434807))
val dfLR = rddLR.toDF("y","x1","x2")
dfLR.show(10)
+--------------------+-------------------+--------------------+
| y| x1| x2|
+--------------------+-------------------+--------------------+
| -0.6846711453351213|-1.5289467818444833|-0.39294234164655323|
| 5.497385056408215| 1.699419230239045| 0.8555415681712383|
| 3.192289974143172| 0.5678191719510146| 0.5047531440104407|
| 9.62629002945858| 4.451573214358624| -1.7447287479723848|
| 2.3798191201086443|-0.1120835302184493| 0.24074263385944478|
| 2.8219504009739422| 2.593362084700087| -2.5060458696190544|
| 3.612693960033294| 0.5534949593776934| -0.3625957248008947|
|-0.43223384405620746| 0.610162799267687| -1.590842317493378|
| 2.6768474073335398| 0.6079959554264716| 0.6957126527559235|
| 7.1521815633527535| 3.5570523071163964| 0.4443439308434807|
+--------------------+-------------------+--------------------+
only showing top 10 rows
dfLR: org.apache.spark.sql.DataFrame = [y: double, x1: double ... 1 more field]
rddLR.getNumPartitions
res47: Int = 8
rddLR.map( yx1x2 => (yx1x2._2, yx1x2._3, yx1x2._1) ).take(10)
res45: Array[(Double, Double, Double)] = Array((-1.5289467818444833,-0.39294234164655323,-0.6846711453351213), (1.699419230239045,0.8555415681712383,5.497385056408215), (0.5678191719510146,0.5047531440104407,3.192289974143172), (4.451573214358624,-1.7447287479723848,9.62629002945858), (-0.1120835302184493,0.24074263385944478,2.3798191201086443), (2.593362084700087,-2.5060458696190544,2.8219504009739422), (0.5534949593776934,-0.3625957248008947,3.612693960033294), (0.610162799267687,-1.590842317493378,-0.43223384405620746), (0.6079959554264716,0.6957126527559235,2.6768474073335398), (3.5570523071163964,0.4443439308434807,7.1521815633527535))
import breeze.linalg.DenseVector
val pts = rddLR.map( yx1x2 => DenseVector(yx1x2._2, yx1x2._3, yx1x2._1) ).cache
import breeze.linalg.DenseVector
pts: org.apache.spark.rdd.RDD[breeze.linalg.DenseVector[Double]] = MapPartitionsRDD[810] at map at command-2744653148556786:3
Now all we need to do is one-to-all and all-to-one broadcast of the gradient...
val w = DenseVector(0.0, 0.0, 0.0)
val w_bc = sc.broadcast(w)
val step = 0.1
val max_iter = 100
/*
YouTry: Fix the expressions for grad_w0, grad_w1 and grad_w2 to have w_bc.value be the same as that from ml lib's fit.coefficients
*/
for (i <- 1 to max_iter) {
val grad_w0 = pts.map(x => 2*(w_bc.value(0)*1.0 + w_bc.value(1)*x(0) + w_bc.value(2)*x(1) - x(2))*1.0).reduce(_+_)
val grad_w1 = pts.map(x => 2*(w_bc.value(0)*1.0 + w_bc.value(1)*x(0) + w_bc.value(2)*x(1) - x(2))*x(0)).reduce(_+_)
val grad_w2 = pts.map(x => 2*(w_bc.value(0)*1.0 + w_bc.value(1)*x(0) + w_bc.value(2)*x(1) - x(2))*x(1)).reduce(_+_)
w_bc.value(0) = w_bc.value(0) - step*grad_w0
w_bc.value(1) = w_bc.value(1) - step*grad_w1
w_bc.value(2) = w_bc.value(2) - step*grad_w2
}
w: breeze.linalg.DenseVector[Double] = DenseVector(713529.4020469891, 2282475.3330477937, 208861.12768314985)
w_bc: org.apache.spark.broadcast.Broadcast[breeze.linalg.DenseVector[Double]] = Broadcast(2899)
step: Double = 0.1
max_iter: Int = 100
w_bc.value
res69: breeze.linalg.DenseVector[Double] = DenseVector(713529.4020469891, 2282475.3330477937, 208861.12768314985)
fit.intercept
res70: Double = 1.4864717388481623
fit.coefficients
res71: org.apache.spark.ml.linalg.Vector = [2.0076393809346107,1.0168498373858426]
Computing each of the gradients requires an all-to-one communication (due to the .reduce(_+_)). There are two of these per iteration. Broadcasting the updated w_bc requires one to all communication.
We need this more detailed deep dive
HOMEWORK:
- read: http://arxiv.org/pdf/1509.02256.pdf (also see References and Appendix A).
- and go through the notebooks here: Data Types - MLlib Programming Guide
- http://stanford.edu/~rezab/dao/
Distributed Machine Learning: Computation and Storage by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning
(watch now 8:48):
This is a transcript of Ameet's lecture in the video above:
HOMEWORK: Make sure you are understanding everything he is trying to convey!
Recall from week 1's lectures that we are weaving over Ameet's course here to get to advanced applications "in a hurry"...
In this segment, we'll begin our discussion of distributed machine learning principles related to computation and storage. We'll use linear regression as a running example to illustrate these ideas. As we previously discussed, the size of datasets is rapidly growing. And this has led to scalability issues for many standard machine learning methods. For instance, consider the least squares regression model. The fact that a closed-form solution exists is an appealing property of this method. But what happens when we try to solve this closed-form solution at scale? In this segment, we'll focus on computational issues associated with linear regression. Though I should note that these same ideas apply in the context of ridge regression, as it has a very similar computational profile. So let's figure out the time and space complexity of solving for this closed-form solution. We'll start with time complexity, considering arithmetic operations as the basic units of computation when discussing big O complexity. Looking at the expression for w, if we perform each of the required operations separately, we see that computing X transpose X takes O of nd squared time, and inverting this resulting matrix takes O of d cubed time. Since matrix multiplication is an associative operation, we can multiply X transpose and y in O of nd time to get some intermediate d dimensional vector, and then perform the multiplication between the inverse matrix and this intermediate d dimensional vector in O of d squared time. Hence, in summary, the two computational bottlenecks in this process involve computing X transpose X, and subsequently computing its inverse. There are other methods to solve this equation that can be faster in practice, but they are only faster in terms of constants, and therefore they all have the same overall time complexity. In summary, we say that computing the closed-form solution for linear regression takes O of nd squared plus d cubed time. Now let's consider the space complexity. And recall that our basic unit of storage here is the storage required to store a single float, which is typically 8 bytes. In order to compute w, we must first store the data matrix, which requires O of nd floats. Additionally, we must compute X transpose X and its inverse. In order to solve for w, each of these matrices are d by d and thus require O of d squared floats. These are the two bottlenecks storage-wise. And thus our space complexity is O of nd plus d squared. So now that we've considered the time and space complexity required to solve for the closed-form solution, let's consider what happens as our data grows large. The first situation we'll consider is one where n, or the number of observations, is large, while d, or the number of features, is relatively small. Specifically, we'll assume that we're in a setting where d is small enough such that O of d cubed operate computation and O of d squared storage is feasible on a single machine. In this scenario, the terms in our big O complexity involving n are the ones that dominate, and thus storing X and computing X transpose X are the bottlenecks. It turns out that in this scenario, we're well suited for a distributed computation. First, we can store the data points or rows of X across several machines, thus reducing the storage burden. Second, we can compute X transpose X in parallel across the machines by treating this multiplication as a sum of outer products. To understand this alternative interpretation of matrix multiplication in terms of outer products, let's first recall our typical definition of matrix multiplication. We usually think about each entry of the output matrix being computed via an inner product between rows and columns of the input matrices. So, for instance, in the example on the slide, to compute the top left entry, we compute the inner product between the first row of the left input matrix and the first column of the right input matrix. Similarly, to compute the top right entry, we compute the inner product between the first row of the left input matrix and the second column of the right input matrix. We perform additional inner products to compute the two remaining entries of the output matrix. There is, however, an alternative interpretation of matrix multiplication as the sum of outer products between corresponding rows and columns of the input matrices. Let's look at the same example from the last slide to get a better sense of what this means. First consider the first column of the left input matrix and the first row of the right input matrix. We can compute their outer product with the result being the 2 by 2 matrix on the bottom of the slide. Next, we can consider the second column of the left input matrix and the second row of the right input matrix, and again compute their outer product, resulting in another 2 by 2 matrix. We can repeat this process a third time to generate a third outer product or 2 by 2 matrix. The sum of these outer products matches the result we obtained in the previous slide using the traditional definition of matrix multiplication. And more generally, taking a sum of outer products of corresponding rows and columns of the input matrices, always returns the desired matrix multiplication result. Now we can use this new interpretation of matrix multiplication to our benefit when distributing the computation of X transpose X for linear regression. Let's first represent X visually by its rows or data points. Then we can express this matrix multiplication as a sum of outer products where each outer product involves only a single row of X or a single data point. Let's see how we can use this insight to effectively distribute our computation. Consider a toy example where we have a cluster of three workers and a data set with six data points. We can distribute the storage of our six data points across the three workers so that each worker is storing two data points. Now we can express matrix multiplication as a simple MapReduce operation. In the map step, we take each point and compute its outer product with itself. And in the subsequent reduce step, we simply sum over all of these outer products. We can then solve for the final linear regression model locally, which includes computing the inverse of this resulting matrix. Now let's look at the storage and computation involved at each step. In the first step, we're not doing any computation, but we need to store the input data, which requires O of nd storage. This is a bottleneck in our setting since n is large. However, the storage can be distributed over several machines. Next, during the map step, we perform an outer product for each data point. Each outer product takes O of d squared time, and we have to compute n of these outer products. This is the computational bottleneck in our setting, but again, it is distributed across multiple workers. In terms of storage, we must store the outer products computed on each machine. Note that although we may be computing several outer products per machine, we can keep a running sum of these outer products, so the local storage required for each machine is O of d squared. Finally, in the reduce step, we must take the sum of these outer products, though the computational bottleneck is, in fact, inverting the resulting matrix, which is cubic nd. However, we're assuming that d is small enough for this computation to be feasible on a single machine. Similarly, the O of d squared storage required to store X transpose X and its inverse is also feasible on a single machine by assumption. This entire process can be concisely summarized via the following Spark code snippet. In this code, train data is an RDD of rows of X. In the map step, we compute an outer product for each row. And in the reduce step, we sum these outer products and invert the resulting matrix. In the final reduce step, we can also perform the remaining steps required to obtain our final regression model.
Distributed Machine Learning: Computation and Storage (Part 2) by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning
(watch now/later? 4:02):
This is a transcript of Ameet's lecture in the video above:
Homework: Make sure you are understanding everything he is trying to convey!
In this segment, we'll continue our discussion of distributed machine learning principles related to computation and storage. We'll focus on the problem when D, the number of features, grows large. In the previous segment, we discussed the big N small D setting. In this setting, we can naturally use a distributed computing environment to solve for the linear regression closed form solution. To do this, we store our data across multiple machines and we compute X transpose X as a sum of outer products. This strategy can be written as a simple MapReduce operation, expressed very concisely in Spark. Now, let's consider what happens when D grows large. As before, storing X and computing X transpose X are bottlenecks. However, storing and operating on X transpose X is now also a bottleneck. And we can no longer use our previous strategy. So let's see what goes wrong. Here's what our strategy looks like in the small D setting with data stored across workers, outer products computed in the map step, and sum of these outer products performed in the reduced step. However, we can no longer perform D cubed operations locally or store D squared floats locally in our new setting. This issue leads to a more general rule of thumb, which is that when N and D are large, we need the computation and storage complexity to be at most linear in N and D. So how do we devise methods that are linear in space and time complexity? One idea is to exploit sparsity. Sparse data is quite prevalent in practice. Some data is inherently sparse, such as rating information and collaborative filtering problems or social networking or other grafted. Additionally, we often generate sparse features during a process of feature extraction, such as when we represent text documents via a bag-of-words features or when we convert categorical features into numerical representations. Accounting for sparsity can lead to orders of magnitudes of savings in terms of storage and computation. A second idea is to make a late and sparsity assumption, whereby we make the assumption that our high dimensional data can in fact be represented in a more succinct fashion, either exactly or approximately. For example, we can make a low rank modeling assumption where we might assume that our data matrix can in fact be represented by the product of two skinny matrices, where the skinny dimension R is much smaller than either N or D. Exploiting this assumption can also yield significant computational and storage gigs. A third option is to use different algorithms. For instance, instead of learning a linear regression model via the closed form solution, we could alternatively use gradient descent. Gradient descent is an iterative algorithm that requires layer computation and storage at each iteration thus making it attractive in the big N and big D setting. So let's see how gradient descent stacks up with a closed form solution in our toy example on a cluster with three machines. As before, we can store the data across the worker machines. Now in the map step, we require O of ND computation, and this computation is distributed across workers. And we also require O of D storage locally. In the reduced step, we require O of D local computation as well as O of D local storage. Moreover, unlike the closed form case, we need to repeat this process several times since gradient descent is an iterative algorithm. At this point, I haven't really told you how these question marks work. And in the next segment, we'll talk about what actually is going on with gradient decent.
Communication Hierarchy by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning
(watch later 2:32):
SUMMARY: Access rates fall sharply with distance.
- roughly 50 x gap between reading from memory and reading from either disk or the network.
We must take this communication hierarchy into consideration when developing parallel and distributed algorithms.
Distributed Machine Learning: Communication Principles by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning
(watch later 11:28):
Focusing on strategies to reduce communication costs.
- access rates fall sharply with distance.
- so this communication hierarchy needs to be accounted for when developing parallel and distributed algorithms.
Lessons:
-
parallelism makes our computation faster
-
but network communication slows us down
-
BINGO: perform parallel and in-memory computation.
-
Persisting in memory is a particularly attractive option when working with iterative algorithms that read the same data multiple times, as is the case in gradient descent.
-
Several machine learning algorithms are iterative!
-
Limits of multi-core scaling (powerful multicore machine with several CPUs, and a huge amount of RAM).
- advantageous:
- sidestep any network communication when working with a single multicore machine
- can indeed handle fairly large data sets, and they're an attractive option in many settings.
- disadvantages:
- can be quite expensive (due to specialized hardware),
- not as widely accessible as commodity computing nodes.
- this approach does have scalability limitations, as we'll eventually hit a wall when the data grows large enough! This is not the case for a distributed environment (like the AWS EC2 cloud under the hood here).
- advantageous:
Simple strategies for algorithms in a distributed setting: to reduce network communication, simply keep large objects local
- In the big n, small d case for linear regression
- we can solve the problem via a closed form solution.
- And this requires us to communicate \(O(d)^2\) intermediate data.
- the largest object in this example is our initial data, which we store in a distributed fashion and never communicate! This is a data parallel setting.
- In the big n, big d case:
- for linear regression.
- we use gradient descent to iteratively train our model and are again in a data parallel setting.
- At each iteration we communicate the current parameter vector \(w_i\) and the required \(O(d)\) communication is feasible even for fairly large d.
- for linear regression.
- In the small n, small d case:
- for ridge regression
- we can communicate the small data to all of the workers.
- this is an example of a model parallel setting where we can train the model for each hyper-parameter in parallel.
- for ridge regression
- Linear regression with big n and huge d is an example of both data and model parallelism.
HOMEWORK: Watch the video and find out why Linear regression with big n and huge d is an example of both data and model parallelism.
In this setting, since our data is large, we must still store it across multiple machines. We can still use gradient descent, or stochastic variants of gradient descent to train our model, but we may not want to communicate the entire d dimensional parameter vector at each iteration, when we have 10s, or hundreds of millions of features. In this setting we often rely on sparsity to reduce the communication. So far we discussed how we can reduce communication by keeping large data local.
Simple strategies for algorithms in a distributed setting: compute more and communicate less per iteration
HOMEWORK: watch the video and understand why it is important at each iteration of an iterative algorithm to compute more and communicate less.
Recall from week 1's lecture that the ideal mathematical preparation to fully digest this material requires a set of self-tutorials from Reza Zadeh's course in Distributed Algorithms and Optimization from Stanford:
This is a minimal pre-requisite for designing new algorithms or improving exixting ones.
Notes for these lectures will be provided for self-study.
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Why are we going through the basic distributed linear algebra Data Types?
This is to get you to be able to directly use them in a project or to roll your own algorithms.
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Local vector in Scala
A local vector has integer-typed and 0-based indices and double-typed values, stored on a single machine.
MLlib supports two types of local vectors:
- dense and
- sparse.
A dense vector is backed by a double array representing its entry values, while a sparse vector is backed by two parallel arrays: indices and values.
For example, a vector (1.0, 0.0, 3.0) can be represented:
- in dense format as
[1.0, 0.0, 3.0]or - in sparse format as
(3, [0, 2], [1.0, 3.0]), where3is the size of the vector.
The base class of local vectors is Vector, and we provide two implementations: DenseVector and SparseVector. We recommend using the factory methods implemented in Vectors to create local vectors. Refer to the Vector Scala docs and Vectors Scala docs for details on the API.
import org.apache.spark.mllib.linalg.{Vector, Vectors}
// Create a dense vector (1.0, 0.0, 3.0).
val dv: Vector = Vectors.dense(1.0, 0.0, 3.0)
import org.apache.spark.mllib.linalg.{Vector, Vectors}
dv: org.apache.spark.mllib.linalg.Vector = [1.0,0.0,3.0]
// Create a sparse vector (1.0, 0.0, 3.0) by specifying its indices and values corresponding to nonzero entries.
val sv1: Vector = Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0))
sv1: org.apache.spark.mllib.linalg.Vector = (3,[0,2],[1.0,3.0])
// Create a sparse vector (1.0, 0.0, 3.0) by specifying its nonzero entries.
val sv2: Vector = Vectors.sparse(3, Seq((0, 1.0), (2, 3.0)))
sv2: org.apache.spark.mllib.linalg.Vector = (3,[0,2],[1.0,3.0])
Note: Scala imports scala.collection.immutable.Vector by default, so you have to import org.apache.spark.mllib.linalg.Vector explicitly to use MLlib’s Vector.
python: MLlib recognizes the following types as dense vectors:
- NumPy’s
array - Python’s list, e.g.,
[1, 2, 3]
and the following as sparse vectors:
- MLlib’s
SparseVector. - SciPy’s
csc_matrixwith a single column
We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented in Vectors to create sparse vectors.
Refer to the Vectors Python docs for more details on the API.
import numpy as np
import scipy.sparse as sps
from pyspark.mllib.linalg import Vectors
# Use a NumPy array as a dense vector.
dv1 = np.array([1.0, 0.0, 3.0])
# Use a Python list as a dense vector.
dv2 = [1.0, 0.0, 3.0]
# Create a SparseVector.
sv1 = Vectors.sparse(3, [0, 2], [1.0, 3.0])
# Use a single-column SciPy csc_matrix as a sparse vector.
sv2 = sps.csc_matrix((np.array([1.0, 3.0]), np.array([0, 2]), np.array([0, 2])), shape = (3, 1))
print (dv1)
print (dv2)
print (sv1)
print (sv2)
[1. 0. 3.]
[1.0, 0.0, 3.0]
(3,[0,2],[1.0,3.0])
(0, 0) 1.0
(2, 0) 3.0
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Labeled point in Scala
A labeled point is a local vector, either dense or sparse, associated with a label/response. In MLlib, labeled points are used in supervised learning algorithms.
We use a double to store a label, so we can use labeled points in both regression and classification.
For binary classification, a label should be either 0 (negative) or 1 (positive). For multiclass classification, labels should be class indices starting from zero: 0, 1, 2, ....
A labeled point is represented by the case class LabeledPoint.
Refer to the LabeledPoint Scala docs for details on the API.
//import first
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
// Create a labeled point with a "positive" label and a dense feature vector.
val pos = LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0))
pos: org.apache.spark.mllib.regression.LabeledPoint = (1.0,[1.0,0.0,3.0])
// Create a labeled point with a "negative" label and a sparse feature vector.
val neg = LabeledPoint(0.0, Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0)))
neg: org.apache.spark.mllib.regression.LabeledPoint = (0.0,(3,[0,2],[1.0,3.0]))
Sparse data in Scala
It is very common in practice to have sparse training data. MLlib supports reading training examples stored in LIBSVM format, which is the default format used by LIBSVM and LIBLINEAR. It is a text format in which each line represents a labeled sparse feature vector using the following format:
label index1:value1 index2:value2 ...
where the indices are one-based and in ascending order. After loading, the feature indices are converted to zero-based.
MLUtils.loadLibSVMFile reads training examples stored in LIBSVM format.
Refer to the MLUtils Scala docs for details on the API.
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD
//val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") // from prog guide but no such data here - can wget from github
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD
Load MNIST training and test datasets
Our datasets are vectors of pixels representing images of handwritten digits. For example:

display(dbutils.fs.ls("/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt"))
| path | name | size |
|---|---|---|
| dbfs:/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt | mnist-digits-train.txt | 6.9430283e7 |
val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
examples: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint] = MapPartitionsRDD[3661] at map at MLUtils.scala:88
examples.take(1)
res1: Array[org.apache.spark.mllib.regression.LabeledPoint] = Array((5.0,(780,[152,153,154,155,156,157,158,159,160,161,162,163,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,231,232,233,234,235,236,237,238,239,240,241,260,261,262,263,264,265,266,268,269,289,290,291,292,293,319,320,321,322,347,348,349,350,376,377,378,379,380,381,405,406,407,408,409,410,434,435,436,437,438,439,463,464,465,466,467,493,494,495,496,518,519,520,521,522,523,524,544,545,546,547,548,549,550,551,570,571,572,573,574,575,576,577,578,596,597,598,599,600,601,602,603,604,605,622,623,624,625,626,627,628,629,630,631,648,649,650,651,652,653,654,655,656,657,676,677,678,679,680,681,682,683],[3.0,18.0,18.0,18.0,126.0,136.0,175.0,26.0,166.0,255.0,247.0,127.0,30.0,36.0,94.0,154.0,170.0,253.0,253.0,253.0,253.0,253.0,225.0,172.0,253.0,242.0,195.0,64.0,49.0,238.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,251.0,93.0,82.0,82.0,56.0,39.0,18.0,219.0,253.0,253.0,253.0,253.0,253.0,198.0,182.0,247.0,241.0,80.0,156.0,107.0,253.0,253.0,205.0,11.0,43.0,154.0,14.0,1.0,154.0,253.0,90.0,139.0,253.0,190.0,2.0,11.0,190.0,253.0,70.0,35.0,241.0,225.0,160.0,108.0,1.0,81.0,240.0,253.0,253.0,119.0,25.0,45.0,186.0,253.0,253.0,150.0,27.0,16.0,93.0,252.0,253.0,187.0,249.0,253.0,249.0,64.0,46.0,130.0,183.0,253.0,253.0,207.0,2.0,39.0,148.0,229.0,253.0,253.0,253.0,250.0,182.0,24.0,114.0,221.0,253.0,253.0,253.0,253.0,201.0,78.0,23.0,66.0,213.0,253.0,253.0,253.0,253.0,198.0,81.0,2.0,18.0,171.0,219.0,253.0,253.0,253.0,253.0,195.0,80.0,9.0,55.0,172.0,226.0,253.0,253.0,253.0,253.0,244.0,133.0,11.0,136.0,253.0,253.0,253.0,212.0,135.0,132.0,16.0])))
Display our data. Each image has the true label (the label column) and a vector of features which represent pixel intensities (see below for details of what is in training).
display(examples.toDF) // covert to DataFrame and display for convenient db visualization
The pixel intensities are represented in features as a sparse vector, for example the first observation, as seen in row 1 of the output to display(training) below, has label as 5, i.e. the hand-written image is for the number 5. And this hand-written image is the following sparse vector (just click the triangle to the left of the feature in first row to see the following):
type: 0
size: 780
indices: [152,153,155,...,682,683]
values: [3, 18, 18,18,126,...,132,16]
Here
type: 0says we hve a sparse vector.size: 780says the vector has 780 indices in total- these indices from 0,...,779 are a unidimensional indexing of the two-dimensional array of pixels in the image
indices: [152,153,155,...,682,683]are the indices from the[0,1,...,779]possible indices with non-zero values- a value is an integer encoding the gray-level at the pixel index
values: [3, 18, 18,18,126,...,132,16]are the actual gray level values, for example:- at pixed index
152the gray-level value is3, - at index
153the gray-level value is18, - ..., and finally at
- at index
683the gray-level value is18
- at pixed index
We could also use the following method as done in notebook 016_* already.
val training = spark.read.format("libsvm")
.option("numFeatures", "780")
.load("/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
training: org.apache.spark.sql.DataFrame = [label: double, features: vector]
display(training)
Labeled point in Python
A labeled point is represented by LabeledPoint.
Refer to the LabeledPoint Python docs for more details on the API.
# import first
from pyspark.mllib.linalg import SparseVector
from pyspark.mllib.regression import LabeledPoint
# Create a labeled point with a positive label and a dense feature vector.
pos = LabeledPoint(1.0, [1.0, 0.0, 3.0])
# Create a labeled point with a negative label and a sparse feature vector.
neg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0]))
Sparse data in Python
MLUtils.loadLibSVMFile reads training examples stored in LIBSVM format.
Refer to the MLUtils Python docs for more details on the API.
from pyspark.mllib.util import MLUtils
# examples = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") #from prog guide but no such data here - can wget from github
examples = MLUtils.loadLibSVMFile(sc, "/databricks-datasets/mnist-digits/data-001/mnist-digits-train.txt")
examples.take(1)
res5: Array[org.apache.spark.mllib.regression.LabeledPoint] = Array((5.0,(780,[152,153,154,155,156,157,158,159,160,161,162,163,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,231,232,233,234,235,236,237,238,239,240,241,260,261,262,263,264,265,266,268,269,289,290,291,292,293,319,320,321,322,347,348,349,350,376,377,378,379,380,381,405,406,407,408,409,410,434,435,436,437,438,439,463,464,465,466,467,493,494,495,496,518,519,520,521,522,523,524,544,545,546,547,548,549,550,551,570,571,572,573,574,575,576,577,578,596,597,598,599,600,601,602,603,604,605,622,623,624,625,626,627,628,629,630,631,648,649,650,651,652,653,654,655,656,657,676,677,678,679,680,681,682,683],[3.0,18.0,18.0,18.0,126.0,136.0,175.0,26.0,166.0,255.0,247.0,127.0,30.0,36.0,94.0,154.0,170.0,253.0,253.0,253.0,253.0,253.0,225.0,172.0,253.0,242.0,195.0,64.0,49.0,238.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,253.0,251.0,93.0,82.0,82.0,56.0,39.0,18.0,219.0,253.0,253.0,253.0,253.0,253.0,198.0,182.0,247.0,241.0,80.0,156.0,107.0,253.0,253.0,205.0,11.0,43.0,154.0,14.0,1.0,154.0,253.0,90.0,139.0,253.0,190.0,2.0,11.0,190.0,253.0,70.0,35.0,241.0,225.0,160.0,108.0,1.0,81.0,240.0,253.0,253.0,119.0,25.0,45.0,186.0,253.0,253.0,150.0,27.0,16.0,93.0,252.0,253.0,187.0,249.0,253.0,249.0,64.0,46.0,130.0,183.0,253.0,253.0,207.0,2.0,39.0,148.0,229.0,253.0,253.0,253.0,250.0,182.0,24.0,114.0,221.0,253.0,253.0,253.0,253.0,201.0,78.0,23.0,66.0,213.0,253.0,253.0,253.0,253.0,198.0,81.0,2.0,18.0,171.0,219.0,253.0,253.0,253.0,253.0,195.0,80.0,9.0,55.0,172.0,226.0,253.0,253.0,253.0,253.0,244.0,133.0,11.0,136.0,253.0,253.0,253.0,212.0,135.0,132.0,16.0])))
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Local Matrix in Scala
A local matrix has integer-typed row and column indices and double-typed values, stored on a single machine. MLlib supports:
- dense matrices, whose entry values are stored in a single double array in column-major order, and
- sparse matrices, whose non-zero entry values are stored in the Compressed Sparse Column (CSC) format in column-major order.
For example, the following dense matrix: \[ \begin{pmatrix} 1.0 & 2.0 \\ 3.0 & 4.0 \\ 5.0 & 6.0 \end{pmatrix} \] is stored in a one-dimensional array [1.0, 3.0, 5.0, 2.0, 4.0, 6.0] with the matrix size (3, 2).
The base class of local matrices is Matrix, and we provide two implementations: DenseMatrix, and SparseMatrix. We recommend using the factory methods implemented in Matrices to create local matrices. Remember, local matrices in MLlib are stored in column-major order.
Refer to the Matrix Scala docs and Matrices Scala docs for details on the API.
Int.MaxValue // note the largest value an index can take
res0: Int = 2147483647
import org.apache.spark.mllib.linalg.{Matrix, Matrices}
// Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
val dm: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
import org.apache.spark.mllib.linalg.{Matrix, Matrices}
dm: org.apache.spark.mllib.linalg.Matrix =
1.0 2.0
3.0 4.0
5.0 6.0
Next, let us create the following sparse local matrix: \[ \begin{pmatrix} 9.0 & 0.0 \\ 0.0 & 8.0 \\ 0.0 & 6.0 \end{pmatrix} \]
// Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
val sm: Matrix = Matrices.sparse(3, 2, Array(0, 1, 3), Array(0, 2, 1), Array(9, 6, 8))
sm: org.apache.spark.mllib.linalg.Matrix =
3 x 2 CSCMatrix
(0,0) 9.0
(2,1) 6.0
(1,1) 8.0
Local Matrix in Python
The base class of local matrices is Matrix, and we provide two implementations: DenseMatrix, and SparseMatrix. We recommend using the factory methods implemented in Matrices to create local matrices. Remember, local matrices in MLlib are stored in column-major order.
Refer to the Matrix Python docs and Matrices Python docs for more details on the API.
from pyspark.mllib.linalg import Matrix, Matrices
# Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
dm2 = Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])
dm2
# Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
sm = Matrices.sparse(3, 2, [0, 1, 3], [0, 2, 1], [9, 6, 8])
sm
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
Distributed matrix in Scala
A distributed matrix has long-typed row and column indices and double-typed values, stored distributively in one or more RDDs.
It is very important to choose the right format to store large and distributed matrices. Converting a distributed matrix to a different format may require a global shuffle, which is quite expensive.
Three types of distributed matrices have been implemented so far.
- The basic type is called
RowMatrix.
- A
RowMatrixis a row-oriented distributed matrix without meaningful row indices, e.g., a collection of feature vectors. It is backed by an RDD of its rows, where each row is a local vector. - We assume that the number of columns is not huge for a
RowMatrixso that a single local vector can be reasonably communicated to the driver and can also be stored / operated on using a single node. - An
IndexedRowMatrixis similar to aRowMatrixbut with row indices, which can be used for identifying rows and executing joins. - A
CoordinateMatrixis a distributed matrix stored in coordinate list (COO) format, backed by an RDD of its entries.
Note
The underlying RDDs of a distributed matrix must be deterministic, because we cache the matrix size. In general the use of non-deterministic RDDs can lead to errors.
Remark: there is a huge difference in the orders of magnitude between the maximum size of local versus distributed matrices!
print(Long.MaxValue.toDouble, Int.MaxValue.toDouble, Long.MaxValue.toDouble / Int.MaxValue.toDouble) // index ranges and ratio for local and distributed matrices
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
RowMatrix in Scala
A RowMatrix is a row-oriented distributed matrix without meaningful row indices, backed by an RDD of its rows, where each row is a local vector. Since each row is represented by a local vector, the number of columns is limited by the integer range but it should be much smaller in practice.
A RowMatrix can be created from an RDD[Vector] instance. Then we can compute its column summary statistics and decompositions.
- QR decomposition is of the form A = QR where Q is an orthogonal matrix and R is an upper triangular matrix.
- For singular value decomposition (SVD) and principal component analysis (PCA), please refer to Dimensionality reduction.
Refer to the RowMatrix Scala docs for details on the API.
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.RowMatrix
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.RowMatrix
val rows: RDD[Vector] = sc.parallelize(Array(Vectors.dense(12.0, -51.0, 4.0), Vectors.dense(6.0, 167.0, -68.0), Vectors.dense(-4.0, 24.0, -41.0))) // an RDD of local vectors
rows: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.Vector] = ParallelCollectionRDD[3670] at parallelize at command-2972105651606776:1
// Create a RowMatrix from an RDD[Vector].
val mat: RowMatrix = new RowMatrix(rows)
mat: org.apache.spark.mllib.linalg.distributed.RowMatrix = org.apache.spark.mllib.linalg.distributed.RowMatrix@75c66624
mat.rows.collect
res0: Array[org.apache.spark.mllib.linalg.Vector] = Array([12.0,-51.0,4.0], [6.0,167.0,-68.0], [-4.0,24.0,-41.0])
// Get its size.
val m = mat.numRows()
val n = mat.numCols()
m: Long = 3
n: Long = 3
// QR decomposition
val qrResult = mat.tallSkinnyQR(true)
qrResult: org.apache.spark.mllib.linalg.QRDecomposition[org.apache.spark.mllib.linalg.distributed.RowMatrix,org.apache.spark.mllib.linalg.Matrix] =
QRDecomposition(org.apache.spark.mllib.linalg.distributed.RowMatrix@34f5da4f,14.0 21.0 -14.0
0.0 -174.99999999999997 70.00000000000001
0.0 0.0 -35.000000000000014 )
qrResult.R
res1: org.apache.spark.mllib.linalg.Matrix =
14.0 21.0 -14.0
0.0 -174.99999999999997 70.00000000000001
0.0 0.0 -35.000000000000014
RowMatrix in Python
A RowMatrix can be created from an RDD of vectors.
Refer to the RowMatrix Python docs for more details on the API.
from pyspark.mllib.linalg.distributed import RowMatrix
# Create an RDD of vectors.
rows = sc.parallelize([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
# Create a RowMatrix from an RDD of vectors.
mat = RowMatrix(rows)
# Get its size.
m = mat.numRows() # 4
n = mat.numCols() # 3
print (m,'x',n)
# Get the rows as an RDD of vectors again.
rowsRDD = mat.rows
4 x 3
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
IndexedRowMatrix in Scala
An IndexedRowMatrix is similar to a RowMatrix but with meaningful row indices. It is backed by an RDD of indexed rows, so that each row is represented by its index (long-typed) and a local vector.
An IndexedRowMatrix can be created from an RDD[IndexedRow] instance, where IndexedRow is a wrapper over (Long, Vector). An IndexedRowMatrix can be converted to a RowMatrix by dropping its row indices.
Refer to the IndexedRowMatrix Scala docs for details on the API.
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.{IndexedRow, IndexedRowMatrix, RowMatrix}
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.distributed.{IndexedRow, IndexedRowMatrix, RowMatrix}
Vector(12.0, -51.0, 4.0) // note Vector is a scala collection
res0: scala.collection.immutable.Vector[Double] = Vector(12.0, -51.0, 4.0)
Vectors.dense(12.0, -51.0, 4.0) // while this is a mllib.linalg.Vector
res1: org.apache.spark.mllib.linalg.Vector = [12.0,-51.0,4.0]
val rows: RDD[IndexedRow] = sc.parallelize(Array(IndexedRow(2, Vectors.dense(1,3)), IndexedRow(4, Vectors.dense(4,5)))) // an RDD of indexed rows
rows: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.distributed.IndexedRow] = ParallelCollectionRDD[3685] at parallelize at command-2972105651607186:1
// Create an IndexedRowMatrix from an RDD[IndexedRow].
val mat: IndexedRowMatrix = new IndexedRowMatrix(rows)
mat: org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix = org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix@300e1e99
// Get its size.
val m = mat.numRows()
val n = mat.numCols()
m: Long = 5
n: Long = 2
// Drop its row indices.
val rowMat: RowMatrix = mat.toRowMatrix()
rowMat: org.apache.spark.mllib.linalg.distributed.RowMatrix = org.apache.spark.mllib.linalg.distributed.RowMatrix@425fc4a2
rowMat.rows.collect()
res2: Array[org.apache.spark.mllib.linalg.Vector] = Array([1.0,3.0], [4.0,5.0])
IndexedRowMatrix in Python
An IndexedRowMatrix can be created from an RDD of IndexedRows, where IndexedRow is a wrapper over (long, vector). An IndexedRowMatrix can be converted to a RowMatrix by dropping its row indices.
Refer to the IndexedRowMatrix Python docs for more details on the API.
from pyspark.mllib.linalg.distributed import IndexedRow, IndexedRowMatrix
# Create an RDD of indexed rows.
# - This can be done explicitly with the IndexedRow class:
indexedRows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
IndexedRow(1, [4, 5, 6]),
IndexedRow(2, [7, 8, 9]),
IndexedRow(3, [10, 11, 12])])
# - or by using (long, vector) tuples:
indexedRows = sc.parallelize([(0, [1, 2, 3]), (1, [4, 5, 6]),
(2, [7, 8, 9]), (3, [10, 11, 12])])
# Create an IndexedRowMatrix from an RDD of IndexedRows.
mat = IndexedRowMatrix(indexedRows)
# Get its size.
m = mat.numRows() # 4
n = mat.numCols() # 3
print (m,n)
# Get the rows as an RDD of IndexedRows.
rowsRDD = mat.rows
# Convert to a RowMatrix by dropping the row indices.
rowMat = mat.toRowMatrix()
# Convert to a CoordinateMatrix.
coordinateMat = mat.toCoordinateMatrix()
# Convert to a BlockMatrix.
blockMat = mat.toBlockMatrix()
4 3
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
CoordinateMatrix in Scala
A CoordinateMatrix is a distributed matrix backed by an RDD of its entries. Each entry is a tuple of (i: Long, j: Long, value: Double), where i is the row index, j is the column index, and value is the entry value. A CoordinateMatrix should be used only when both dimensions of the matrix are huge and the matrix is very sparse.
A CoordinateMatrix can be created from an RDD[MatrixEntry] instance, where MatrixEntry is a wrapper over (Long, Long, Double). A CoordinateMatrix can be converted to an IndexedRowMatrix with sparse rows by calling toIndexedRowMatrix. Other computations for CoordinateMatrix are not currently supported.
Refer to the CoordinateMatrix Scala docs for details on the API.
import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry}
import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry}
val entries: RDD[MatrixEntry] = sc.parallelize(Array(MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7))) // an RDD of matrix entries
entries: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.distributed.MatrixEntry] = ParallelCollectionRDD[3712] at parallelize at command-2972105651606627:1
// Create a CoordinateMatrix from an RDD[MatrixEntry].
val mat: CoordinateMatrix = new CoordinateMatrix(entries)
mat: org.apache.spark.mllib.linalg.distributed.CoordinateMatrix = org.apache.spark.mllib.linalg.distributed.CoordinateMatrix@56954954
// Get its size.
val m = mat.numRows()
val n = mat.numCols()
m: Long = 7
n: Long = 2
// Convert it to an IndexRowMatrix whose rows are sparse vectors.
val indexedRowMatrix = mat.toIndexedRowMatrix()
indexedRowMatrix: org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix = org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix@45570b82
indexedRowMatrix.rows.collect()
res0: Array[org.apache.spark.mllib.linalg.distributed.IndexedRow] = Array(IndexedRow(0,(2,[0],[1.2])), IndexedRow(1,(2,[0],[2.1])), IndexedRow(6,(2,[1],[3.7])))
CoordinateMatrix in Scala
A CoordinateMatrix can be created from an RDD of MatrixEntry entries, where MatrixEntry is a wrapper over (long, long, float). A CoordinateMatrix can be converted to a RowMatrix by calling toRowMatrix, or to an IndexedRowMatrix with sparse rows by calling toIndexedRowMatrix.
Refer to the CoordinateMatrix Python docs for more details on the API.
from pyspark.mllib.linalg.distributed import CoordinateMatrix, MatrixEntry
# Create an RDD of coordinate entries.
# - This can be done explicitly with the MatrixEntry class:
entries = sc.parallelize([MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7)])
# - or using (long, long, float) tuples:
entries = sc.parallelize([(0, 0, 1.2), (1, 0, 2.1), (2, 1, 3.7)])
# Create an CoordinateMatrix from an RDD of MatrixEntries.
mat = CoordinateMatrix(entries)
# Get its size.
m = mat.numRows() # 3
n = mat.numCols() # 2
print (m,n)
# Get the entries as an RDD of MatrixEntries.
entriesRDD = mat.entries
# Convert to a RowMatrix.
rowMat = mat.toRowMatrix()
# Convert to an IndexedRowMatrix.
indexedRowMat = mat.toIndexedRowMatrix()
# Convert to a BlockMatrix.
blockMat = mat.toBlockMatrix()
3 2
This is an elaboration of the Apache Spark mllib-progamming-guide on mllib-data-types.
Overview
Data Types - MLlib Programming Guide
MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by Breeze and jblas. A training example used in supervised learning is called a “labeled point” in MLlib.
BlockMatrix in Scala
A BlockMatrix is a distributed matrix backed by an RDD of MatrixBlocks, where a MatrixBlock is a tuple of ((Int, Int), Matrix), where the (Int, Int) is the index of the block, and Matrix is the sub-matrix at the given index with size rowsPerBlock x colsPerBlock. BlockMatrix supports methods such as add and multiply with another BlockMatrix. BlockMatrix also has a helper function validate which can be used to check whether the BlockMatrix is set up properly.
A BlockMatrix can be most easily created from an IndexedRowMatrix or CoordinateMatrix by calling toBlockMatrix. toBlockMatrix creates blocks of size 1024 x 1024 by default. Users may change the block size by supplying the values through toBlockMatrix(rowsPerBlock, colsPerBlock).
Refer to the BlockMatrix Scala docs for details on the API.
//import org.apache.spark.mllib.linalg.{Matrix, Matrices}
import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry}
import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry}
val entries: RDD[MatrixEntry] = sc.parallelize(Array(MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7))) // an RDD of matrix entries
entries: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.distributed.MatrixEntry] = ParallelCollectionRDD[3746] at parallelize at command-2972105651607062:1
// Create a CoordinateMatrix from an RDD[MatrixEntry].
val coordMat: CoordinateMatrix = new CoordinateMatrix(entries)
coordMat: org.apache.spark.mllib.linalg.distributed.CoordinateMatrix = org.apache.spark.mllib.linalg.distributed.CoordinateMatrix@161a1942
// Transform the CoordinateMatrix to a BlockMatrix
val matA: BlockMatrix = coordMat.toBlockMatrix().cache()
matA: org.apache.spark.mllib.linalg.distributed.BlockMatrix = org.apache.spark.mllib.linalg.distributed.BlockMatrix@1ddf85ce
// Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid.
// Nothing happens if it is valid.
matA.validate()
// Calculate A^T A.
val ata = matA.transpose.multiply(matA)
ata: org.apache.spark.mllib.linalg.distributed.BlockMatrix = org.apache.spark.mllib.linalg.distributed.BlockMatrix@18c0eeca
ata.blocks.collect()
res1: Array[((Int, Int), org.apache.spark.mllib.linalg.Matrix)] =
Array(((0,0),5.85 0.0
0.0 13.690000000000001 ))
ata.toLocalMatrix()
res2: org.apache.spark.mllib.linalg.Matrix =
5.85 0.0
0.0 13.690000000000001
BlockMatrix in Scala
A BlockMatrix can be created from an RDD of sub-matrix blocks, where a sub-matrix block is a ((blockRowIndex, blockColIndex), sub-matrix) tuple.
Refer to the BlockMatrix Python docs for more details on the API.
from pyspark.mllib.linalg import Matrices
from pyspark.mllib.linalg.distributed import BlockMatrix
# Create an RDD of sub-matrix blocks.
blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
# Create a BlockMatrix from an RDD of sub-matrix blocks.
mat = BlockMatrix(blocks, 3, 2)
# Get its size.
m = mat.numRows() # 6
n = mat.numCols() # 2
print (m,n)
# Get the blocks as an RDD of sub-matrix blocks.
blocksRDD = mat.blocks
# Convert to a LocalMatrix.
localMat = mat.toLocalMatrix()
# Convert to an IndexedRowMatrix.
indexedRowMat = mat.toIndexedRowMatrix()
# Convert to a CoordinateMatrix.
coordinateMat = mat.toCoordinateMatrix()
6 2
Power Plant ML Pipeline Application
This is an end-to-end example of using a number of different machine learning algorithms to solve a supervised regression problem.
Table of Contents
- Step 1: Business Understanding
- Step 2: Load Your Data
- Step 3: Explore Your Data
- Step 4: Visualize Your Data
- Step 5: Data Preparation
- Step 6: Data Modeling
- Step 7: Tuning and Evaluation
- Step 8: Deployment
We are trying to predict power output given a set of readings from various sensors in a gas-fired power generation plant. Power generation is a complex process, and understanding and predicting power output is an important element in managing a plant and its connection to the power grid.
More information about Peaker or Peaking Power Plants can be found on Wikipedia https://en.wikipedia.org/wiki/Peakingpowerplant
Given this business problem, we need to translate it to a Machine Learning task. The ML task is regression since the label (or target) we are trying to predict is numeric.
The example data is provided by UCI at UCI Machine Learning Repository Combined Cycle Power Plant Data Set
You can read the background on the UCI page, but in summary we have collected a number of readings from sensors at a Gas Fired Power Plant
(also called a Peaker Plant) and now we want to use those sensor readings to predict how much power the plant will generate.
More information about Machine Learning with Spark can be found in the Spark MLLib Programming Guide
Please note this example only works with Spark version 1.4 or higher
To Rerun Steps 1-4 done in the notebook at:
Workspace -> PATH_TO -> 009_PowerPlantPipeline_01ETLEDA]
just run the following command as shown in the cell below:
%run "/PATH_TO/009_PowerPlantPipeline_01ETLEDA"
-
Note: If you already evaluated the
%run ...command above then:- first delete the cell by pressing on
xon the top-right corner of the cell and - revaluate the
runcommand above.
- first delete the cell by pressing on
"../009_PowerPlantPipeline_01ETLEDA"
Now we will do the following Steps:
Step 5: Data Preparation,
Step 6: Modeling, and
Step 7: Tuning and Evaluation
We will do Step 8: Deployment later after we get introduced to SparkStreaming.
Step 5: Data Preparation
The next step is to prepare the data. Since all of this data is numeric and consistent, this is a simple task for us today.
We will need to convert the predictor features from columns to Feature Vectors using the org.apache.spark.ml.feature.VectorAssembler
The VectorAssembler will be the first step in building our ML pipeline.
res2: Int = 301
//Let's quickly recall the schema and make sure our table is here now
table("power_plant_table").printSchema
root
|-- AT: double (nullable = true)
|-- V: double (nullable = true)
|-- AP: double (nullable = true)
|-- RH: double (nullable = true)
|-- PE: double (nullable = true)
| path | name | size |
|---|---|---|
| dbfs:/databricks-datasets/power-plant/data/Sheet1.tsv | Sheet1.tsv | 308693.0 |
| dbfs:/databricks-datasets/power-plant/data/Sheet2.tsv | Sheet2.tsv | 308693.0 |
| dbfs:/databricks-datasets/power-plant/data/Sheet3.tsv | Sheet3.tsv | 308693.0 |
| dbfs:/databricks-datasets/power-plant/data/Sheet4.tsv | Sheet4.tsv | 308693.0 |
| dbfs:/databricks-datasets/power-plant/data/Sheet5.tsv | Sheet5.tsv | 308693.0 |
powerPlantRDD: org.apache.spark.rdd.RDD[String] = /databricks-datasets/power-plant/data/Sheet1.tsv MapPartitionsRDD[1] at textFile at command-1267216879634554:1
powerPlantDF // make sure we have the DataFrame too
res82: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 3 more fields]
AT V AP RH PE
14.96 41.76 1024.07 73.17 463.26
25.18 62.96 1020.04 59.08 444.37
5.11 39.4 1012.16 92.14 488.56
20.86 57.32 1010.24 76.64 446.48
powerPlantDF: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 3 more fields]
root
|-- AT: double (nullable = true)
|-- V: double (nullable = true)
|-- AP: double (nullable = true)
|-- RH: double (nullable = true)
|-- PE: double (nullable = true)
import org.apache.spark.ml.feature.VectorAssembler
// make a DataFrame called dataset from the table
val dataset = sqlContext.table("power_plant_table")
val vectorizer = new VectorAssembler()
.setInputCols(Array("AT", "V", "AP", "RH"))
.setOutputCol("features")
import org.apache.spark.ml.feature.VectorAssembler
dataset: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 3 more fields]
vectorizer: org.apache.spark.ml.feature.VectorAssembler = VectorAssembler: uid=vecAssembler_ea4ed428c7e4, handleInvalid=error, numInputCols=4
res9: Long = 9568
+-----+-----+-------+-----+------+
| AT| V| AP| RH| PE|
+-----+-----+-------+-----+------+
|14.96|41.76|1024.07|73.17|463.26|
|25.18|62.96|1020.04|59.08|444.37|
| 5.11| 39.4|1012.16|92.14|488.56|
|20.86|57.32|1010.24|76.64|446.48|
|10.82| 37.5|1009.23|96.62| 473.9|
|26.27|59.44|1012.23|58.77|443.67|
|15.89|43.96|1014.02|75.24|467.35|
| 9.48|44.71|1019.12|66.43|478.42|
|14.64| 45.0|1021.78|41.25|475.98|
|11.74|43.56|1015.14|70.72| 477.5|
+-----+-----+-------+-----+------+
only showing top 10 rows
| AT | V | AP | RH | PE |
|---|---|---|---|---|
| 14.96 | 41.76 | 1024.07 | 73.17 | 463.26 |
| 25.18 | 62.96 | 1020.04 | 59.08 | 444.37 |
| 5.11 | 39.4 | 1012.16 | 92.14 | 488.56 |
| 20.86 | 57.32 | 1010.24 | 76.64 | 446.48 |
| 10.82 | 37.5 | 1009.23 | 96.62 | 473.9 |
| 26.27 | 59.44 | 1012.23 | 58.77 | 443.67 |
| 15.89 | 43.96 | 1014.02 | 75.24 | 467.35 |
| 9.48 | 44.71 | 1019.12 | 66.43 | 478.42 |
| 14.64 | 45.0 | 1021.78 | 41.25 | 475.98 |
| 11.74 | 43.56 | 1015.14 | 70.72 | 477.5 |
| 17.99 | 43.72 | 1008.64 | 75.04 | 453.02 |
| 20.14 | 46.93 | 1014.66 | 64.22 | 453.99 |
| 24.34 | 73.5 | 1011.31 | 84.15 | 440.29 |
| 25.71 | 58.59 | 1012.77 | 61.83 | 451.28 |
| 26.19 | 69.34 | 1009.48 | 87.59 | 433.99 |
| 21.42 | 43.79 | 1015.76 | 43.08 | 462.19 |
| 18.21 | 45.0 | 1022.86 | 48.84 | 467.54 |
| 11.04 | 41.74 | 1022.6 | 77.51 | 477.2 |
| 14.45 | 52.75 | 1023.97 | 63.59 | 459.85 |
| 13.97 | 38.47 | 1015.15 | 55.28 | 464.3 |
| 17.76 | 42.42 | 1009.09 | 66.26 | 468.27 |
| 5.41 | 40.07 | 1019.16 | 64.77 | 495.24 |
| 7.76 | 42.28 | 1008.52 | 83.31 | 483.8 |
| 27.23 | 63.9 | 1014.3 | 47.19 | 443.61 |
| 27.36 | 48.6 | 1003.18 | 54.93 | 436.06 |
| 27.47 | 70.72 | 1009.97 | 74.62 | 443.25 |
| 14.6 | 39.31 | 1011.11 | 72.52 | 464.16 |
| 7.91 | 39.96 | 1023.57 | 88.44 | 475.52 |
| 5.81 | 35.79 | 1012.14 | 92.28 | 484.41 |
| 30.53 | 65.18 | 1012.69 | 41.85 | 437.89 |
| 23.87 | 63.94 | 1019.02 | 44.28 | 445.11 |
| 26.09 | 58.41 | 1013.64 | 64.58 | 438.86 |
| 29.27 | 66.85 | 1011.11 | 63.25 | 440.98 |
| 27.38 | 74.16 | 1010.08 | 78.61 | 436.65 |
| 24.81 | 63.94 | 1018.76 | 44.51 | 444.26 |
| 12.75 | 44.03 | 1007.29 | 89.46 | 465.86 |
| 24.66 | 63.73 | 1011.4 | 74.52 | 444.37 |
| 16.38 | 47.45 | 1010.08 | 88.86 | 450.69 |
| 13.91 | 39.35 | 1014.69 | 75.51 | 469.02 |
| 23.18 | 51.3 | 1012.04 | 78.64 | 448.86 |
| 22.47 | 47.45 | 1007.62 | 76.65 | 447.14 |
| 13.39 | 44.85 | 1017.24 | 80.44 | 469.18 |
| 9.28 | 41.54 | 1018.33 | 79.89 | 482.8 |
| 11.82 | 42.86 | 1014.12 | 88.28 | 476.7 |
| 10.27 | 40.64 | 1020.63 | 84.6 | 474.99 |
| 22.92 | 63.94 | 1019.28 | 42.69 | 444.22 |
| 16.0 | 37.87 | 1020.24 | 78.41 | 461.33 |
| 21.22 | 43.43 | 1010.96 | 61.07 | 448.06 |
| 13.46 | 44.71 | 1014.51 | 50.0 | 474.6 |
| 9.39 | 40.11 | 1029.14 | 77.29 | 473.05 |
| 31.07 | 73.5 | 1010.58 | 43.66 | 432.06 |
| 12.82 | 38.62 | 1018.71 | 83.8 | 467.41 |
| 32.57 | 78.92 | 1011.6 | 66.47 | 430.12 |
| 8.11 | 42.18 | 1014.82 | 93.09 | 473.62 |
| 13.92 | 39.39 | 1012.94 | 80.52 | 471.81 |
| 23.04 | 59.43 | 1010.23 | 68.99 | 442.99 |
| 27.31 | 64.44 | 1014.65 | 57.27 | 442.77 |
| 5.91 | 39.33 | 1010.18 | 95.53 | 491.49 |
| 25.26 | 61.08 | 1013.68 | 71.72 | 447.46 |
| 27.97 | 58.84 | 1002.25 | 57.88 | 446.11 |
| 26.08 | 52.3 | 1007.03 | 63.34 | 442.44 |
| 29.01 | 65.71 | 1013.61 | 48.07 | 446.22 |
| 12.18 | 40.1 | 1016.67 | 91.87 | 471.49 |
| 13.76 | 45.87 | 1008.89 | 87.27 | 463.5 |
| 25.5 | 58.79 | 1016.02 | 64.4 | 440.01 |
| 28.26 | 65.34 | 1014.56 | 43.4 | 441.03 |
| 21.39 | 62.96 | 1019.49 | 72.24 | 452.68 |
| 7.26 | 40.69 | 1020.43 | 90.22 | 474.91 |
| 10.54 | 34.03 | 1018.71 | 74.0 | 478.77 |
| 27.71 | 74.34 | 998.14 | 71.85 | 434.2 |
| 23.11 | 68.3 | 1017.83 | 86.62 | 437.91 |
| 7.51 | 41.01 | 1024.61 | 97.41 | 477.61 |
| 26.46 | 74.67 | 1016.65 | 84.44 | 431.65 |
| 29.34 | 74.34 | 998.58 | 81.55 | 430.57 |
| 10.32 | 42.28 | 1008.82 | 75.66 | 481.09 |
| 22.74 | 61.02 | 1009.56 | 79.41 | 445.56 |
| 13.48 | 39.85 | 1012.71 | 58.91 | 475.74 |
| 25.52 | 69.75 | 1010.36 | 90.06 | 435.12 |
| 21.58 | 67.25 | 1017.39 | 79.0 | 446.15 |
| 27.66 | 76.86 | 1001.31 | 69.47 | 436.64 |
| 26.96 | 69.45 | 1013.89 | 51.47 | 436.69 |
| 12.29 | 42.18 | 1016.53 | 83.13 | 468.75 |
| 15.86 | 43.02 | 1012.18 | 40.33 | 466.6 |
| 13.87 | 45.08 | 1024.42 | 81.69 | 465.48 |
| 24.09 | 73.68 | 1014.93 | 94.55 | 441.34 |
| 20.45 | 69.45 | 1012.53 | 91.81 | 441.83 |
| 15.07 | 39.3 | 1019.0 | 63.62 | 464.7 |
| 32.72 | 69.75 | 1009.6 | 49.35 | 437.99 |
| 18.23 | 58.96 | 1015.55 | 69.61 | 459.12 |
| 35.56 | 68.94 | 1006.56 | 38.75 | 429.69 |
| 18.36 | 51.43 | 1010.57 | 90.17 | 459.8 |
| 26.35 | 64.05 | 1009.81 | 81.24 | 433.63 |
| 25.92 | 60.95 | 1014.62 | 48.46 | 442.84 |
| 8.01 | 41.66 | 1014.49 | 76.72 | 485.13 |
| 19.63 | 52.72 | 1025.09 | 51.16 | 459.12 |
| 20.02 | 67.32 | 1012.05 | 76.34 | 445.31 |
| 10.08 | 40.72 | 1022.7 | 67.3 | 480.8 |
| 27.23 | 66.48 | 1005.23 | 52.38 | 432.55 |
| 23.37 | 63.77 | 1013.42 | 76.44 | 443.86 |
| 18.74 | 59.21 | 1018.3 | 91.55 | 449.77 |
| 14.81 | 43.69 | 1017.19 | 71.9 | 470.71 |
| 23.1 | 51.3 | 1011.93 | 80.05 | 452.17 |
| 10.72 | 41.38 | 1021.6 | 63.77 | 478.29 |
| 29.46 | 71.94 | 1006.96 | 62.26 | 428.54 |
| 8.1 | 40.64 | 1020.66 | 89.04 | 478.27 |
| 27.29 | 62.66 | 1007.63 | 58.02 | 439.58 |
| 17.1 | 49.69 | 1005.53 | 81.82 | 457.32 |
| 11.49 | 44.2 | 1018.79 | 91.14 | 475.51 |
| 23.69 | 65.59 | 1010.85 | 88.92 | 439.66 |
| 13.51 | 40.89 | 1011.03 | 84.83 | 471.99 |
| 9.64 | 39.35 | 1015.1 | 91.76 | 479.81 |
| 25.65 | 78.92 | 1010.83 | 86.56 | 434.78 |
| 21.59 | 61.87 | 1011.18 | 57.21 | 446.58 |
| 27.98 | 58.33 | 1013.92 | 54.25 | 437.76 |
| 18.8 | 39.72 | 1001.24 | 63.8 | 459.36 |
| 18.28 | 44.71 | 1016.99 | 33.71 | 462.28 |
| 13.55 | 43.48 | 1016.08 | 67.25 | 464.33 |
| 22.99 | 46.21 | 1010.71 | 60.11 | 444.36 |
| 23.94 | 59.39 | 1014.32 | 74.55 | 438.64 |
| 13.74 | 34.03 | 1018.69 | 67.34 | 470.49 |
| 21.3 | 41.1 | 1001.86 | 42.75 | 455.13 |
| 27.54 | 66.93 | 1017.06 | 55.2 | 450.22 |
| 24.81 | 63.73 | 1009.34 | 83.61 | 440.43 |
| 4.97 | 42.85 | 1014.02 | 88.78 | 482.98 |
| 15.22 | 50.88 | 1014.19 | 100.12 | 460.44 |
| 23.88 | 54.2 | 1012.81 | 64.52 | 444.97 |
| 33.01 | 68.67 | 1005.2 | 51.41 | 433.94 |
| 25.98 | 73.18 | 1012.28 | 85.78 | 439.73 |
| 28.18 | 73.88 | 1005.89 | 75.41 | 434.48 |
| 21.67 | 60.84 | 1017.93 | 81.63 | 442.33 |
| 17.67 | 45.09 | 1014.26 | 51.92 | 457.67 |
| 21.37 | 57.76 | 1018.8 | 70.12 | 454.66 |
| 28.69 | 67.25 | 1017.71 | 53.83 | 432.21 |
| 16.61 | 43.77 | 1012.25 | 77.23 | 457.66 |
| 27.91 | 63.76 | 1010.27 | 65.67 | 435.21 |
| 20.97 | 47.43 | 1007.64 | 71.18 | 448.22 |
| 10.8 | 41.66 | 1013.79 | 81.96 | 475.51 |
| 20.61 | 62.91 | 1013.24 | 79.54 | 446.53 |
| 25.45 | 57.32 | 1011.7 | 47.09 | 441.3 |
| 30.16 | 69.34 | 1007.67 | 57.69 | 433.54 |
| 4.99 | 39.04 | 1020.45 | 78.89 | 472.52 |
| 10.51 | 44.78 | 1012.59 | 85.29 | 474.77 |
| 33.79 | 69.05 | 1001.62 | 40.13 | 435.1 |
| 21.34 | 59.8 | 1016.92 | 77.06 | 450.74 |
| 23.4 | 65.06 | 1014.32 | 67.38 | 442.7 |
| 32.21 | 68.14 | 1003.34 | 62.44 | 426.56 |
| 14.26 | 42.32 | 1016.0 | 77.43 | 463.71 |
| 27.71 | 66.93 | 1016.85 | 58.77 | 447.06 |
| 21.95 | 57.76 | 1018.02 | 67.72 | 452.27 |
| 25.76 | 63.94 | 1018.49 | 42.14 | 445.78 |
| 23.68 | 68.3 | 1017.93 | 84.16 | 438.65 |
| 8.28 | 40.77 | 1011.55 | 89.79 | 480.15 |
| 23.44 | 62.52 | 1016.46 | 67.21 | 447.19 |
| 25.32 | 48.41 | 1008.47 | 72.14 | 443.04 |
| 3.94 | 39.9 | 1008.06 | 97.49 | 488.81 |
| 17.3 | 57.76 | 1016.26 | 87.74 | 455.75 |
| 18.2 | 49.39 | 1018.83 | 96.3 | 455.86 |
| 21.43 | 46.97 | 1013.94 | 61.25 | 457.68 |
| 11.16 | 40.05 | 1014.95 | 88.38 | 479.11 |
| 30.38 | 74.16 | 1007.44 | 74.77 | 432.84 |
| 23.36 | 62.52 | 1016.18 | 68.18 | 448.37 |
| 21.69 | 47.45 | 1007.56 | 77.2 | 447.06 |
| 23.62 | 49.21 | 1014.1 | 49.54 | 443.53 |
| 21.87 | 61.45 | 1011.13 | 92.22 | 445.21 |
| 29.25 | 66.51 | 1015.53 | 33.65 | 441.7 |
| 20.03 | 66.86 | 1013.05 | 64.59 | 450.93 |
| 18.14 | 49.78 | 1002.95 | 100.09 | 451.44 |
| 24.23 | 56.89 | 1012.32 | 68.04 | 441.29 |
| 18.11 | 44.85 | 1014.48 | 48.94 | 458.85 |
| 6.57 | 43.65 | 1018.24 | 74.47 | 481.46 |
| 12.56 | 43.41 | 1016.93 | 81.02 | 467.19 |
| 13.4 | 41.58 | 1020.5 | 71.17 | 461.54 |
| 27.1 | 52.84 | 1006.28 | 53.85 | 439.08 |
| 14.28 | 42.74 | 1028.79 | 70.67 | 467.22 |
| 16.29 | 44.34 | 1019.49 | 59.36 | 468.8 |
| 31.24 | 71.98 | 1004.66 | 57.17 | 426.93 |
| 10.57 | 37.73 | 1024.36 | 70.29 | 474.65 |
| 13.8 | 44.21 | 1022.93 | 83.37 | 468.97 |
| 25.3 | 71.58 | 1010.18 | 87.36 | 433.97 |
| 18.06 | 50.16 | 1009.52 | 100.09 | 450.53 |
| 25.42 | 59.04 | 1011.98 | 68.78 | 444.51 |
| 15.07 | 40.69 | 1015.29 | 70.98 | 469.03 |
| 11.75 | 71.14 | 1019.36 | 75.68 | 466.56 |
| 20.23 | 52.05 | 1012.15 | 47.49 | 457.57 |
| 27.31 | 59.54 | 1006.24 | 71.99 | 440.13 |
| 28.57 | 69.84 | 1003.57 | 66.55 | 433.24 |
| 17.9 | 43.72 | 1008.64 | 74.73 | 452.55 |
| 23.83 | 71.37 | 1002.04 | 64.78 | 443.29 |
| 27.92 | 74.99 | 1005.47 | 75.13 | 431.76 |
| 17.34 | 44.78 | 1007.81 | 56.38 | 454.97 |
| 17.94 | 63.07 | 1012.42 | 94.35 | 456.7 |
| 6.4 | 39.9 | 1007.75 | 86.55 | 486.03 |
| 11.78 | 39.96 | 1011.37 | 82.95 | 472.79 |
| 20.28 | 57.25 | 1010.12 | 88.42 | 452.03 |
| 21.04 | 54.2 | 1012.26 | 85.61 | 443.41 |
| 25.11 | 67.32 | 1014.49 | 58.39 | 441.93 |
| 30.28 | 70.98 | 1007.51 | 74.28 | 432.64 |
| 8.14 | 36.24 | 1013.15 | 87.85 | 480.25 |
| 16.86 | 39.63 | 1004.47 | 83.5 | 466.68 |
| 6.25 | 40.07 | 1020.19 | 65.24 | 494.39 |
| 22.35 | 54.42 | 1012.46 | 75.01 | 454.72 |
| 17.98 | 56.85 | 1012.28 | 84.52 | 448.71 |
| 21.19 | 42.48 | 1013.43 | 80.52 | 469.76 |
| 20.94 | 44.89 | 1009.64 | 75.14 | 450.71 |
| 24.23 | 58.79 | 1009.8 | 75.75 | 444.01 |
| 19.18 | 58.2 | 1017.46 | 76.72 | 453.2 |
| 20.88 | 57.85 | 1012.39 | 85.47 | 450.87 |
| 23.67 | 63.86 | 1019.67 | 57.95 | 441.73 |
| 14.12 | 39.52 | 1018.41 | 78.32 | 465.09 |
| 25.23 | 64.63 | 1020.59 | 52.2 | 447.28 |
| 6.54 | 39.33 | 1011.54 | 93.69 | 491.16 |
| 20.08 | 62.52 | 1017.99 | 75.74 | 450.98 |
| 24.67 | 63.56 | 1013.75 | 67.56 | 446.3 |
| 27.82 | 79.74 | 1008.37 | 69.46 | 436.48 |
| 15.55 | 42.03 | 1017.41 | 74.58 | 460.84 |
| 24.26 | 69.51 | 1013.43 | 53.23 | 442.56 |
| 13.45 | 41.49 | 1020.19 | 88.72 | 467.3 |
| 11.06 | 40.64 | 1021.47 | 96.16 | 479.13 |
| 24.91 | 52.3 | 1008.72 | 68.26 | 441.15 |
| 22.39 | 59.04 | 1011.78 | 86.39 | 445.52 |
| 11.95 | 40.69 | 1015.62 | 85.34 | 475.4 |
| 14.85 | 40.69 | 1014.91 | 72.64 | 469.3 |
| 10.11 | 41.62 | 1017.17 | 97.82 | 463.57 |
| 23.67 | 68.67 | 1006.71 | 77.22 | 445.32 |
| 16.14 | 44.21 | 1020.36 | 80.59 | 461.03 |
| 15.11 | 43.13 | 1014.99 | 46.91 | 466.74 |
| 24.14 | 59.87 | 1018.47 | 57.76 | 444.04 |
| 30.08 | 67.25 | 1017.6 | 53.09 | 434.01 |
| 14.77 | 44.9 | 1020.5 | 84.31 | 465.23 |
| 27.6 | 69.34 | 1009.63 | 71.58 | 440.6 |
| 13.89 | 44.84 | 1023.66 | 92.97 | 466.74 |
| 26.85 | 75.6 | 1017.43 | 74.55 | 433.48 |
| 12.41 | 40.96 | 1023.36 | 78.96 | 473.59 |
| 13.08 | 41.74 | 1020.75 | 64.44 | 474.81 |
| 18.93 | 44.06 | 1017.58 | 68.23 | 454.75 |
| 20.5 | 49.69 | 1009.6 | 70.81 | 452.94 |
| 30.72 | 69.13 | 1009.94 | 61.66 | 435.83 |
| 7.55 | 39.22 | 1014.53 | 77.76 | 482.19 |
| 13.49 | 44.47 | 1030.46 | 69.49 | 466.66 |
| 15.62 | 40.12 | 1013.03 | 96.26 | 462.59 |
| 24.8 | 64.63 | 1020.69 | 55.74 | 447.82 |
| 10.03 | 41.62 | 1014.55 | 95.61 | 462.73 |
| 22.43 | 63.21 | 1012.06 | 84.75 | 447.98 |
| 14.95 | 39.31 | 1009.15 | 75.3 | 462.72 |
| 24.78 | 58.46 | 1016.82 | 67.5 | 442.42 |
| 23.2 | 48.41 | 1008.64 | 80.92 | 444.69 |
| 14.01 | 39.0 | 1016.73 | 79.23 | 466.7 |
| 19.4 | 64.63 | 1020.38 | 81.1 | 453.84 |
| 30.15 | 67.32 | 1013.83 | 32.8 | 436.92 |
| 6.91 | 36.08 | 1021.82 | 84.31 | 486.37 |
| 29.04 | 60.07 | 1015.42 | 46.15 | 440.43 |
| 26.02 | 63.07 | 1010.94 | 53.96 | 446.82 |
| 5.89 | 39.48 | 1005.11 | 59.83 | 484.91 |
| 26.52 | 71.64 | 1008.27 | 75.3 | 437.76 |
| 28.53 | 68.08 | 1013.27 | 42.53 | 438.91 |
| 16.59 | 39.54 | 1007.97 | 70.58 | 464.19 |
| 22.95 | 67.79 | 1009.89 | 91.69 | 442.19 |
| 23.96 | 47.43 | 1008.38 | 63.55 | 446.86 |
| 17.48 | 44.2 | 1018.89 | 61.51 | 457.15 |
| 6.69 | 43.65 | 1020.14 | 69.55 | 482.57 |
| 10.25 | 41.26 | 1007.44 | 98.08 | 476.03 |
| 28.87 | 72.58 | 1008.69 | 79.34 | 428.89 |
| 12.04 | 40.23 | 1018.07 | 81.28 | 472.7 |
| 22.58 | 52.3 | 1009.04 | 78.99 | 445.6 |
| 15.12 | 52.05 | 1014.63 | 80.38 | 464.78 |
| 25.48 | 58.95 | 1017.02 | 51.16 | 440.42 |
| 27.87 | 70.79 | 1003.96 | 72.17 | 428.41 |
| 23.72 | 70.47 | 1010.65 | 75.39 | 438.5 |
| 25.0 | 59.43 | 1007.84 | 68.91 | 438.28 |
| 8.42 | 40.64 | 1022.35 | 96.38 | 476.29 |
| 22.46 | 58.49 | 1011.5 | 70.54 | 448.46 |
| 29.92 | 57.19 | 1008.62 | 45.8 | 438.99 |
| 11.68 | 39.22 | 1017.9 | 57.95 | 471.8 |
| 14.04 | 42.44 | 1012.74 | 81.89 | 471.81 |
| 19.86 | 59.14 | 1016.12 | 69.32 | 449.82 |
| 25.99 | 68.08 | 1013.13 | 59.14 | 442.14 |
| 23.42 | 58.79 | 1009.74 | 81.54 | 441.46 |
| 10.6 | 40.22 | 1011.37 | 85.81 | 477.62 |
| 20.97 | 61.87 | 1011.45 | 65.41 | 446.76 |
| 14.14 | 39.82 | 1012.46 | 81.15 | 472.52 |
| 8.56 | 40.71 | 1021.27 | 95.87 | 471.58 |
| 24.86 | 72.39 | 1001.15 | 90.24 | 440.85 |
| 29.0 | 77.54 | 1011.33 | 75.13 | 431.37 |
| 27.59 | 71.97 | 1008.64 | 88.22 | 437.33 |
| 10.45 | 40.71 | 1015.68 | 81.48 | 469.22 |
| 8.51 | 40.78 | 1023.51 | 89.84 | 471.11 |
| 29.82 | 66.51 | 1010.98 | 43.57 | 439.17 |
| 22.56 | 62.26 | 1012.11 | 63.16 | 445.33 |
| 11.38 | 39.22 | 1018.62 | 57.14 | 473.71 |
| 20.25 | 57.76 | 1016.28 | 77.76 | 452.66 |
| 22.42 | 59.43 | 1007.12 | 90.56 | 440.99 |
| 14.85 | 38.91 | 1014.48 | 60.98 | 467.42 |
| 25.62 | 58.82 | 1010.02 | 70.31 | 444.14 |
| 19.85 | 56.53 | 1020.57 | 74.05 | 457.17 |
| 13.67 | 54.3 | 1015.92 | 75.42 | 467.87 |
| 24.39 | 70.72 | 1009.78 | 82.25 | 442.04 |
| 16.07 | 44.58 | 1019.52 | 67.95 | 471.36 |
| 11.6 | 39.1 | 1009.81 | 100.09 | 460.7 |
| 31.38 | 70.83 | 1010.35 | 47.28 | 431.33 |
| 29.91 | 76.86 | 998.59 | 72.41 | 432.6 |
| 19.67 | 59.39 | 1014.07 | 77.67 | 447.61 |
| 27.18 | 64.79 | 1016.27 | 63.7 | 443.87 |
| 21.39 | 52.3 | 1009.2 | 79.77 | 446.87 |
| 10.45 | 41.01 | 1020.57 | 93.84 | 465.74 |
| 19.46 | 56.89 | 1014.02 | 84.95 | 447.86 |
| 23.55 | 62.96 | 1020.16 | 70.16 | 447.65 |
| 23.35 | 63.47 | 1011.78 | 84.24 | 437.87 |
| 9.26 | 41.66 | 1016.87 | 73.32 | 483.51 |
| 10.3 | 41.46 | 1018.21 | 86.17 | 479.65 |
| 20.94 | 58.16 | 1016.88 | 65.43 | 455.16 |
| 23.13 | 71.25 | 1002.49 | 94.59 | 431.91 |
| 12.77 | 41.5 | 1014.13 | 86.8 | 470.68 |
| 28.29 | 69.13 | 1009.29 | 58.18 | 429.28 |
| 19.13 | 59.21 | 1018.32 | 89.66 | 450.81 |
| 24.44 | 73.5 | 1011.49 | 87.39 | 437.73 |
| 20.32 | 44.6 | 1015.16 | 36.35 | 460.21 |
| 20.54 | 69.05 | 1001.6 | 79.62 | 442.86 |
| 12.16 | 45.0 | 1021.51 | 50.52 | 482.99 |
| 28.09 | 65.27 | 1013.27 | 51.96 | 440.0 |
| 9.25 | 41.82 | 1033.25 | 74.73 | 478.48 |
| 21.75 | 49.82 | 1015.01 | 78.33 | 455.28 |
| 23.7 | 66.56 | 1002.07 | 85.19 | 436.94 |
| 16.22 | 37.87 | 1022.36 | 83.13 | 461.06 |
| 24.75 | 69.45 | 1013.97 | 53.49 | 438.28 |
| 10.48 | 39.58 | 1011.81 | 88.86 | 472.61 |
| 29.53 | 70.79 | 1003.7 | 60.89 | 426.85 |
| 12.59 | 39.72 | 1017.76 | 61.14 | 470.18 |
| 23.5 | 54.42 | 1012.31 | 68.29 | 455.38 |
| 29.01 | 66.56 | 1006.44 | 57.62 | 428.32 |
| 9.75 | 42.49 | 1010.57 | 83.63 | 480.35 |
| 19.55 | 56.53 | 1020.2 | 78.1 | 455.56 |
| 21.05 | 58.33 | 1013.14 | 66.34 | 447.66 |
| 24.72 | 68.67 | 1006.74 | 79.02 | 443.06 |
| 21.19 | 58.86 | 1014.19 | 68.96 | 452.43 |
| 10.77 | 41.54 | 1019.94 | 71.13 | 477.81 |
| 28.68 | 73.77 | 1004.72 | 87.01 | 431.66 |
| 29.87 | 73.91 | 1004.53 | 74.3 | 431.8 |
| 22.99 | 68.67 | 1006.65 | 77.62 | 446.67 |
| 24.66 | 60.29 | 1018.0 | 59.56 | 445.26 |
| 32.63 | 69.89 | 1013.85 | 41.66 | 425.72 |
| 31.38 | 72.29 | 1008.73 | 73.27 | 430.58 |
| 23.87 | 60.27 | 1018.94 | 77.16 | 439.86 |
| 25.6 | 59.15 | 1013.31 | 67.02 | 441.11 |
| 27.62 | 71.14 | 1011.6 | 52.8 | 434.72 |
| 30.1 | 67.45 | 1014.23 | 39.04 | 434.01 |
| 12.19 | 41.17 | 1019.43 | 65.47 | 475.64 |
| 13.11 | 41.58 | 1020.43 | 74.32 | 460.44 |
| 28.29 | 68.67 | 1005.46 | 69.22 | 436.4 |
| 13.45 | 40.73 | 1018.7 | 93.88 | 461.03 |
| 10.98 | 41.54 | 1019.94 | 69.83 | 479.08 |
| 26.48 | 69.14 | 1009.31 | 84.11 | 435.76 |
| 13.07 | 45.51 | 1015.22 | 78.65 | 460.14 |
| 25.56 | 75.6 | 1017.37 | 69.31 | 442.2 |
| 22.68 | 50.78 | 1008.83 | 70.3 | 447.69 |
| 28.86 | 73.67 | 1006.65 | 68.23 | 431.15 |
| 22.7 | 63.56 | 1014.32 | 71.76 | 445.0 |
| 27.89 | 73.21 | 1001.32 | 85.88 | 431.59 |
| 13.78 | 44.47 | 1027.94 | 71.09 | 467.22 |
| 28.14 | 51.43 | 1012.16 | 52.67 | 445.33 |
| 11.8 | 45.09 | 1013.21 | 89.68 | 470.57 |
| 10.71 | 39.61 | 1018.72 | 73.66 | 473.77 |
| 24.54 | 60.29 | 1017.42 | 58.94 | 447.67 |
| 11.54 | 40.05 | 1014.78 | 87.05 | 474.29 |
| 29.47 | 71.32 | 1008.07 | 67.0 | 437.14 |
| 29.24 | 69.05 | 1003.12 | 43.18 | 432.56 |
| 14.51 | 41.79 | 1009.72 | 80.62 | 459.14 |
| 22.91 | 60.07 | 1016.03 | 59.72 | 446.19 |
| 27.02 | 71.77 | 1006.38 | 72.1 | 428.1 |
| 13.49 | 44.47 | 1030.18 | 69.15 | 468.46 |
| 30.24 | 66.75 | 1017.95 | 55.66 | 435.02 |
| 23.19 | 48.6 | 1002.38 | 61.19 | 445.52 |
| 17.73 | 40.55 | 1003.36 | 74.62 | 462.69 |
| 18.62 | 61.27 | 1019.26 | 73.35 | 455.75 |
| 12.85 | 40.0 | 1015.89 | 68.85 | 463.74 |
| 32.33 | 69.68 | 1011.95 | 39.89 | 439.79 |
| 25.09 | 58.95 | 1016.99 | 53.16 | 443.26 |
| 29.45 | 69.13 | 1009.3 | 52.97 | 432.04 |
| 16.91 | 43.96 | 1013.32 | 79.87 | 465.86 |
| 14.09 | 45.87 | 1009.05 | 84.09 | 465.6 |
| 10.73 | 25.36 | 1009.35 | 100.15 | 469.43 |
| 23.2 | 49.3 | 1003.4 | 79.77 | 440.75 |
| 8.21 | 38.91 | 1015.82 | 88.99 | 481.32 |
| 9.3 | 40.56 | 1022.64 | 76.14 | 479.87 |
| 16.97 | 39.16 | 1005.7 | 69.13 | 458.59 |
| 23.69 | 71.97 | 1009.62 | 93.03 | 438.62 |
| 25.13 | 59.44 | 1012.38 | 77.92 | 445.59 |
| 9.86 | 43.56 | 1015.13 | 74.89 | 481.87 |
| 11.33 | 41.5 | 1013.58 | 88.7 | 475.01 |
| 26.95 | 48.41 | 1008.53 | 62.94 | 436.54 |
| 15.0 | 40.66 | 1016.28 | 89.62 | 456.63 |
| 20.76 | 62.52 | 1015.63 | 81.04 | 451.69 |
| 14.29 | 39.59 | 1010.93 | 94.53 | 463.04 |
| 19.74 | 67.71 | 1007.68 | 64.02 | 446.1 |
| 26.68 | 59.92 | 1009.94 | 70.57 | 438.67 |
| 14.24 | 41.4 | 1019.7 | 70.32 | 466.88 |
| 21.98 | 48.41 | 1008.42 | 84.86 | 444.6 |
| 22.75 | 59.39 | 1015.4 | 81.41 | 440.26 |
| 8.34 | 40.96 | 1023.28 | 89.45 | 483.92 |
| 11.8 | 41.2 | 1017.18 | 82.71 | 475.19 |
| 8.81 | 44.68 | 1023.06 | 93.93 | 479.24 |
| 30.05 | 73.68 | 1014.95 | 70.6 | 434.92 |
| 16.01 | 65.46 | 1014.0 | 87.68 | 454.16 |
| 21.75 | 58.79 | 1012.42 | 87.58 | 447.58 |
| 13.94 | 41.26 | 1021.67 | 74.4 | 467.9 |
| 29.25 | 69.13 | 1010.27 | 67.35 | 426.29 |
| 22.33 | 45.87 | 1007.8 | 63.61 | 447.02 |
| 16.43 | 41.79 | 1005.47 | 76.89 | 455.85 |
| 11.5 | 40.22 | 1010.31 | 78.08 | 476.46 |
| 23.53 | 68.94 | 1007.53 | 69.17 | 437.48 |
| 21.86 | 49.21 | 1014.61 | 53.31 | 452.77 |
| 6.17 | 39.33 | 1012.57 | 93.32 | 491.54 |
| 30.19 | 64.79 | 1017.22 | 42.47 | 438.41 |
| 11.67 | 41.93 | 1019.81 | 82.58 | 476.1 |
| 15.34 | 36.99 | 1007.87 | 94.59 | 464.58 |
| 11.5 | 40.78 | 1023.91 | 86.31 | 467.74 |
| 25.53 | 57.17 | 1010.0 | 72.57 | 442.12 |
| 21.27 | 57.5 | 1014.53 | 80.76 | 453.34 |
| 28.37 | 69.13 | 1010.44 | 71.93 | 425.29 |
| 28.39 | 51.43 | 1011.74 | 47.54 | 449.63 |
| 13.78 | 45.78 | 1025.27 | 95.72 | 462.88 |
| 14.6 | 42.32 | 1015.71 | 77.03 | 464.67 |
| 5.1 | 35.57 | 1027.17 | 80.49 | 489.96 |
| 7.0 | 38.08 | 1020.27 | 77.67 | 482.38 |
| 26.3 | 77.95 | 1009.45 | 78.72 | 437.95 |
| 30.56 | 71.98 | 1004.74 | 58.77 | 429.2 |
| 21.09 | 46.63 | 1013.03 | 74.8 | 453.34 |
| 28.21 | 70.02 | 1010.58 | 51.34 | 442.47 |
| 15.84 | 49.69 | 1015.14 | 90.41 | 462.6 |
| 10.03 | 40.96 | 1024.57 | 91.1 | 478.79 |
| 20.37 | 52.05 | 1012.34 | 62.57 | 456.11 |
| 21.19 | 50.16 | 1005.81 | 84.27 | 450.33 |
| 33.73 | 69.88 | 1007.21 | 42.93 | 434.83 |
| 29.87 | 73.68 | 1015.1 | 40.96 | 433.43 |
| 19.62 | 62.96 | 1020.76 | 76.53 | 456.02 |
| 9.93 | 40.67 | 1018.08 | 69.74 | 485.23 |
| 9.43 | 37.14 | 1013.03 | 74.99 | 473.57 |
| 14.24 | 39.58 | 1011.17 | 70.45 | 469.94 |
| 12.97 | 49.83 | 1008.69 | 91.49 | 452.07 |
| 7.6 | 41.04 | 1021.82 | 88.97 | 475.32 |
| 8.39 | 36.24 | 1013.39 | 89.13 | 480.69 |
| 25.41 | 48.06 | 1013.12 | 46.52 | 444.01 |
| 18.43 | 56.03 | 1020.41 | 60.55 | 465.17 |
| 10.31 | 39.82 | 1012.87 | 88.71 | 480.61 |
| 11.29 | 41.5 | 1013.39 | 89.15 | 476.04 |
| 22.61 | 49.3 | 1003.51 | 83.02 | 441.76 |
| 29.34 | 71.98 | 1005.19 | 75.19 | 428.24 |
| 18.87 | 67.71 | 1004.0 | 87.35 | 444.77 |
| 13.21 | 45.87 | 1008.58 | 85.66 | 463.1 |
| 11.3 | 44.6 | 1018.19 | 91.66 | 470.5 |
| 29.23 | 72.99 | 1007.04 | 63.47 | 431.0 |
| 27.76 | 69.4 | 1004.27 | 72.25 | 430.68 |
| 29.26 | 67.17 | 1006.6 | 70.58 | 436.42 |
| 25.72 | 49.82 | 1016.19 | 60.1 | 452.33 |
| 23.43 | 63.94 | 1010.64 | 89.29 | 440.16 |
| 25.6 | 63.76 | 1010.18 | 67.43 | 435.75 |
| 22.3 | 44.57 | 1008.48 | 67.58 | 449.74 |
| 27.91 | 72.24 | 1010.74 | 70.8 | 430.73 |
| 30.35 | 77.17 | 1009.55 | 63.62 | 432.75 |
| 21.78 | 47.43 | 1007.88 | 66.68 | 446.79 |
| 7.19 | 41.39 | 1018.12 | 90.76 | 486.35 |
| 20.88 | 59.8 | 1015.66 | 75.34 | 453.18 |
| 24.19 | 50.23 | 1015.73 | 59.77 | 458.31 |
| 9.98 | 41.54 | 1019.7 | 80.79 | 480.26 |
| 23.47 | 51.3 | 1011.89 | 74.1 | 448.65 |
| 26.35 | 49.5 | 1012.67 | 41.34 | 458.41 |
| 29.89 | 64.69 | 1006.37 | 58.78 | 435.39 |
| 19.29 | 50.16 | 1010.49 | 97.78 | 450.21 |
| 17.48 | 43.14 | 1018.68 | 74.85 | 459.59 |
| 25.21 | 75.6 | 1017.19 | 69.84 | 445.84 |
| 23.3 | 48.78 | 1018.17 | 75.36 | 441.08 |
| 15.42 | 37.85 | 1009.89 | 85.8 | 467.33 |
| 21.44 | 63.09 | 1016.56 | 90.11 | 444.19 |
| 29.45 | 68.27 | 1007.96 | 61.63 | 432.96 |
| 29.69 | 47.93 | 1002.85 | 44.76 | 438.09 |
| 15.52 | 36.99 | 1006.86 | 89.7 | 467.9 |
| 11.47 | 43.67 | 1012.68 | 72.51 | 475.72 |
| 9.77 | 34.69 | 1027.72 | 74.98 | 477.51 |
| 22.6 | 69.84 | 1006.37 | 79.59 | 435.13 |
| 8.24 | 39.61 | 1017.99 | 78.42 | 477.9 |
| 17.01 | 44.2 | 1019.18 | 61.23 | 457.26 |
| 19.64 | 44.6 | 1015.88 | 47.56 | 467.53 |
| 10.61 | 41.58 | 1021.08 | 93.06 | 465.15 |
| 12.04 | 40.1 | 1014.42 | 89.65 | 474.28 |
| 29.19 | 65.71 | 1013.85 | 50.5 | 444.49 |
| 21.75 | 45.09 | 1014.15 | 44.84 | 452.84 |
| 23.66 | 77.54 | 1008.5 | 85.32 | 435.38 |
| 27.05 | 75.33 | 1003.88 | 82.94 | 433.57 |
| 29.63 | 69.71 | 1009.04 | 67.26 | 435.27 |
| 18.2 | 39.63 | 1005.35 | 79.05 | 468.49 |
| 32.22 | 70.8 | 1009.9 | 62.03 | 433.07 |
| 26.88 | 73.56 | 1004.85 | 94.36 | 430.63 |
| 29.05 | 65.74 | 1013.29 | 60.02 | 440.74 |
| 8.9 | 39.96 | 1026.31 | 95.46 | 474.49 |
| 18.93 | 48.6 | 1005.72 | 84.92 | 449.74 |
| 27.49 | 63.76 | 1010.09 | 62.8 | 436.73 |
| 23.1 | 70.79 | 1006.53 | 90.81 | 434.58 |
| 11.22 | 43.13 | 1017.24 | 80.9 | 473.93 |
| 31.97 | 79.74 | 1007.03 | 55.84 | 435.99 |
| 13.32 | 43.22 | 1009.45 | 75.3 | 466.83 |
| 31.68 | 68.24 | 1005.29 | 37.34 | 427.22 |
| 23.69 | 63.77 | 1013.39 | 79.5 | 444.07 |
| 13.83 | 41.49 | 1020.11 | 87.29 | 469.57 |
| 18.32 | 66.51 | 1015.18 | 81.5 | 459.89 |
| 11.05 | 40.71 | 1024.91 | 76.42 | 479.59 |
| 22.03 | 64.69 | 1007.21 | 75.75 | 440.92 |
| 10.23 | 41.46 | 1020.45 | 84.95 | 480.87 |
| 23.92 | 66.54 | 1009.93 | 62.37 | 441.9 |
| 29.38 | 69.68 | 1011.35 | 49.25 | 430.2 |
| 17.35 | 42.86 | 1014.62 | 74.16 | 465.16 |
| 9.81 | 44.45 | 1021.19 | 90.55 | 471.32 |
| 4.97 | 40.64 | 1020.91 | 94.28 | 485.43 |
| 5.15 | 40.07 | 1012.27 | 63.31 | 495.35 |
| 21.54 | 58.49 | 1010.85 | 78.9 | 449.12 |
| 7.94 | 42.02 | 1006.22 | 90.97 | 480.53 |
| 18.77 | 50.66 | 1014.89 | 87.34 | 457.07 |
| 21.69 | 69.94 | 1010.7 | 80.8 | 443.67 |
| 10.07 | 44.68 | 1023.44 | 90.95 | 477.52 |
| 13.83 | 39.64 | 1012.52 | 69.97 | 472.95 |
| 10.45 | 39.69 | 1003.92 | 89.45 | 472.54 |
| 11.56 | 40.71 | 1015.85 | 76.08 | 469.17 |
| 23.64 | 70.04 | 1011.09 | 83.35 | 435.21 |
| 10.48 | 40.22 | 1004.81 | 92.16 | 477.78 |
| 13.09 | 39.85 | 1012.86 | 58.42 | 475.89 |
| 10.67 | 40.23 | 1017.75 | 85.06 | 483.9 |
| 12.57 | 39.16 | 1016.53 | 88.91 | 476.2 |
| 14.45 | 43.34 | 1015.47 | 83.33 | 462.16 |
| 14.22 | 37.85 | 1011.24 | 88.49 | 471.05 |
| 6.97 | 41.26 | 1010.6 | 96.88 | 484.71 |
| 20.61 | 63.86 | 1015.43 | 73.86 | 446.34 |
| 14.67 | 42.28 | 1007.21 | 65.17 | 469.02 |
| 29.06 | 72.86 | 1004.23 | 69.41 | 432.12 |
| 14.38 | 40.1 | 1015.51 | 81.23 | 467.28 |
| 32.51 | 69.98 | 1013.29 | 54.07 | 429.66 |
| 11.79 | 45.09 | 1013.16 | 89.17 | 469.49 |
| 8.65 | 40.56 | 1023.23 | 78.85 | 485.87 |
| 9.75 | 40.81 | 1026.0 | 84.44 | 481.95 |
| 9.11 | 40.02 | 1031.1 | 83.02 | 479.03 |
| 23.39 | 69.13 | 1010.99 | 90.66 | 434.5 |
| 14.3 | 54.3 | 1015.16 | 75.29 | 464.9 |
| 17.49 | 63.94 | 1020.02 | 82.6 | 452.71 |
| 31.1 | 69.51 | 1010.84 | 45.4 | 429.74 |
| 19.77 | 56.65 | 1020.67 | 66.33 | 457.09 |
| 28.61 | 72.29 | 1011.61 | 45.33 | 446.77 |
| 13.52 | 41.48 | 1014.46 | 67.12 | 460.76 |
| 13.52 | 40.83 | 1008.31 | 84.14 | 471.95 |
| 17.57 | 46.21 | 1014.09 | 80.81 | 453.29 |
| 28.18 | 60.07 | 1016.34 | 49.13 | 441.61 |
| 14.29 | 46.18 | 1017.01 | 87.29 | 464.73 |
| 18.12 | 43.69 | 1016.91 | 52.95 | 464.68 |
| 31.27 | 73.91 | 1003.72 | 68.92 | 430.59 |
| 26.24 | 77.95 | 1014.19 | 85.21 | 438.01 |
| 7.44 | 41.04 | 1021.84 | 88.56 | 479.08 |
| 29.78 | 74.78 | 1009.28 | 55.09 | 436.39 |
| 23.37 | 65.46 | 1016.25 | 48.64 | 447.07 |
| 10.62 | 39.58 | 1011.9 | 87.85 | 479.91 |
| 5.84 | 43.02 | 1013.88 | 87.42 | 489.05 |
| 14.51 | 53.82 | 1016.46 | 62.75 | 463.17 |
| 11.31 | 42.02 | 1001.18 | 94.86 | 471.26 |
| 11.25 | 40.67 | 1011.64 | 63.54 | 480.49 |
| 9.18 | 39.42 | 1025.41 | 69.46 | 473.78 |
| 19.82 | 58.16 | 1016.76 | 74.66 | 455.5 |
| 24.77 | 58.41 | 1013.78 | 80.57 | 446.27 |
| 9.66 | 41.06 | 1021.21 | 84.7 | 482.2 |
| 21.96 | 59.8 | 1016.72 | 72.6 | 452.48 |
| 18.59 | 43.14 | 1011.92 | 52.63 | 464.48 |
| 24.75 | 69.89 | 1015.29 | 82.01 | 438.1 |
| 24.37 | 63.47 | 1012.77 | 75.22 | 445.6 |
| 29.6 | 67.79 | 1010.37 | 51.05 | 442.43 |
| 25.32 | 61.25 | 1011.56 | 80.1 | 436.67 |
| 16.15 | 41.85 | 1016.54 | 81.58 | 466.56 |
| 15.74 | 71.14 | 1019.65 | 65.94 | 457.29 |
| 5.97 | 36.25 | 1029.65 | 86.74 | 487.03 |
| 15.84 | 52.72 | 1026.45 | 62.57 | 464.93 |
| 14.84 | 44.63 | 1019.28 | 57.37 | 466.0 |
| 12.25 | 48.79 | 1017.44 | 88.91 | 469.52 |
| 27.38 | 70.04 | 1011.18 | 72.26 | 428.88 |
| 8.76 | 41.48 | 1018.49 | 74.98 | 474.3 |
| 15.54 | 39.31 | 1009.69 | 71.19 | 461.06 |
| 18.71 | 39.39 | 1014.09 | 62.82 | 465.57 |
| 13.06 | 41.78 | 1012.3 | 55.31 | 467.67 |
| 12.72 | 40.71 | 1016.02 | 71.57 | 466.99 |
| 19.83 | 39.39 | 1013.73 | 59.16 | 463.72 |
| 27.23 | 49.16 | 1004.03 | 40.8 | 443.78 |
| 24.27 | 68.28 | 1005.43 | 67.63 | 445.23 |
| 11.8 | 40.66 | 1017.13 | 97.2 | 464.43 |
| 6.76 | 36.25 | 1028.31 | 91.16 | 484.36 |
| 25.99 | 63.07 | 1012.5 | 64.81 | 442.16 |
| 16.3 | 39.63 | 1004.64 | 85.61 | 464.11 |
| 16.5 | 49.39 | 1018.35 | 93.42 | 462.48 |
| 10.59 | 42.49 | 1009.59 | 77.36 | 477.49 |
| 26.05 | 65.59 | 1012.78 | 67.03 | 437.04 |
| 19.5 | 40.79 | 1003.8 | 89.45 | 457.09 |
| 22.21 | 45.01 | 1012.22 | 54.84 | 450.6 |
| 17.86 | 45.0 | 1023.25 | 53.48 | 465.78 |
| 29.96 | 70.04 | 1010.15 | 54.47 | 427.1 |
| 19.08 | 44.63 | 1020.14 | 43.36 | 459.81 |
| 23.59 | 47.43 | 1006.64 | 48.92 | 447.36 |
| 3.38 | 39.64 | 1011.0 | 81.22 | 488.92 |
| 26.39 | 66.49 | 1012.96 | 60.35 | 433.36 |
| 8.99 | 39.04 | 1021.99 | 75.98 | 483.35 |
| 10.91 | 41.04 | 1026.57 | 74.24 | 469.53 |
| 13.08 | 39.82 | 1012.27 | 85.21 | 476.96 |
| 23.95 | 58.46 | 1017.5 | 68.46 | 440.75 |
| 15.64 | 43.71 | 1024.51 | 78.31 | 462.55 |
| 18.78 | 54.2 | 1012.05 | 89.25 | 448.04 |
| 20.65 | 50.59 | 1016.22 | 68.57 | 455.24 |
| 4.96 | 40.07 | 1011.8 | 67.38 | 494.75 |
| 23.51 | 57.32 | 1012.55 | 53.6 | 444.58 |
| 5.99 | 35.79 | 1011.56 | 91.69 | 484.82 |
| 23.65 | 66.05 | 1019.6 | 78.21 | 442.9 |
| 5.17 | 39.33 | 1009.68 | 94.19 | 485.46 |
| 26.38 | 49.5 | 1012.82 | 37.19 | 457.81 |
| 6.02 | 43.65 | 1013.85 | 83.53 | 481.92 |
| 23.2 | 61.02 | 1009.63 | 79.45 | 443.23 |
| 8.57 | 39.69 | 1000.91 | 99.9 | 474.29 |
| 30.72 | 71.58 | 1009.98 | 50.39 | 430.46 |
| 21.52 | 50.66 | 1013.56 | 74.33 | 455.71 |
| 22.93 | 62.26 | 1011.25 | 83.66 | 438.34 |
| 5.71 | 41.31 | 1003.24 | 89.48 | 485.83 |
| 18.62 | 44.06 | 1017.76 | 64.59 | 452.82 |
| 27.88 | 68.94 | 1007.68 | 75.68 | 435.04 |
| 22.32 | 59.8 | 1016.82 | 64.18 | 451.21 |
| 14.55 | 42.74 | 1028.41 | 70.09 | 465.81 |
| 17.83 | 44.92 | 1025.04 | 70.58 | 458.42 |
| 9.68 | 39.96 | 1026.09 | 99.28 | 470.22 |
| 19.41 | 49.39 | 1020.84 | 81.89 | 449.24 |
| 13.22 | 44.92 | 1023.84 | 87.99 | 471.43 |
| 12.24 | 44.92 | 1023.74 | 88.21 | 473.26 |
| 19.21 | 58.49 | 1011.7 | 91.29 | 452.82 |
| 29.74 | 70.32 | 1008.1 | 52.72 | 432.69 |
| 23.28 | 60.84 | 1017.91 | 67.5 | 444.13 |
| 8.02 | 41.92 | 1029.8 | 92.05 | 467.21 |
| 22.47 | 48.6 | 1002.33 | 63.23 | 445.98 |
| 27.51 | 73.77 | 1002.42 | 90.88 | 436.91 |
| 17.51 | 44.9 | 1009.05 | 74.91 | 455.01 |
| 23.22 | 66.56 | 1002.47 | 85.39 | 437.11 |
| 11.73 | 40.64 | 1020.68 | 96.98 | 477.06 |
| 21.19 | 67.71 | 1006.65 | 56.28 | 441.71 |
| 5.48 | 40.07 | 1019.63 | 65.62 | 495.76 |
| 24.26 | 66.44 | 1011.33 | 55.32 | 445.63 |
| 12.32 | 41.62 | 1012.88 | 88.88 | 464.72 |
| 31.26 | 68.94 | 1005.94 | 39.49 | 438.03 |
| 32.09 | 72.86 | 1003.47 | 54.59 | 434.78 |
| 24.98 | 60.32 | 1015.63 | 57.19 | 444.67 |
| 27.48 | 61.41 | 1012.2 | 45.06 | 452.24 |
| 21.04 | 45.09 | 1014.19 | 40.62 | 450.92 |
| 27.75 | 70.4 | 1006.65 | 90.21 | 436.53 |
| 22.79 | 71.77 | 1005.75 | 90.91 | 435.53 |
| 24.22 | 68.51 | 1013.23 | 74.96 | 440.01 |
| 27.06 | 64.45 | 1008.72 | 54.21 | 443.1 |
| 29.25 | 71.94 | 1007.18 | 63.62 | 427.49 |
| 26.86 | 68.08 | 1012.99 | 50.04 | 436.25 |
| 29.64 | 67.79 | 1009.99 | 51.23 | 440.74 |
| 19.92 | 63.31 | 1015.02 | 82.71 | 443.54 |
| 18.5 | 51.43 | 1010.82 | 92.04 | 459.42 |
| 23.71 | 60.23 | 1009.76 | 90.67 | 439.66 |
| 14.39 | 44.84 | 1023.55 | 91.14 | 464.15 |
| 19.3 | 56.65 | 1020.55 | 70.43 | 459.1 |
| 24.65 | 52.36 | 1014.76 | 66.63 | 455.68 |
| 13.5 | 45.51 | 1015.33 | 86.95 | 469.08 |
| 9.82 | 41.26 | 1007.71 | 96.69 | 478.02 |
| 18.4 | 44.06 | 1017.36 | 70.88 | 456.8 |
| 28.12 | 44.89 | 1009.18 | 47.14 | 441.13 |
| 17.15 | 43.69 | 1017.05 | 63.36 | 463.88 |
| 30.69 | 73.67 | 1006.14 | 60.58 | 430.45 |
| 28.82 | 65.71 | 1014.24 | 54.3 | 449.18 |
| 21.3 | 48.92 | 1010.92 | 65.09 | 447.89 |
| 30.58 | 70.04 | 1010.4 | 48.16 | 431.59 |
| 21.17 | 52.3 | 1009.36 | 81.51 | 447.5 |
| 9.87 | 41.82 | 1033.04 | 68.57 | 475.58 |
| 22.18 | 59.8 | 1016.77 | 73.16 | 453.24 |
| 24.39 | 63.21 | 1012.59 | 80.88 | 446.4 |
| 10.73 | 44.92 | 1025.1 | 85.4 | 476.81 |
| 9.38 | 40.46 | 1019.29 | 75.77 | 474.1 |
| 20.27 | 57.76 | 1016.66 | 75.76 | 450.71 |
| 24.82 | 66.48 | 1006.4 | 70.21 | 433.62 |
| 16.55 | 41.66 | 1011.45 | 55.53 | 465.14 |
| 20.73 | 59.87 | 1019.08 | 80.48 | 445.18 |
| 9.51 | 39.22 | 1015.3 | 72.41 | 474.12 |
| 8.63 | 43.79 | 1016.08 | 83.25 | 483.91 |
| 6.48 | 40.27 | 1010.55 | 82.12 | 486.68 |
| 14.95 | 43.52 | 1022.43 | 94.75 | 464.98 |
| 5.76 | 45.87 | 1010.83 | 95.79 | 481.4 |
| 10.94 | 39.04 | 1021.81 | 86.02 | 479.2 |
| 15.87 | 41.16 | 1005.85 | 78.29 | 463.86 |
| 12.42 | 38.25 | 1012.76 | 82.23 | 472.3 |
| 29.12 | 58.84 | 1001.31 | 52.86 | 446.51 |
| 29.12 | 51.43 | 1005.93 | 60.66 | 437.71 |
| 19.08 | 41.1 | 1001.96 | 62.77 | 458.94 |
| 31.06 | 67.17 | 1007.62 | 65.54 | 437.91 |
| 5.72 | 39.33 | 1009.96 | 95.4 | 490.76 |
| 26.52 | 65.06 | 1013.4 | 51.78 | 439.66 |
| 13.84 | 44.9 | 1007.58 | 63.62 | 463.27 |
| 13.03 | 39.52 | 1016.68 | 83.09 | 473.99 |
| 25.94 | 66.49 | 1012.83 | 61.81 | 433.38 |
| 16.64 | 53.82 | 1015.13 | 68.24 | 459.01 |
| 14.13 | 40.75 | 1016.05 | 72.41 | 471.44 |
| 13.65 | 39.28 | 1012.97 | 79.64 | 471.91 |
| 14.5 | 44.47 | 1028.2 | 66.95 | 465.15 |
| 19.8 | 51.19 | 1008.25 | 91.98 | 446.66 |
| 25.2 | 63.76 | 1009.78 | 64.96 | 438.15 |
| 20.66 | 51.19 | 1008.81 | 88.93 | 447.14 |
| 12.07 | 43.71 | 1025.53 | 85.62 | 472.32 |
| 25.64 | 70.72 | 1010.16 | 84.0 | 441.68 |
| 23.33 | 72.99 | 1009.33 | 89.41 | 440.04 |
| 29.41 | 64.05 | 1009.82 | 67.4 | 444.82 |
| 16.6 | 53.16 | 1014.5 | 76.75 | 457.26 |
| 27.53 | 72.58 | 1009.13 | 89.06 | 428.83 |
| 20.62 | 43.43 | 1009.93 | 64.02 | 449.07 |
| 26.02 | 71.94 | 1009.38 | 64.12 | 435.21 |
| 12.75 | 44.2 | 1017.59 | 81.22 | 471.03 |
| 12.87 | 48.04 | 1012.47 | 100.13 | 465.56 |
| 25.77 | 62.96 | 1019.86 | 58.07 | 442.83 |
| 14.84 | 41.48 | 1017.26 | 63.42 | 460.3 |
| 7.41 | 40.71 | 1023.07 | 83.32 | 474.25 |
| 8.87 | 41.82 | 1033.3 | 74.28 | 477.97 |
| 9.69 | 40.46 | 1019.1 | 71.91 | 472.16 |
| 16.17 | 46.97 | 1014.22 | 85.8 | 456.08 |
| 26.24 | 49.82 | 1014.9 | 55.58 | 452.41 |
| 13.78 | 43.22 | 1011.31 | 69.7 | 463.71 |
| 26.3 | 67.07 | 1006.26 | 63.79 | 433.72 |
| 17.37 | 57.76 | 1016.0 | 86.59 | 456.4 |
| 23.6 | 48.98 | 1015.41 | 48.28 | 448.43 |
| 8.3 | 36.08 | 1020.63 | 80.42 | 481.6 |
| 18.86 | 42.18 | 1001.16 | 98.58 | 457.07 |
| 22.12 | 49.39 | 1019.8 | 72.83 | 451.0 |
| 28.41 | 75.6 | 1018.48 | 56.07 | 440.28 |
| 29.42 | 71.32 | 1002.26 | 67.13 | 437.47 |
| 18.61 | 67.71 | 1004.07 | 84.49 | 443.57 |
| 27.57 | 69.84 | 1004.91 | 68.37 | 426.6 |
| 12.83 | 41.5 | 1013.12 | 86.07 | 470.87 |
| 9.64 | 39.85 | 1012.9 | 83.82 | 478.37 |
| 19.13 | 58.66 | 1013.32 | 74.86 | 453.92 |
| 15.92 | 40.56 | 1020.79 | 53.52 | 470.22 |
| 24.64 | 72.24 | 1011.37 | 80.61 | 434.54 |
| 27.62 | 63.9 | 1013.11 | 43.56 | 442.89 |
| 8.9 | 36.24 | 1013.29 | 89.35 | 479.03 |
| 9.55 | 43.99 | 1020.5 | 97.28 | 476.06 |
| 10.57 | 36.71 | 1022.62 | 80.49 | 473.88 |
| 19.8 | 57.25 | 1010.84 | 88.9 | 451.75 |
| 25.63 | 56.85 | 1012.68 | 49.7 | 439.2 |
| 24.7 | 58.46 | 1015.58 | 68.64 | 439.7 |
| 15.26 | 46.18 | 1013.68 | 98.58 | 463.6 |
| 20.06 | 52.84 | 1004.21 | 82.12 | 447.47 |
| 19.84 | 56.89 | 1013.23 | 78.32 | 447.92 |
| 11.49 | 44.63 | 1020.44 | 86.04 | 471.08 |
| 23.74 | 72.43 | 1007.99 | 91.36 | 437.55 |
| 22.62 | 51.3 | 1012.36 | 81.02 | 448.27 |
| 29.53 | 72.39 | 998.47 | 76.05 | 431.69 |
| 21.32 | 48.14 | 1016.57 | 71.81 | 449.09 |
| 20.3 | 58.46 | 1015.93 | 82.13 | 448.79 |
| 16.97 | 44.92 | 1025.21 | 74.27 | 460.21 |
| 12.07 | 41.17 | 1013.54 | 71.32 | 479.28 |
| 7.46 | 41.82 | 1032.67 | 74.59 | 483.11 |
| 19.2 | 54.2 | 1011.46 | 84.44 | 450.75 |
| 28.64 | 66.54 | 1010.43 | 43.39 | 437.97 |
| 13.56 | 41.48 | 1008.53 | 87.2 | 459.76 |
| 17.4 | 44.9 | 1020.5 | 77.11 | 457.75 |
| 14.08 | 40.1 | 1015.48 | 82.81 | 469.33 |
| 27.11 | 69.75 | 1009.74 | 85.67 | 433.28 |
| 20.92 | 70.02 | 1010.23 | 95.58 | 444.64 |
| 16.18 | 44.9 | 1021.3 | 74.46 | 463.1 |
| 15.57 | 44.68 | 1022.01 | 90.02 | 460.91 |
| 10.37 | 39.04 | 1023.95 | 81.93 | 479.35 |
| 19.6 | 59.21 | 1017.65 | 86.29 | 449.23 |
| 9.22 | 40.92 | 1021.83 | 85.43 | 474.51 |
| 27.76 | 72.99 | 1007.81 | 71.66 | 435.02 |
| 28.68 | 70.72 | 1009.43 | 71.33 | 435.45 |
| 20.95 | 48.14 | 1013.3 | 67.72 | 452.38 |
| 9.06 | 39.3 | 1019.73 | 84.23 | 480.41 |
| 9.21 | 39.72 | 1019.54 | 74.44 | 478.96 |
| 13.65 | 42.74 | 1026.58 | 71.48 | 468.87 |
| 31.79 | 76.2 | 1007.89 | 56.3 | 434.01 |
| 14.32 | 44.6 | 1013.85 | 68.13 | 466.36 |
| 26.28 | 75.23 | 1011.44 | 68.35 | 435.28 |
| 7.69 | 43.02 | 1014.51 | 85.23 | 486.46 |
| 14.44 | 40.1 | 1015.51 | 79.78 | 468.19 |
| 9.19 | 41.01 | 1022.14 | 98.98 | 468.37 |
| 13.35 | 41.39 | 1019.17 | 72.87 | 474.19 |
| 23.04 | 74.22 | 1009.52 | 90.93 | 440.32 |
| 4.83 | 38.44 | 1015.35 | 72.94 | 485.32 |
| 17.29 | 42.86 | 1014.38 | 72.3 | 464.27 |
| 8.73 | 36.18 | 1013.66 | 77.74 | 479.25 |
| 26.21 | 70.32 | 1007.0 | 78.29 | 430.4 |
| 23.72 | 58.62 | 1016.65 | 69.1 | 447.49 |
| 29.27 | 64.69 | 1006.85 | 55.79 | 438.23 |
| 10.4 | 40.43 | 1025.46 | 75.09 | 492.09 |
| 12.19 | 40.75 | 1015.13 | 88.98 | 475.36 |
| 20.4 | 54.9 | 1016.68 | 64.26 | 452.56 |
| 34.3 | 74.67 | 1015.98 | 25.89 | 427.84 |
| 27.56 | 68.08 | 1010.8 | 59.18 | 433.95 |
| 30.9 | 70.8 | 1008.48 | 67.48 | 435.27 |
| 14.85 | 58.59 | 1014.04 | 89.85 | 454.62 |
| 16.42 | 40.56 | 1020.36 | 50.62 | 472.17 |
| 16.45 | 63.31 | 1015.96 | 83.97 | 452.42 |
| 10.14 | 42.02 | 1003.19 | 96.51 | 472.17 |
| 9.53 | 41.44 | 1018.01 | 80.09 | 481.83 |
| 17.01 | 49.15 | 1021.83 | 84.02 | 458.78 |
| 23.94 | 62.08 | 1022.47 | 61.97 | 447.5 |
| 15.95 | 49.25 | 1019.04 | 88.51 | 463.4 |
| 11.15 | 41.26 | 1022.67 | 81.83 | 473.57 |
| 25.56 | 70.32 | 1009.07 | 90.63 | 433.72 |
| 27.16 | 66.44 | 1011.2 | 73.37 | 431.85 |
| 26.71 | 77.95 | 1012.13 | 77.5 | 433.47 |
| 29.56 | 74.22 | 1007.45 | 57.46 | 432.84 |
| 31.19 | 70.94 | 1007.29 | 51.91 | 436.6 |
| 6.86 | 41.17 | 1020.12 | 79.14 | 490.23 |
| 12.36 | 41.74 | 1020.58 | 69.24 | 477.16 |
| 32.82 | 68.31 | 1010.44 | 41.85 | 441.06 |
| 25.3 | 70.98 | 1007.22 | 95.1 | 440.86 |
| 8.71 | 41.82 | 1033.08 | 74.53 | 477.94 |
| 13.34 | 40.8 | 1026.56 | 64.85 | 474.47 |
| 14.2 | 43.02 | 1012.18 | 57.07 | 470.67 |
| 23.74 | 65.34 | 1013.7 | 62.9 | 447.31 |
| 16.9 | 44.88 | 1018.14 | 72.21 | 466.8 |
| 28.54 | 71.94 | 1007.4 | 65.99 | 430.91 |
| 30.15 | 69.88 | 1007.2 | 73.67 | 434.75 |
| 14.33 | 42.86 | 1010.82 | 88.59 | 469.52 |
| 25.57 | 59.43 | 1008.88 | 61.19 | 438.9 |
| 30.55 | 70.04 | 1010.51 | 49.37 | 429.56 |
| 28.04 | 74.33 | 1013.53 | 48.65 | 432.92 |
| 26.39 | 49.16 | 1005.68 | 56.18 | 442.87 |
| 15.3 | 41.76 | 1022.57 | 71.56 | 466.59 |
| 6.03 | 41.14 | 1028.04 | 87.46 | 479.61 |
| 13.49 | 44.63 | 1019.12 | 70.02 | 471.08 |
| 27.67 | 59.14 | 1016.51 | 61.2 | 433.37 |
| 24.19 | 65.48 | 1018.8 | 60.54 | 443.92 |
| 24.44 | 59.14 | 1016.74 | 71.82 | 443.5 |
| 29.86 | 64.79 | 1017.37 | 44.8 | 439.89 |
| 30.2 | 69.59 | 1008.9 | 67.32 | 434.66 |
| 7.99 | 41.38 | 1021.95 | 78.77 | 487.57 |
| 9.93 | 41.62 | 1013.76 | 96.02 | 464.64 |
| 11.03 | 42.32 | 1017.26 | 90.56 | 470.92 |
| 22.34 | 63.73 | 1014.37 | 83.19 | 444.39 |
| 25.33 | 48.6 | 1002.54 | 68.45 | 442.48 |
| 18.87 | 52.08 | 1005.25 | 99.19 | 449.61 |
| 25.97 | 69.34 | 1009.43 | 88.11 | 435.02 |
| 16.58 | 43.99 | 1021.81 | 79.29 | 458.67 |
| 14.35 | 46.18 | 1016.63 | 87.76 | 461.74 |
| 25.06 | 62.39 | 1008.09 | 82.56 | 438.31 |
| 13.85 | 48.92 | 1011.68 | 79.24 | 462.38 |
| 16.09 | 44.2 | 1019.39 | 67.24 | 460.56 |
| 26.34 | 59.21 | 1013.37 | 58.98 | 439.22 |
| 23.01 | 58.79 | 1009.71 | 84.22 | 444.64 |
| 26.39 | 71.25 | 999.8 | 89.12 | 430.34 |
| 31.32 | 71.29 | 1008.37 | 50.07 | 430.46 |
| 16.64 | 45.87 | 1009.02 | 98.86 | 456.79 |
| 13.42 | 41.23 | 994.17 | 95.79 | 468.82 |
| 20.06 | 44.9 | 1008.79 | 70.06 | 448.51 |
| 14.8 | 44.71 | 1014.67 | 41.71 | 470.77 |
| 12.59 | 41.14 | 1025.79 | 86.55 | 465.74 |
| 26.7 | 66.56 | 1005.31 | 71.97 | 430.21 |
| 19.78 | 50.32 | 1008.62 | 96.4 | 449.23 |
| 15.17 | 49.15 | 1021.91 | 91.73 | 461.89 |
| 21.71 | 61.45 | 1010.97 | 91.62 | 445.72 |
| 19.09 | 39.39 | 1013.36 | 59.14 | 466.13 |
| 19.76 | 51.19 | 1008.38 | 92.56 | 448.71 |
| 14.68 | 41.23 | 998.43 | 83.71 | 469.25 |
| 21.3 | 66.86 | 1013.04 | 55.43 | 450.56 |
| 16.73 | 39.64 | 1008.94 | 74.91 | 464.46 |
| 12.26 | 41.5 | 1014.87 | 89.41 | 471.13 |
| 14.77 | 48.06 | 1010.92 | 69.81 | 461.52 |
| 18.26 | 59.15 | 1012.04 | 86.01 | 451.09 |
| 27.1 | 79.74 | 1005.43 | 86.05 | 431.51 |
| 14.72 | 40.83 | 1009.65 | 80.98 | 469.8 |
| 26.3 | 51.43 | 1012.05 | 63.62 | 442.28 |
| 16.48 | 48.92 | 1011.84 | 64.16 | 458.67 |
| 17.99 | 43.79 | 1016.13 | 75.63 | 462.4 |
| 20.34 | 59.8 | 1015.18 | 80.21 | 453.54 |
| 25.53 | 62.96 | 1019.81 | 59.7 | 444.38 |
| 31.59 | 58.9 | 1003.39 | 47.6 | 440.52 |
| 30.8 | 69.14 | 1007.68 | 63.78 | 433.62 |
| 10.75 | 45.0 | 1023.68 | 89.37 | 481.96 |
| 19.3 | 44.9 | 1008.89 | 70.55 | 452.75 |
| 4.71 | 39.42 | 1026.4 | 84.42 | 481.28 |
| 23.1 | 66.05 | 1020.28 | 80.62 | 439.03 |
| 32.63 | 73.88 | 1005.64 | 52.56 | 435.75 |
| 26.63 | 74.16 | 1009.72 | 83.26 | 436.03 |
| 24.35 | 58.49 | 1011.03 | 70.64 | 445.6 |
| 15.11 | 56.03 | 1020.27 | 89.95 | 462.65 |
| 29.1 | 50.05 | 1005.87 | 51.53 | 438.66 |
| 21.24 | 50.32 | 1008.54 | 84.83 | 447.32 |
| 6.16 | 39.48 | 1004.85 | 59.68 | 484.55 |
| 7.36 | 41.01 | 1024.9 | 97.88 | 476.8 |
| 10.44 | 39.04 | 1023.99 | 85.03 | 480.34 |
| 26.76 | 48.41 | 1010.53 | 47.38 | 440.63 |
| 16.79 | 44.6 | 1014.27 | 48.08 | 459.48 |
| 10.76 | 40.43 | 1025.98 | 79.65 | 490.78 |
| 6.07 | 38.91 | 1019.25 | 83.39 | 483.56 |
| 27.33 | 73.18 | 1012.26 | 82.18 | 429.38 |
| 27.15 | 59.21 | 1013.49 | 51.71 | 440.27 |
| 22.35 | 51.43 | 1011.34 | 77.33 | 445.34 |
| 21.82 | 65.27 | 1013.86 | 72.81 | 447.43 |
| 21.11 | 69.94 | 1004.37 | 84.26 | 439.91 |
| 19.95 | 50.59 | 1016.11 | 73.23 | 459.27 |
| 7.45 | 39.61 | 1017.88 | 79.73 | 478.89 |
| 15.36 | 41.66 | 1012.41 | 62.32 | 466.7 |
| 15.65 | 43.5 | 1021.39 | 78.58 | 463.5 |
| 25.31 | 74.33 | 1015.04 | 79.88 | 436.21 |
| 25.88 | 63.47 | 1011.95 | 65.87 | 443.94 |
| 24.6 | 63.94 | 1012.87 | 80.28 | 439.63 |
| 22.58 | 41.54 | 1013.21 | 71.33 | 460.95 |
| 19.69 | 59.14 | 1015.99 | 70.33 | 448.69 |
| 25.85 | 75.08 | 1006.24 | 57.73 | 444.63 |
| 10.06 | 37.83 | 1005.49 | 99.46 | 473.51 |
| 18.59 | 39.54 | 1008.56 | 68.61 | 462.56 |
| 18.27 | 50.16 | 1011.07 | 95.91 | 451.76 |
| 8.85 | 40.43 | 1025.68 | 80.42 | 491.81 |
| 30.04 | 68.08 | 1011.04 | 51.01 | 429.52 |
| 26.06 | 49.02 | 1007.59 | 74.08 | 437.9 |
| 14.8 | 38.73 | 1003.18 | 80.73 | 467.54 |
| 23.93 | 64.45 | 1015.35 | 54.71 | 449.97 |
| 23.72 | 66.48 | 1003.61 | 73.75 | 436.62 |
| 11.44 | 40.55 | 1023.37 | 88.43 | 477.68 |
| 20.28 | 63.86 | 1016.04 | 74.66 | 447.26 |
| 27.9 | 63.13 | 1011.8 | 70.04 | 439.76 |
| 24.74 | 59.39 | 1015.23 | 74.64 | 437.49 |
| 14.8 | 58.2 | 1018.29 | 85.11 | 455.14 |
| 8.22 | 41.03 | 1021.76 | 82.97 | 485.5 |
| 27.56 | 66.93 | 1016.81 | 55.59 | 444.1 |
| 32.07 | 70.94 | 1006.91 | 49.9 | 432.33 |
| 9.53 | 44.03 | 1008.87 | 89.99 | 471.23 |
| 13.61 | 42.34 | 1017.93 | 91.61 | 463.89 |
| 22.2 | 51.19 | 1009.2 | 82.95 | 445.54 |
| 21.36 | 59.54 | 1007.99 | 92.62 | 446.09 |
| 23.25 | 63.86 | 1017.82 | 59.64 | 445.12 |
| 23.5 | 59.21 | 1018.29 | 63.0 | 443.31 |
| 8.46 | 39.66 | 1015.14 | 85.38 | 484.16 |
| 8.19 | 40.69 | 1019.86 | 85.23 | 477.76 |
| 30.67 | 71.29 | 1008.36 | 52.08 | 430.28 |
| 32.48 | 62.04 | 1010.39 | 38.05 | 446.48 |
| 8.99 | 36.66 | 1028.11 | 71.98 | 481.03 |
| 13.77 | 47.83 | 1007.41 | 90.66 | 466.07 |
| 19.05 | 67.32 | 1013.2 | 83.14 | 447.47 |
| 21.19 | 55.5 | 1019.83 | 65.22 | 455.93 |
| 10.12 | 40.0 | 1021.15 | 91.67 | 479.62 |
| 24.93 | 47.01 | 1014.28 | 66.04 | 455.06 |
| 8.47 | 40.46 | 1019.87 | 78.19 | 475.06 |
| 24.52 | 56.85 | 1012.59 | 54.47 | 438.89 |
| 28.55 | 69.84 | 1003.38 | 67.26 | 432.7 |
| 20.58 | 50.9 | 1011.89 | 72.56 | 452.6 |
| 18.31 | 46.21 | 1010.46 | 82.15 | 451.75 |
| 27.18 | 71.06 | 1008.16 | 86.32 | 430.66 |
| 4.43 | 38.91 | 1019.04 | 88.17 | 491.9 |
| 26.02 | 74.78 | 1010.04 | 72.78 | 439.82 |
| 15.75 | 39.0 | 1015.91 | 69.58 | 460.73 |
| 22.99 | 60.95 | 1015.14 | 69.86 | 449.7 |
| 25.52 | 59.15 | 1013.88 | 65.37 | 439.42 |
| 27.04 | 65.06 | 1013.33 | 52.37 | 439.84 |
| 6.42 | 35.57 | 1025.58 | 79.63 | 485.86 |
| 17.04 | 40.12 | 1011.81 | 83.14 | 458.1 |
| 10.79 | 39.82 | 1012.89 | 88.25 | 479.92 |
| 20.41 | 56.03 | 1019.94 | 55.85 | 458.29 |
| 7.36 | 40.07 | 1017.29 | 52.55 | 489.45 |
| 28.08 | 73.42 | 1012.17 | 62.74 | 434.0 |
| 24.74 | 69.13 | 1010.69 | 90.08 | 431.24 |
| 28.32 | 47.93 | 1003.26 | 54.5 | 439.5 |
| 16.71 | 40.56 | 1019.48 | 49.88 | 467.46 |
| 30.7 | 71.58 | 1010.0 | 48.96 | 429.27 |
| 18.42 | 58.95 | 1016.95 | 86.77 | 452.1 |
| 10.62 | 42.02 | 999.83 | 96.66 | 472.41 |
| 22.18 | 69.05 | 1002.75 | 70.84 | 442.14 |
| 22.38 | 49.3 | 1003.56 | 83.83 | 441.0 |
| 13.94 | 41.58 | 1020.76 | 68.22 | 463.07 |
| 21.24 | 60.84 | 1017.99 | 82.22 | 445.71 |
| 6.76 | 39.81 | 1017.11 | 87.9 | 483.16 |
| 26.73 | 68.84 | 1010.75 | 66.83 | 440.45 |
| 7.24 | 38.06 | 1020.6 | 85.36 | 481.83 |
| 10.84 | 40.62 | 1015.53 | 60.9 | 467.6 |
| 19.32 | 52.84 | 1004.29 | 83.51 | 450.88 |
| 29.0 | 69.13 | 1001.22 | 52.96 | 425.5 |
| 23.38 | 54.42 | 1013.95 | 73.02 | 451.87 |
| 31.17 | 69.51 | 1010.51 | 43.11 | 428.94 |
| 26.17 | 48.6 | 1002.59 | 61.41 | 439.86 |
| 30.9 | 73.42 | 1011.21 | 65.32 | 433.44 |
| 24.92 | 73.68 | 1015.12 | 93.68 | 438.23 |
| 32.77 | 71.32 | 1007.68 | 42.39 | 436.95 |
| 14.37 | 40.56 | 1021.67 | 68.18 | 470.19 |
| 8.36 | 40.22 | 1011.6 | 89.18 | 484.66 |
| 31.45 | 68.27 | 1007.56 | 64.79 | 430.81 |
| 31.6 | 73.17 | 1010.05 | 43.48 | 433.37 |
| 17.9 | 48.98 | 1014.17 | 80.4 | 453.02 |
| 20.35 | 50.9 | 1012.6 | 72.43 | 453.5 |
| 16.21 | 41.23 | 995.88 | 80.0 | 463.09 |
| 19.36 | 44.6 | 1016.25 | 45.65 | 464.56 |
| 21.04 | 65.46 | 1017.22 | 63.02 | 452.12 |
| 14.05 | 40.69 | 1015.66 | 74.39 | 470.9 |
| 23.48 | 64.15 | 1021.08 | 57.77 | 450.89 |
| 21.91 | 63.76 | 1009.85 | 76.8 | 445.04 |
| 24.42 | 63.07 | 1011.49 | 67.39 | 444.72 |
| 14.26 | 40.92 | 1022.07 | 73.96 | 460.38 |
| 21.38 | 58.33 | 1013.05 | 72.75 | 446.8 |
| 15.71 | 44.06 | 1018.34 | 71.69 | 465.05 |
| 5.78 | 40.62 | 1016.55 | 84.98 | 484.13 |
| 6.77 | 39.81 | 1017.01 | 87.68 | 488.27 |
| 23.84 | 49.21 | 1013.85 | 50.36 | 447.09 |
| 21.17 | 58.16 | 1017.16 | 68.11 | 452.02 |
| 19.94 | 58.96 | 1014.16 | 66.27 | 455.55 |
| 8.73 | 41.92 | 1029.41 | 89.72 | 480.99 |
| 16.39 | 41.67 | 1012.96 | 61.07 | 467.68 |
res12: Long = 9568
+--------+--------------------+-----------+
|database| tableName|isTemporary|
+--------+--------------------+-----------+
| default|power_plant_predi...| false|
| default| sentimentlex_csv| false|
| default| simple_range| false|
| default| social_media_usage| false|
| default|social_media_usag...| false|
+--------+--------------------+-----------+
+------------------------------------------------------------+--------+-----------+---------+-----------+
|name |database|description|tableType|isTemporary|
+------------------------------------------------------------+--------+-----------+---------+-----------+
|power_plant_predictions |default |null |MANAGED |false |
|sentimentlex_csv |default |null |EXTERNAL |false |
|simple_range |default |null |MANAGED |false |
|social_media_usage |default |null |MANAGED |false |
|social_media_usage_table_partitionedbyplatformbucketedbydate|default |null |MANAGED |false |
+------------------------------------------------------------+--------+-----------+---------+-----------+
+-------+---------------------+-------------------------+
|name |description |locationUri |
+-------+---------------------+-------------------------+
|default|Default Hive database|dbfs:/user/hive/warehouse|
+-------+---------------------+-------------------------+
Step 6: Data Modeling
Now let's model our data to predict what the power output will be given a set of sensor readings
Our first model will be based on simple linear regression since we saw some linear patterns in our data based on the scatter plots during the exploration stage.
+--------+--------------------+-----------+
|database| tableName|isTemporary|
+--------+--------------------+-----------+
| default|power_plant_predi...| false|
| default| sentimentlex_csv| false|
| default| simple_range| false|
| default| social_media_usage| false|
| default|social_media_usag...| false|
| | power_plant_table| true|
+--------+--------------------+-----------+
| AT | V | AP | RH | PE |
|---|---|---|---|---|
| 14.96 | 41.76 | 1024.07 | 73.17 | 463.26 |
| 25.18 | 62.96 | 1020.04 | 59.08 | 444.37 |
| 5.11 | 39.4 | 1012.16 | 92.14 | 488.56 |
| 20.86 | 57.32 | 1010.24 | 76.64 | 446.48 |
| 10.82 | 37.5 | 1009.23 | 96.62 | 473.9 |
| 26.27 | 59.44 | 1012.23 | 58.77 | 443.67 |
| 15.89 | 43.96 | 1014.02 | 75.24 | 467.35 |
| 9.48 | 44.71 | 1019.12 | 66.43 | 478.42 |
| 14.64 | 45.0 | 1021.78 | 41.25 | 475.98 |
| 11.74 | 43.56 | 1015.14 | 70.72 | 477.5 |
| 17.99 | 43.72 | 1008.64 | 75.04 | 453.02 |
| 20.14 | 46.93 | 1014.66 | 64.22 | 453.99 |
| 24.34 | 73.5 | 1011.31 | 84.15 | 440.29 |
| 25.71 | 58.59 | 1012.77 | 61.83 | 451.28 |
| 26.19 | 69.34 | 1009.48 | 87.59 | 433.99 |
| 21.42 | 43.79 | 1015.76 | 43.08 | 462.19 |
| 18.21 | 45.0 | 1022.86 | 48.84 | 467.54 |
| 11.04 | 41.74 | 1022.6 | 77.51 | 477.2 |
| 14.45 | 52.75 | 1023.97 | 63.59 | 459.85 |
| 13.97 | 38.47 | 1015.15 | 55.28 | 464.3 |
| 17.76 | 42.42 | 1009.09 | 66.26 | 468.27 |
| 5.41 | 40.07 | 1019.16 | 64.77 | 495.24 |
| 7.76 | 42.28 | 1008.52 | 83.31 | 483.8 |
| 27.23 | 63.9 | 1014.3 | 47.19 | 443.61 |
| 27.36 | 48.6 | 1003.18 | 54.93 | 436.06 |
| 27.47 | 70.72 | 1009.97 | 74.62 | 443.25 |
| 14.6 | 39.31 | 1011.11 | 72.52 | 464.16 |
| 7.91 | 39.96 | 1023.57 | 88.44 | 475.52 |
| 5.81 | 35.79 | 1012.14 | 92.28 | 484.41 |
| 30.53 | 65.18 | 1012.69 | 41.85 | 437.89 |
| 23.87 | 63.94 | 1019.02 | 44.28 | 445.11 |
| 26.09 | 58.41 | 1013.64 | 64.58 | 438.86 |
| 29.27 | 66.85 | 1011.11 | 63.25 | 440.98 |
| 27.38 | 74.16 | 1010.08 | 78.61 | 436.65 |
| 24.81 | 63.94 | 1018.76 | 44.51 | 444.26 |
| 12.75 | 44.03 | 1007.29 | 89.46 | 465.86 |
| 24.66 | 63.73 | 1011.4 | 74.52 | 444.37 |
| 16.38 | 47.45 | 1010.08 | 88.86 | 450.69 |
| 13.91 | 39.35 | 1014.69 | 75.51 | 469.02 |
| 23.18 | 51.3 | 1012.04 | 78.64 | 448.86 |
| 22.47 | 47.45 | 1007.62 | 76.65 | 447.14 |
| 13.39 | 44.85 | 1017.24 | 80.44 | 469.18 |
| 9.28 | 41.54 | 1018.33 | 79.89 | 482.8 |
| 11.82 | 42.86 | 1014.12 | 88.28 | 476.7 |
| 10.27 | 40.64 | 1020.63 | 84.6 | 474.99 |
| 22.92 | 63.94 | 1019.28 | 42.69 | 444.22 |
| 16.0 | 37.87 | 1020.24 | 78.41 | 461.33 |
| 21.22 | 43.43 | 1010.96 | 61.07 | 448.06 |
| 13.46 | 44.71 | 1014.51 | 50.0 | 474.6 |
| 9.39 | 40.11 | 1029.14 | 77.29 | 473.05 |
| 31.07 | 73.5 | 1010.58 | 43.66 | 432.06 |
| 12.82 | 38.62 | 1018.71 | 83.8 | 467.41 |
| 32.57 | 78.92 | 1011.6 | 66.47 | 430.12 |
| 8.11 | 42.18 | 1014.82 | 93.09 | 473.62 |
| 13.92 | 39.39 | 1012.94 | 80.52 | 471.81 |
| 23.04 | 59.43 | 1010.23 | 68.99 | 442.99 |
| 27.31 | 64.44 | 1014.65 | 57.27 | 442.77 |
| 5.91 | 39.33 | 1010.18 | 95.53 | 491.49 |
| 25.26 | 61.08 | 1013.68 | 71.72 | 447.46 |
| 27.97 | 58.84 | 1002.25 | 57.88 | 446.11 |
| 26.08 | 52.3 | 1007.03 | 63.34 | 442.44 |
| 29.01 | 65.71 | 1013.61 | 48.07 | 446.22 |
| 12.18 | 40.1 | 1016.67 | 91.87 | 471.49 |
| 13.76 | 45.87 | 1008.89 | 87.27 | 463.5 |
| 25.5 | 58.79 | 1016.02 | 64.4 | 440.01 |
| 28.26 | 65.34 | 1014.56 | 43.4 | 441.03 |
| 21.39 | 62.96 | 1019.49 | 72.24 | 452.68 |
| 7.26 | 40.69 | 1020.43 | 90.22 | 474.91 |
| 10.54 | 34.03 | 1018.71 | 74.0 | 478.77 |
| 27.71 | 74.34 | 998.14 | 71.85 | 434.2 |
| 23.11 | 68.3 | 1017.83 | 86.62 | 437.91 |
| 7.51 | 41.01 | 1024.61 | 97.41 | 477.61 |
| 26.46 | 74.67 | 1016.65 | 84.44 | 431.65 |
| 29.34 | 74.34 | 998.58 | 81.55 | 430.57 |
| 10.32 | 42.28 | 1008.82 | 75.66 | 481.09 |
| 22.74 | 61.02 | 1009.56 | 79.41 | 445.56 |
| 13.48 | 39.85 | 1012.71 | 58.91 | 475.74 |
| 25.52 | 69.75 | 1010.36 | 90.06 | 435.12 |
| 21.58 | 67.25 | 1017.39 | 79.0 | 446.15 |
| 27.66 | 76.86 | 1001.31 | 69.47 | 436.64 |
| 26.96 | 69.45 | 1013.89 | 51.47 | 436.69 |
| 12.29 | 42.18 | 1016.53 | 83.13 | 468.75 |
| 15.86 | 43.02 | 1012.18 | 40.33 | 466.6 |
| 13.87 | 45.08 | 1024.42 | 81.69 | 465.48 |
| 24.09 | 73.68 | 1014.93 | 94.55 | 441.34 |
| 20.45 | 69.45 | 1012.53 | 91.81 | 441.83 |
| 15.07 | 39.3 | 1019.0 | 63.62 | 464.7 |
| 32.72 | 69.75 | 1009.6 | 49.35 | 437.99 |
| 18.23 | 58.96 | 1015.55 | 69.61 | 459.12 |
| 35.56 | 68.94 | 1006.56 | 38.75 | 429.69 |
| 18.36 | 51.43 | 1010.57 | 90.17 | 459.8 |
| 26.35 | 64.05 | 1009.81 | 81.24 | 433.63 |
| 25.92 | 60.95 | 1014.62 | 48.46 | 442.84 |
| 8.01 | 41.66 | 1014.49 | 76.72 | 485.13 |
| 19.63 | 52.72 | 1025.09 | 51.16 | 459.12 |
| 20.02 | 67.32 | 1012.05 | 76.34 | 445.31 |
| 10.08 | 40.72 | 1022.7 | 67.3 | 480.8 |
| 27.23 | 66.48 | 1005.23 | 52.38 | 432.55 |
| 23.37 | 63.77 | 1013.42 | 76.44 | 443.86 |
| 18.74 | 59.21 | 1018.3 | 91.55 | 449.77 |
| 14.81 | 43.69 | 1017.19 | 71.9 | 470.71 |
| 23.1 | 51.3 | 1011.93 | 80.05 | 452.17 |
| 10.72 | 41.38 | 1021.6 | 63.77 | 478.29 |
| 29.46 | 71.94 | 1006.96 | 62.26 | 428.54 |
| 8.1 | 40.64 | 1020.66 | 89.04 | 478.27 |
| 27.29 | 62.66 | 1007.63 | 58.02 | 439.58 |
| 17.1 | 49.69 | 1005.53 | 81.82 | 457.32 |
| 11.49 | 44.2 | 1018.79 | 91.14 | 475.51 |
| 23.69 | 65.59 | 1010.85 | 88.92 | 439.66 |
| 13.51 | 40.89 | 1011.03 | 84.83 | 471.99 |
| 9.64 | 39.35 | 1015.1 | 91.76 | 479.81 |
| 25.65 | 78.92 | 1010.83 | 86.56 | 434.78 |
| 21.59 | 61.87 | 1011.18 | 57.21 | 446.58 |
| 27.98 | 58.33 | 1013.92 | 54.25 | 437.76 |
| 18.8 | 39.72 | 1001.24 | 63.8 | 459.36 |
| 18.28 | 44.71 | 1016.99 | 33.71 | 462.28 |
| 13.55 | 43.48 | 1016.08 | 67.25 | 464.33 |
| 22.99 | 46.21 | 1010.71 | 60.11 | 444.36 |
| 23.94 | 59.39 | 1014.32 | 74.55 | 438.64 |
| 13.74 | 34.03 | 1018.69 | 67.34 | 470.49 |
| 21.3 | 41.1 | 1001.86 | 42.75 | 455.13 |
| 27.54 | 66.93 | 1017.06 | 55.2 | 450.22 |
| 24.81 | 63.73 | 1009.34 | 83.61 | 440.43 |
| 4.97 | 42.85 | 1014.02 | 88.78 | 482.98 |
| 15.22 | 50.88 | 1014.19 | 100.12 | 460.44 |
| 23.88 | 54.2 | 1012.81 | 64.52 | 444.97 |
| 33.01 | 68.67 | 1005.2 | 51.41 | 433.94 |
| 25.98 | 73.18 | 1012.28 | 85.78 | 439.73 |
| 28.18 | 73.88 | 1005.89 | 75.41 | 434.48 |
| 21.67 | 60.84 | 1017.93 | 81.63 | 442.33 |
| 17.67 | 45.09 | 1014.26 | 51.92 | 457.67 |
| 21.37 | 57.76 | 1018.8 | 70.12 | 454.66 |
| 28.69 | 67.25 | 1017.71 | 53.83 | 432.21 |
| 16.61 | 43.77 | 1012.25 | 77.23 | 457.66 |
| 27.91 | 63.76 | 1010.27 | 65.67 | 435.21 |
| 20.97 | 47.43 | 1007.64 | 71.18 | 448.22 |
| 10.8 | 41.66 | 1013.79 | 81.96 | 475.51 |
| 20.61 | 62.91 | 1013.24 | 79.54 | 446.53 |
| 25.45 | 57.32 | 1011.7 | 47.09 | 441.3 |
| 30.16 | 69.34 | 1007.67 | 57.69 | 433.54 |
| 4.99 | 39.04 | 1020.45 | 78.89 | 472.52 |
| 10.51 | 44.78 | 1012.59 | 85.29 | 474.77 |
| 33.79 | 69.05 | 1001.62 | 40.13 | 435.1 |
| 21.34 | 59.8 | 1016.92 | 77.06 | 450.74 |
| 23.4 | 65.06 | 1014.32 | 67.38 | 442.7 |
| 32.21 | 68.14 | 1003.34 | 62.44 | 426.56 |
| 14.26 | 42.32 | 1016.0 | 77.43 | 463.71 |
| 27.71 | 66.93 | 1016.85 | 58.77 | 447.06 |
| 21.95 | 57.76 | 1018.02 | 67.72 | 452.27 |
| 25.76 | 63.94 | 1018.49 | 42.14 | 445.78 |
| 23.68 | 68.3 | 1017.93 | 84.16 | 438.65 |
| 8.28 | 40.77 | 1011.55 | 89.79 | 480.15 |
| 23.44 | 62.52 | 1016.46 | 67.21 | 447.19 |
| 25.32 | 48.41 | 1008.47 | 72.14 | 443.04 |
| 3.94 | 39.9 | 1008.06 | 97.49 | 488.81 |
| 17.3 | 57.76 | 1016.26 | 87.74 | 455.75 |
| 18.2 | 49.39 | 1018.83 | 96.3 | 455.86 |
| 21.43 | 46.97 | 1013.94 | 61.25 | 457.68 |
| 11.16 | 40.05 | 1014.95 | 88.38 | 479.11 |
| 30.38 | 74.16 | 1007.44 | 74.77 | 432.84 |
| 23.36 | 62.52 | 1016.18 | 68.18 | 448.37 |
| 21.69 | 47.45 | 1007.56 | 77.2 | 447.06 |
| 23.62 | 49.21 | 1014.1 | 49.54 | 443.53 |
| 21.87 | 61.45 | 1011.13 | 92.22 | 445.21 |
| 29.25 | 66.51 | 1015.53 | 33.65 | 441.7 |
| 20.03 | 66.86 | 1013.05 | 64.59 | 450.93 |
| 18.14 | 49.78 | 1002.95 | 100.09 | 451.44 |
| 24.23 | 56.89 | 1012.32 | 68.04 | 441.29 |
| 18.11 | 44.85 | 1014.48 | 48.94 | 458.85 |
| 6.57 | 43.65 | 1018.24 | 74.47 | 481.46 |
| 12.56 | 43.41 | 1016.93 | 81.02 | 467.19 |
| 13.4 | 41.58 | 1020.5 | 71.17 | 461.54 |
| 27.1 | 52.84 | 1006.28 | 53.85 | 439.08 |
| 14.28 | 42.74 | 1028.79 | 70.67 | 467.22 |
| 16.29 | 44.34 | 1019.49 | 59.36 | 468.8 |
| 31.24 | 71.98 | 1004.66 | 57.17 | 426.93 |
| 10.57 | 37.73 | 1024.36 | 70.29 | 474.65 |
| 13.8 | 44.21 | 1022.93 | 83.37 | 468.97 |
| 25.3 | 71.58 | 1010.18 | 87.36 | 433.97 |
| 18.06 | 50.16 | 1009.52 | 100.09 | 450.53 |
| 25.42 | 59.04 | 1011.98 | 68.78 | 444.51 |
| 15.07 | 40.69 | 1015.29 | 70.98 | 469.03 |
| 11.75 | 71.14 | 1019.36 | 75.68 | 466.56 |
| 20.23 | 52.05 | 1012.15 | 47.49 | 457.57 |
| 27.31 | 59.54 | 1006.24 | 71.99 | 440.13 |
| 28.57 | 69.84 | 1003.57 | 66.55 | 433.24 |
| 17.9 | 43.72 | 1008.64 | 74.73 | 452.55 |
| 23.83 | 71.37 | 1002.04 | 64.78 | 443.29 |
| 27.92 | 74.99 | 1005.47 | 75.13 | 431.76 |
| 17.34 | 44.78 | 1007.81 | 56.38 | 454.97 |
| 17.94 | 63.07 | 1012.42 | 94.35 | 456.7 |
| 6.4 | 39.9 | 1007.75 | 86.55 | 486.03 |
| 11.78 | 39.96 | 1011.37 | 82.95 | 472.79 |
| 20.28 | 57.25 | 1010.12 | 88.42 | 452.03 |
| 21.04 | 54.2 | 1012.26 | 85.61 | 443.41 |
| 25.11 | 67.32 | 1014.49 | 58.39 | 441.93 |
| 30.28 | 70.98 | 1007.51 | 74.28 | 432.64 |
| 8.14 | 36.24 | 1013.15 | 87.85 | 480.25 |
| 16.86 | 39.63 | 1004.47 | 83.5 | 466.68 |
| 6.25 | 40.07 | 1020.19 | 65.24 | 494.39 |
| 22.35 | 54.42 | 1012.46 | 75.01 | 454.72 |
| 17.98 | 56.85 | 1012.28 | 84.52 | 448.71 |
| 21.19 | 42.48 | 1013.43 | 80.52 | 469.76 |
| 20.94 | 44.89 | 1009.64 | 75.14 | 450.71 |
| 24.23 | 58.79 | 1009.8 | 75.75 | 444.01 |
| 19.18 | 58.2 | 1017.46 | 76.72 | 453.2 |
| 20.88 | 57.85 | 1012.39 | 85.47 | 450.87 |
| 23.67 | 63.86 | 1019.67 | 57.95 | 441.73 |
| 14.12 | 39.52 | 1018.41 | 78.32 | 465.09 |
| 25.23 | 64.63 | 1020.59 | 52.2 | 447.28 |
| 6.54 | 39.33 | 1011.54 | 93.69 | 491.16 |
| 20.08 | 62.52 | 1017.99 | 75.74 | 450.98 |
| 24.67 | 63.56 | 1013.75 | 67.56 | 446.3 |
| 27.82 | 79.74 | 1008.37 | 69.46 | 436.48 |
| 15.55 | 42.03 | 1017.41 | 74.58 | 460.84 |
| 24.26 | 69.51 | 1013.43 | 53.23 | 442.56 |
| 13.45 | 41.49 | 1020.19 | 88.72 | 467.3 |
| 11.06 | 40.64 | 1021.47 | 96.16 | 479.13 |
| 24.91 | 52.3 | 1008.72 | 68.26 | 441.15 |
| 22.39 | 59.04 | 1011.78 | 86.39 | 445.52 |
| 11.95 | 40.69 | 1015.62 | 85.34 | 475.4 |
| 14.85 | 40.69 | 1014.91 | 72.64 | 469.3 |
| 10.11 | 41.62 | 1017.17 | 97.82 | 463.57 |
| 23.67 | 68.67 | 1006.71 | 77.22 | 445.32 |
| 16.14 | 44.21 | 1020.36 | 80.59 | 461.03 |
| 15.11 | 43.13 | 1014.99 | 46.91 | 466.74 |
| 24.14 | 59.87 | 1018.47 | 57.76 | 444.04 |
| 30.08 | 67.25 | 1017.6 | 53.09 | 434.01 |
| 14.77 | 44.9 | 1020.5 | 84.31 | 465.23 |
| 27.6 | 69.34 | 1009.63 | 71.58 | 440.6 |
| 13.89 | 44.84 | 1023.66 | 92.97 | 466.74 |
| 26.85 | 75.6 | 1017.43 | 74.55 | 433.48 |
| 12.41 | 40.96 | 1023.36 | 78.96 | 473.59 |
| 13.08 | 41.74 | 1020.75 | 64.44 | 474.81 |
| 18.93 | 44.06 | 1017.58 | 68.23 | 454.75 |
| 20.5 | 49.69 | 1009.6 | 70.81 | 452.94 |
| 30.72 | 69.13 | 1009.94 | 61.66 | 435.83 |
| 7.55 | 39.22 | 1014.53 | 77.76 | 482.19 |
| 13.49 | 44.47 | 1030.46 | 69.49 | 466.66 |
| 15.62 | 40.12 | 1013.03 | 96.26 | 462.59 |
| 24.8 | 64.63 | 1020.69 | 55.74 | 447.82 |
| 10.03 | 41.62 | 1014.55 | 95.61 | 462.73 |
| 22.43 | 63.21 | 1012.06 | 84.75 | 447.98 |
| 14.95 | 39.31 | 1009.15 | 75.3 | 462.72 |
| 24.78 | 58.46 | 1016.82 | 67.5 | 442.42 |
| 23.2 | 48.41 | 1008.64 | 80.92 | 444.69 |
| 14.01 | 39.0 | 1016.73 | 79.23 | 466.7 |
| 19.4 | 64.63 | 1020.38 | 81.1 | 453.84 |
| 30.15 | 67.32 | 1013.83 | 32.8 | 436.92 |
| 6.91 | 36.08 | 1021.82 | 84.31 | 486.37 |
| 29.04 | 60.07 | 1015.42 | 46.15 | 440.43 |
| 26.02 | 63.07 | 1010.94 | 53.96 | 446.82 |
| 5.89 | 39.48 | 1005.11 | 59.83 | 484.91 |
| 26.52 | 71.64 | 1008.27 | 75.3 | 437.76 |
| 28.53 | 68.08 | 1013.27 | 42.53 | 438.91 |
| 16.59 | 39.54 | 1007.97 | 70.58 | 464.19 |
| 22.95 | 67.79 | 1009.89 | 91.69 | 442.19 |
| 23.96 | 47.43 | 1008.38 | 63.55 | 446.86 |
| 17.48 | 44.2 | 1018.89 | 61.51 | 457.15 |
| 6.69 | 43.65 | 1020.14 | 69.55 | 482.57 |
| 10.25 | 41.26 | 1007.44 | 98.08 | 476.03 |
| 28.87 | 72.58 | 1008.69 | 79.34 | 428.89 |
| 12.04 | 40.23 | 1018.07 | 81.28 | 472.7 |
| 22.58 | 52.3 | 1009.04 | 78.99 | 445.6 |
| 15.12 | 52.05 | 1014.63 | 80.38 | 464.78 |
| 25.48 | 58.95 | 1017.02 | 51.16 | 440.42 |
| 27.87 | 70.79 | 1003.96 | 72.17 | 428.41 |
| 23.72 | 70.47 | 1010.65 | 75.39 | 438.5 |
| 25.0 | 59.43 | 1007.84 | 68.91 | 438.28 |
| 8.42 | 40.64 | 1022.35 | 96.38 | 476.29 |
| 22.46 | 58.49 | 1011.5 | 70.54 | 448.46 |
| 29.92 | 57.19 | 1008.62 | 45.8 | 438.99 |
| 11.68 | 39.22 | 1017.9 | 57.95 | 471.8 |
| 14.04 | 42.44 | 1012.74 | 81.89 | 471.81 |
| 19.86 | 59.14 | 1016.12 | 69.32 | 449.82 |
| 25.99 | 68.08 | 1013.13 | 59.14 | 442.14 |
| 23.42 | 58.79 | 1009.74 | 81.54 | 441.46 |
| 10.6 | 40.22 | 1011.37 | 85.81 | 477.62 |
| 20.97 | 61.87 | 1011.45 | 65.41 | 446.76 |
| 14.14 | 39.82 | 1012.46 | 81.15 | 472.52 |
| 8.56 | 40.71 | 1021.27 | 95.87 | 471.58 |
| 24.86 | 72.39 | 1001.15 | 90.24 | 440.85 |
| 29.0 | 77.54 | 1011.33 | 75.13 | 431.37 |
| 27.59 | 71.97 | 1008.64 | 88.22 | 437.33 |
| 10.45 | 40.71 | 1015.68 | 81.48 | 469.22 |
| 8.51 | 40.78 | 1023.51 | 89.84 | 471.11 |
| 29.82 | 66.51 | 1010.98 | 43.57 | 439.17 |
| 22.56 | 62.26 | 1012.11 | 63.16 | 445.33 |
| 11.38 | 39.22 | 1018.62 | 57.14 | 473.71 |
| 20.25 | 57.76 | 1016.28 | 77.76 | 452.66 |
| 22.42 | 59.43 | 1007.12 | 90.56 | 440.99 |
| 14.85 | 38.91 | 1014.48 | 60.98 | 467.42 |
| 25.62 | 58.82 | 1010.02 | 70.31 | 444.14 |
| 19.85 | 56.53 | 1020.57 | 74.05 | 457.17 |
| 13.67 | 54.3 | 1015.92 | 75.42 | 467.87 |
| 24.39 | 70.72 | 1009.78 | 82.25 | 442.04 |
| 16.07 | 44.58 | 1019.52 | 67.95 | 471.36 |
| 11.6 | 39.1 | 1009.81 | 100.09 | 460.7 |
| 31.38 | 70.83 | 1010.35 | 47.28 | 431.33 |
| 29.91 | 76.86 | 998.59 | 72.41 | 432.6 |
| 19.67 | 59.39 | 1014.07 | 77.67 | 447.61 |
| 27.18 | 64.79 | 1016.27 | 63.7 | 443.87 |
| 21.39 | 52.3 | 1009.2 | 79.77 | 446.87 |
| 10.45 | 41.01 | 1020.57 | 93.84 | 465.74 |
| 19.46 | 56.89 | 1014.02 | 84.95 | 447.86 |
| 23.55 | 62.96 | 1020.16 | 70.16 | 447.65 |
| 23.35 | 63.47 | 1011.78 | 84.24 | 437.87 |
| 9.26 | 41.66 | 1016.87 | 73.32 | 483.51 |
| 10.3 | 41.46 | 1018.21 | 86.17 | 479.65 |
| 20.94 | 58.16 | 1016.88 | 65.43 | 455.16 |
| 23.13 | 71.25 | 1002.49 | 94.59 | 431.91 |
| 12.77 | 41.5 | 1014.13 | 86.8 | 470.68 |
| 28.29 | 69.13 | 1009.29 | 58.18 | 429.28 |
| 19.13 | 59.21 | 1018.32 | 89.66 | 450.81 |
| 24.44 | 73.5 | 1011.49 | 87.39 | 437.73 |
| 20.32 | 44.6 | 1015.16 | 36.35 | 460.21 |
| 20.54 | 69.05 | 1001.6 | 79.62 | 442.86 |
| 12.16 | 45.0 | 1021.51 | 50.52 | 482.99 |
| 28.09 | 65.27 | 1013.27 | 51.96 | 440.0 |
| 9.25 | 41.82 | 1033.25 | 74.73 | 478.48 |
| 21.75 | 49.82 | 1015.01 | 78.33 | 455.28 |
| 23.7 | 66.56 | 1002.07 | 85.19 | 436.94 |
| 16.22 | 37.87 | 1022.36 | 83.13 | 461.06 |
| 24.75 | 69.45 | 1013.97 | 53.49 | 438.28 |
| 10.48 | 39.58 | 1011.81 | 88.86 | 472.61 |
| 29.53 | 70.79 | 1003.7 | 60.89 | 426.85 |
| 12.59 | 39.72 | 1017.76 | 61.14 | 470.18 |
| 23.5 | 54.42 | 1012.31 | 68.29 | 455.38 |
| 29.01 | 66.56 | 1006.44 | 57.62 | 428.32 |
| 9.75 | 42.49 | 1010.57 | 83.63 | 480.35 |
| 19.55 | 56.53 | 1020.2 | 78.1 | 455.56 |
| 21.05 | 58.33 | 1013.14 | 66.34 | 447.66 |
| 24.72 | 68.67 | 1006.74 | 79.02 | 443.06 |
| 21.19 | 58.86 | 1014.19 | 68.96 | 452.43 |
| 10.77 | 41.54 | 1019.94 | 71.13 | 477.81 |
| 28.68 | 73.77 | 1004.72 | 87.01 | 431.66 |
| 29.87 | 73.91 | 1004.53 | 74.3 | 431.8 |
| 22.99 | 68.67 | 1006.65 | 77.62 | 446.67 |
| 24.66 | 60.29 | 1018.0 | 59.56 | 445.26 |
| 32.63 | 69.89 | 1013.85 | 41.66 | 425.72 |
| 31.38 | 72.29 | 1008.73 | 73.27 | 430.58 |
| 23.87 | 60.27 | 1018.94 | 77.16 | 439.86 |
| 25.6 | 59.15 | 1013.31 | 67.02 | 441.11 |
| 27.62 | 71.14 | 1011.6 | 52.8 | 434.72 |
| 30.1 | 67.45 | 1014.23 | 39.04 | 434.01 |
| 12.19 | 41.17 | 1019.43 | 65.47 | 475.64 |
| 13.11 | 41.58 | 1020.43 | 74.32 | 460.44 |
| 28.29 | 68.67 | 1005.46 | 69.22 | 436.4 |
| 13.45 | 40.73 | 1018.7 | 93.88 | 461.03 |
| 10.98 | 41.54 | 1019.94 | 69.83 | 479.08 |
| 26.48 | 69.14 | 1009.31 | 84.11 | 435.76 |
| 13.07 | 45.51 | 1015.22 | 78.65 | 460.14 |
| 25.56 | 75.6 | 1017.37 | 69.31 | 442.2 |
| 22.68 | 50.78 | 1008.83 | 70.3 | 447.69 |
| 28.86 | 73.67 | 1006.65 | 68.23 | 431.15 |
| 22.7 | 63.56 | 1014.32 | 71.76 | 445.0 |
| 27.89 | 73.21 | 1001.32 | 85.88 | 431.59 |
| 13.78 | 44.47 | 1027.94 | 71.09 | 467.22 |
| 28.14 | 51.43 | 1012.16 | 52.67 | 445.33 |
| 11.8 | 45.09 | 1013.21 | 89.68 | 470.57 |
| 10.71 | 39.61 | 1018.72 | 73.66 | 473.77 |
| 24.54 | 60.29 | 1017.42 | 58.94 | 447.67 |
| 11.54 | 40.05 | 1014.78 | 87.05 | 474.29 |
| 29.47 | 71.32 | 1008.07 | 67.0 | 437.14 |
| 29.24 | 69.05 | 1003.12 | 43.18 | 432.56 |
| 14.51 | 41.79 | 1009.72 | 80.62 | 459.14 |
| 22.91 | 60.07 | 1016.03 | 59.72 | 446.19 |
| 27.02 | 71.77 | 1006.38 | 72.1 | 428.1 |
| 13.49 | 44.47 | 1030.18 | 69.15 | 468.46 |
| 30.24 | 66.75 | 1017.95 | 55.66 | 435.02 |
| 23.19 | 48.6 | 1002.38 | 61.19 | 445.52 |
| 17.73 | 40.55 | 1003.36 | 74.62 | 462.69 |
| 18.62 | 61.27 | 1019.26 | 73.35 | 455.75 |
| 12.85 | 40.0 | 1015.89 | 68.85 | 463.74 |
| 32.33 | 69.68 | 1011.95 | 39.89 | 439.79 |
| 25.09 | 58.95 | 1016.99 | 53.16 | 443.26 |
| 29.45 | 69.13 | 1009.3 | 52.97 | 432.04 |
| 16.91 | 43.96 | 1013.32 | 79.87 | 465.86 |
| 14.09 | 45.87 | 1009.05 | 84.09 | 465.6 |
| 10.73 | 25.36 | 1009.35 | 100.15 | 469.43 |
| 23.2 | 49.3 | 1003.4 | 79.77 | 440.75 |
| 8.21 | 38.91 | 1015.82 | 88.99 | 481.32 |
| 9.3 | 40.56 | 1022.64 | 76.14 | 479.87 |
| 16.97 | 39.16 | 1005.7 | 69.13 | 458.59 |
| 23.69 | 71.97 | 1009.62 | 93.03 | 438.62 |
| 25.13 | 59.44 | 1012.38 | 77.92 | 445.59 |
| 9.86 | 43.56 | 1015.13 | 74.89 | 481.87 |
| 11.33 | 41.5 | 1013.58 | 88.7 | 475.01 |
| 26.95 | 48.41 | 1008.53 | 62.94 | 436.54 |
| 15.0 | 40.66 | 1016.28 | 89.62 | 456.63 |
| 20.76 | 62.52 | 1015.63 | 81.04 | 451.69 |
| 14.29 | 39.59 | 1010.93 | 94.53 | 463.04 |
| 19.74 | 67.71 | 1007.68 | 64.02 | 446.1 |
| 26.68 | 59.92 | 1009.94 | 70.57 | 438.67 |
| 14.24 | 41.4 | 1019.7 | 70.32 | 466.88 |
| 21.98 | 48.41 | 1008.42 | 84.86 | 444.6 |
| 22.75 | 59.39 | 1015.4 | 81.41 | 440.26 |
| 8.34 | 40.96 | 1023.28 | 89.45 | 483.92 |
| 11.8 | 41.2 | 1017.18 | 82.71 | 475.19 |
| 8.81 | 44.68 | 1023.06 | 93.93 | 479.24 |
| 30.05 | 73.68 | 1014.95 | 70.6 | 434.92 |
| 16.01 | 65.46 | 1014.0 | 87.68 | 454.16 |
| 21.75 | 58.79 | 1012.42 | 87.58 | 447.58 |
| 13.94 | 41.26 | 1021.67 | 74.4 | 467.9 |
| 29.25 | 69.13 | 1010.27 | 67.35 | 426.29 |
| 22.33 | 45.87 | 1007.8 | 63.61 | 447.02 |
| 16.43 | 41.79 | 1005.47 | 76.89 | 455.85 |
| 11.5 | 40.22 | 1010.31 | 78.08 | 476.46 |
| 23.53 | 68.94 | 1007.53 | 69.17 | 437.48 |
| 21.86 | 49.21 | 1014.61 | 53.31 | 452.77 |
| 6.17 | 39.33 | 1012.57 | 93.32 | 491.54 |
| 30.19 | 64.79 | 1017.22 | 42.47 | 438.41 |
| 11.67 | 41.93 | 1019.81 | 82.58 | 476.1 |
| 15.34 | 36.99 | 1007.87 | 94.59 | 464.58 |
| 11.5 | 40.78 | 1023.91 | 86.31 | 467.74 |
| 25.53 | 57.17 | 1010.0 | 72.57 | 442.12 |
| 21.27 | 57.5 | 1014.53 | 80.76 | 453.34 |
| 28.37 | 69.13 | 1010.44 | 71.93 | 425.29 |
| 28.39 | 51.43 | 1011.74 | 47.54 | 449.63 |
| 13.78 | 45.78 | 1025.27 | 95.72 | 462.88 |
| 14.6 | 42.32 | 1015.71 | 77.03 | 464.67 |
| 5.1 | 35.57 | 1027.17 | 80.49 | 489.96 |
| 7.0 | 38.08 | 1020.27 | 77.67 | 482.38 |
| 26.3 | 77.95 | 1009.45 | 78.72 | 437.95 |
| 30.56 | 71.98 | 1004.74 | 58.77 | 429.2 |
| 21.09 | 46.63 | 1013.03 | 74.8 | 453.34 |
| 28.21 | 70.02 | 1010.58 | 51.34 | 442.47 |
| 15.84 | 49.69 | 1015.14 | 90.41 | 462.6 |
| 10.03 | 40.96 | 1024.57 | 91.1 | 478.79 |
| 20.37 | 52.05 | 1012.34 | 62.57 | 456.11 |
| 21.19 | 50.16 | 1005.81 | 84.27 | 450.33 |
| 33.73 | 69.88 | 1007.21 | 42.93 | 434.83 |
| 29.87 | 73.68 | 1015.1 | 40.96 | 433.43 |
| 19.62 | 62.96 | 1020.76 | 76.53 | 456.02 |
| 9.93 | 40.67 | 1018.08 | 69.74 | 485.23 |
| 9.43 | 37.14 | 1013.03 | 74.99 | 473.57 |
| 14.24 | 39.58 | 1011.17 | 70.45 | 469.94 |
| 12.97 | 49.83 | 1008.69 | 91.49 | 452.07 |
| 7.6 | 41.04 | 1021.82 | 88.97 | 475.32 |
| 8.39 | 36.24 | 1013.39 | 89.13 | 480.69 |
| 25.41 | 48.06 | 1013.12 | 46.52 | 444.01 |
| 18.43 | 56.03 | 1020.41 | 60.55 | 465.17 |
| 10.31 | 39.82 | 1012.87 | 88.71 | 480.61 |
| 11.29 | 41.5 | 1013.39 | 89.15 | 476.04 |
| 22.61 | 49.3 | 1003.51 | 83.02 | 441.76 |
| 29.34 | 71.98 | 1005.19 | 75.19 | 428.24 |
| 18.87 | 67.71 | 1004.0 | 87.35 | 444.77 |
| 13.21 | 45.87 | 1008.58 | 85.66 | 463.1 |
| 11.3 | 44.6 | 1018.19 | 91.66 | 470.5 |
| 29.23 | 72.99 | 1007.04 | 63.47 | 431.0 |
| 27.76 | 69.4 | 1004.27 | 72.25 | 430.68 |
| 29.26 | 67.17 | 1006.6 | 70.58 | 436.42 |
| 25.72 | 49.82 | 1016.19 | 60.1 | 452.33 |
| 23.43 | 63.94 | 1010.64 | 89.29 | 440.16 |
| 25.6 | 63.76 | 1010.18 | 67.43 | 435.75 |
| 22.3 | 44.57 | 1008.48 | 67.58 | 449.74 |
| 27.91 | 72.24 | 1010.74 | 70.8 | 430.73 |
| 30.35 | 77.17 | 1009.55 | 63.62 | 432.75 |
| 21.78 | 47.43 | 1007.88 | 66.68 | 446.79 |
| 7.19 | 41.39 | 1018.12 | 90.76 | 486.35 |
| 20.88 | 59.8 | 1015.66 | 75.34 | 453.18 |
| 24.19 | 50.23 | 1015.73 | 59.77 | 458.31 |
| 9.98 | 41.54 | 1019.7 | 80.79 | 480.26 |
| 23.47 | 51.3 | 1011.89 | 74.1 | 448.65 |
| 26.35 | 49.5 | 1012.67 | 41.34 | 458.41 |
| 29.89 | 64.69 | 1006.37 | 58.78 | 435.39 |
| 19.29 | 50.16 | 1010.49 | 97.78 | 450.21 |
| 17.48 | 43.14 | 1018.68 | 74.85 | 459.59 |
| 25.21 | 75.6 | 1017.19 | 69.84 | 445.84 |
| 23.3 | 48.78 | 1018.17 | 75.36 | 441.08 |
| 15.42 | 37.85 | 1009.89 | 85.8 | 467.33 |
| 21.44 | 63.09 | 1016.56 | 90.11 | 444.19 |
| 29.45 | 68.27 | 1007.96 | 61.63 | 432.96 |
| 29.69 | 47.93 | 1002.85 | 44.76 | 438.09 |
| 15.52 | 36.99 | 1006.86 | 89.7 | 467.9 |
| 11.47 | 43.67 | 1012.68 | 72.51 | 475.72 |
| 9.77 | 34.69 | 1027.72 | 74.98 | 477.51 |
| 22.6 | 69.84 | 1006.37 | 79.59 | 435.13 |
| 8.24 | 39.61 | 1017.99 | 78.42 | 477.9 |
| 17.01 | 44.2 | 1019.18 | 61.23 | 457.26 |
| 19.64 | 44.6 | 1015.88 | 47.56 | 467.53 |
| 10.61 | 41.58 | 1021.08 | 93.06 | 465.15 |
| 12.04 | 40.1 | 1014.42 | 89.65 | 474.28 |
| 29.19 | 65.71 | 1013.85 | 50.5 | 444.49 |
| 21.75 | 45.09 | 1014.15 | 44.84 | 452.84 |
| 23.66 | 77.54 | 1008.5 | 85.32 | 435.38 |
| 27.05 | 75.33 | 1003.88 | 82.94 | 433.57 |
| 29.63 | 69.71 | 1009.04 | 67.26 | 435.27 |
| 18.2 | 39.63 | 1005.35 | 79.05 | 468.49 |
| 32.22 | 70.8 | 1009.9 | 62.03 | 433.07 |
| 26.88 | 73.56 | 1004.85 | 94.36 | 430.63 |
| 29.05 | 65.74 | 1013.29 | 60.02 | 440.74 |
| 8.9 | 39.96 | 1026.31 | 95.46 | 474.49 |
| 18.93 | 48.6 | 1005.72 | 84.92 | 449.74 |
| 27.49 | 63.76 | 1010.09 | 62.8 | 436.73 |
| 23.1 | 70.79 | 1006.53 | 90.81 | 434.58 |
| 11.22 | 43.13 | 1017.24 | 80.9 | 473.93 |
| 31.97 | 79.74 | 1007.03 | 55.84 | 435.99 |
| 13.32 | 43.22 | 1009.45 | 75.3 | 466.83 |
| 31.68 | 68.24 | 1005.29 | 37.34 | 427.22 |
| 23.69 | 63.77 | 1013.39 | 79.5 | 444.07 |
| 13.83 | 41.49 | 1020.11 | 87.29 | 469.57 |
| 18.32 | 66.51 | 1015.18 | 81.5 | 459.89 |
| 11.05 | 40.71 | 1024.91 | 76.42 | 479.59 |
| 22.03 | 64.69 | 1007.21 | 75.75 | 440.92 |
| 10.23 | 41.46 | 1020.45 | 84.95 | 480.87 |
| 23.92 | 66.54 | 1009.93 | 62.37 | 441.9 |
| 29.38 | 69.68 | 1011.35 | 49.25 | 430.2 |
| 17.35 | 42.86 | 1014.62 | 74.16 | 465.16 |
| 9.81 | 44.45 | 1021.19 | 90.55 | 471.32 |
| 4.97 | 40.64 | 1020.91 | 94.28 | 485.43 |
| 5.15 | 40.07 | 1012.27 | 63.31 | 495.35 |
| 21.54 | 58.49 | 1010.85 | 78.9 | 449.12 |
| 7.94 | 42.02 | 1006.22 | 90.97 | 480.53 |
| 18.77 | 50.66 | 1014.89 | 87.34 | 457.07 |
| 21.69 | 69.94 | 1010.7 | 80.8 | 443.67 |
| 10.07 | 44.68 | 1023.44 | 90.95 | 477.52 |
| 13.83 | 39.64 | 1012.52 | 69.97 | 472.95 |
| 10.45 | 39.69 | 1003.92 | 89.45 | 472.54 |
| 11.56 | 40.71 | 1015.85 | 76.08 | 469.17 |
| 23.64 | 70.04 | 1011.09 | 83.35 | 435.21 |
| 10.48 | 40.22 | 1004.81 | 92.16 | 477.78 |
| 13.09 | 39.85 | 1012.86 | 58.42 | 475.89 |
| 10.67 | 40.23 | 1017.75 | 85.06 | 483.9 |
| 12.57 | 39.16 | 1016.53 | 88.91 | 476.2 |
| 14.45 | 43.34 | 1015.47 | 83.33 | 462.16 |
| 14.22 | 37.85 | 1011.24 | 88.49 | 471.05 |
| 6.97 | 41.26 | 1010.6 | 96.88 | 484.71 |
| 20.61 | 63.86 | 1015.43 | 73.86 | 446.34 |
| 14.67 | 42.28 | 1007.21 | 65.17 | 469.02 |
| 29.06 | 72.86 | 1004.23 | 69.41 | 432.12 |
| 14.38 | 40.1 | 1015.51 | 81.23 | 467.28 |
| 32.51 | 69.98 | 1013.29 | 54.07 | 429.66 |
| 11.79 | 45.09 | 1013.16 | 89.17 | 469.49 |
| 8.65 | 40.56 | 1023.23 | 78.85 | 485.87 |
| 9.75 | 40.81 | 1026.0 | 84.44 | 481.95 |
| 9.11 | 40.02 | 1031.1 | 83.02 | 479.03 |
| 23.39 | 69.13 | 1010.99 | 90.66 | 434.5 |
| 14.3 | 54.3 | 1015.16 | 75.29 | 464.9 |
| 17.49 | 63.94 | 1020.02 | 82.6 | 452.71 |
| 31.1 | 69.51 | 1010.84 | 45.4 | 429.74 |
| 19.77 | 56.65 | 1020.67 | 66.33 | 457.09 |
| 28.61 | 72.29 | 1011.61 | 45.33 | 446.77 |
| 13.52 | 41.48 | 1014.46 | 67.12 | 460.76 |
| 13.52 | 40.83 | 1008.31 | 84.14 | 471.95 |
| 17.57 | 46.21 | 1014.09 | 80.81 | 453.29 |
| 28.18 | 60.07 | 1016.34 | 49.13 | 441.61 |
| 14.29 | 46.18 | 1017.01 | 87.29 | 464.73 |
| 18.12 | 43.69 | 1016.91 | 52.95 | 464.68 |
| 31.27 | 73.91 | 1003.72 | 68.92 | 430.59 |
| 26.24 | 77.95 | 1014.19 | 85.21 | 438.01 |
| 7.44 | 41.04 | 1021.84 | 88.56 | 479.08 |
| 29.78 | 74.78 | 1009.28 | 55.09 | 436.39 |
| 23.37 | 65.46 | 1016.25 | 48.64 | 447.07 |
| 10.62 | 39.58 | 1011.9 | 87.85 | 479.91 |
| 5.84 | 43.02 | 1013.88 | 87.42 | 489.05 |
| 14.51 | 53.82 | 1016.46 | 62.75 | 463.17 |
| 11.31 | 42.02 | 1001.18 | 94.86 | 471.26 |
| 11.25 | 40.67 | 1011.64 | 63.54 | 480.49 |
| 9.18 | 39.42 | 1025.41 | 69.46 | 473.78 |
| 19.82 | 58.16 | 1016.76 | 74.66 | 455.5 |
| 24.77 | 58.41 | 1013.78 | 80.57 | 446.27 |
| 9.66 | 41.06 | 1021.21 | 84.7 | 482.2 |
| 21.96 | 59.8 | 1016.72 | 72.6 | 452.48 |
| 18.59 | 43.14 | 1011.92 | 52.63 | 464.48 |
| 24.75 | 69.89 | 1015.29 | 82.01 | 438.1 |
| 24.37 | 63.47 | 1012.77 | 75.22 | 445.6 |
| 29.6 | 67.79 | 1010.37 | 51.05 | 442.43 |
| 25.32 | 61.25 | 1011.56 | 80.1 | 436.67 |
| 16.15 | 41.85 | 1016.54 | 81.58 | 466.56 |
| 15.74 | 71.14 | 1019.65 | 65.94 | 457.29 |
| 5.97 | 36.25 | 1029.65 | 86.74 | 487.03 |
| 15.84 | 52.72 | 1026.45 | 62.57 | 464.93 |
| 14.84 | 44.63 | 1019.28 | 57.37 | 466.0 |
| 12.25 | 48.79 | 1017.44 | 88.91 | 469.52 |
| 27.38 | 70.04 | 1011.18 | 72.26 | 428.88 |
| 8.76 | 41.48 | 1018.49 | 74.98 | 474.3 |
| 15.54 | 39.31 | 1009.69 | 71.19 | 461.06 |
| 18.71 | 39.39 | 1014.09 | 62.82 | 465.57 |
| 13.06 | 41.78 | 1012.3 | 55.31 | 467.67 |
| 12.72 | 40.71 | 1016.02 | 71.57 | 466.99 |
| 19.83 | 39.39 | 1013.73 | 59.16 | 463.72 |
| 27.23 | 49.16 | 1004.03 | 40.8 | 443.78 |
| 24.27 | 68.28 | 1005.43 | 67.63 | 445.23 |
| 11.8 | 40.66 | 1017.13 | 97.2 | 464.43 |
| 6.76 | 36.25 | 1028.31 | 91.16 | 484.36 |
| 25.99 | 63.07 | 1012.5 | 64.81 | 442.16 |
| 16.3 | 39.63 | 1004.64 | 85.61 | 464.11 |
| 16.5 | 49.39 | 1018.35 | 93.42 | 462.48 |
| 10.59 | 42.49 | 1009.59 | 77.36 | 477.49 |
| 26.05 | 65.59 | 1012.78 | 67.03 | 437.04 |
| 19.5 | 40.79 | 1003.8 | 89.45 | 457.09 |
| 22.21 | 45.01 | 1012.22 | 54.84 | 450.6 |
| 17.86 | 45.0 | 1023.25 | 53.48 | 465.78 |
| 29.96 | 70.04 | 1010.15 | 54.47 | 427.1 |
| 19.08 | 44.63 | 1020.14 | 43.36 | 459.81 |
| 23.59 | 47.43 | 1006.64 | 48.92 | 447.36 |
| 3.38 | 39.64 | 1011.0 | 81.22 | 488.92 |
| 26.39 | 66.49 | 1012.96 | 60.35 | 433.36 |
| 8.99 | 39.04 | 1021.99 | 75.98 | 483.35 |
| 10.91 | 41.04 | 1026.57 | 74.24 | 469.53 |
| 13.08 | 39.82 | 1012.27 | 85.21 | 476.96 |
| 23.95 | 58.46 | 1017.5 | 68.46 | 440.75 |
| 15.64 | 43.71 | 1024.51 | 78.31 | 462.55 |
| 18.78 | 54.2 | 1012.05 | 89.25 | 448.04 |
| 20.65 | 50.59 | 1016.22 | 68.57 | 455.24 |
| 4.96 | 40.07 | 1011.8 | 67.38 | 494.75 |
| 23.51 | 57.32 | 1012.55 | 53.6 | 444.58 |
| 5.99 | 35.79 | 1011.56 | 91.69 | 484.82 |
| 23.65 | 66.05 | 1019.6 | 78.21 | 442.9 |
| 5.17 | 39.33 | 1009.68 | 94.19 | 485.46 |
| 26.38 | 49.5 | 1012.82 | 37.19 | 457.81 |
| 6.02 | 43.65 | 1013.85 | 83.53 | 481.92 |
| 23.2 | 61.02 | 1009.63 | 79.45 | 443.23 |
| 8.57 | 39.69 | 1000.91 | 99.9 | 474.29 |
| 30.72 | 71.58 | 1009.98 | 50.39 | 430.46 |
| 21.52 | 50.66 | 1013.56 | 74.33 | 455.71 |
| 22.93 | 62.26 | 1011.25 | 83.66 | 438.34 |
| 5.71 | 41.31 | 1003.24 | 89.48 | 485.83 |
| 18.62 | 44.06 | 1017.76 | 64.59 | 452.82 |
| 27.88 | 68.94 | 1007.68 | 75.68 | 435.04 |
| 22.32 | 59.8 | 1016.82 | 64.18 | 451.21 |
| 14.55 | 42.74 | 1028.41 | 70.09 | 465.81 |
| 17.83 | 44.92 | 1025.04 | 70.58 | 458.42 |
| 9.68 | 39.96 | 1026.09 | 99.28 | 470.22 |
| 19.41 | 49.39 | 1020.84 | 81.89 | 449.24 |
| 13.22 | 44.92 | 1023.84 | 87.99 | 471.43 |
| 12.24 | 44.92 | 1023.74 | 88.21 | 473.26 |
| 19.21 | 58.49 | 1011.7 | 91.29 | 452.82 |
| 29.74 | 70.32 | 1008.1 | 52.72 | 432.69 |
| 23.28 | 60.84 | 1017.91 | 67.5 | 444.13 |
| 8.02 | 41.92 | 1029.8 | 92.05 | 467.21 |
| 22.47 | 48.6 | 1002.33 | 63.23 | 445.98 |
| 27.51 | 73.77 | 1002.42 | 90.88 | 436.91 |
| 17.51 | 44.9 | 1009.05 | 74.91 | 455.01 |
| 23.22 | 66.56 | 1002.47 | 85.39 | 437.11 |
| 11.73 | 40.64 | 1020.68 | 96.98 | 477.06 |
| 21.19 | 67.71 | 1006.65 | 56.28 | 441.71 |
| 5.48 | 40.07 | 1019.63 | 65.62 | 495.76 |
| 24.26 | 66.44 | 1011.33 | 55.32 | 445.63 |
| 12.32 | 41.62 | 1012.88 | 88.88 | 464.72 |
| 31.26 | 68.94 | 1005.94 | 39.49 | 438.03 |
| 32.09 | 72.86 | 1003.47 | 54.59 | 434.78 |
| 24.98 | 60.32 | 1015.63 | 57.19 | 444.67 |
| 27.48 | 61.41 | 1012.2 | 45.06 | 452.24 |
| 21.04 | 45.09 | 1014.19 | 40.62 | 450.92 |
| 27.75 | 70.4 | 1006.65 | 90.21 | 436.53 |
| 22.79 | 71.77 | 1005.75 | 90.91 | 435.53 |
| 24.22 | 68.51 | 1013.23 | 74.96 | 440.01 |
| 27.06 | 64.45 | 1008.72 | 54.21 | 443.1 |
| 29.25 | 71.94 | 1007.18 | 63.62 | 427.49 |
| 26.86 | 68.08 | 1012.99 | 50.04 | 436.25 |
| 29.64 | 67.79 | 1009.99 | 51.23 | 440.74 |
| 19.92 | 63.31 | 1015.02 | 82.71 | 443.54 |
| 18.5 | 51.43 | 1010.82 | 92.04 | 459.42 |
| 23.71 | 60.23 | 1009.76 | 90.67 | 439.66 |
| 14.39 | 44.84 | 1023.55 | 91.14 | 464.15 |
| 19.3 | 56.65 | 1020.55 | 70.43 | 459.1 |
| 24.65 | 52.36 | 1014.76 | 66.63 | 455.68 |
| 13.5 | 45.51 | 1015.33 | 86.95 | 469.08 |
| 9.82 | 41.26 | 1007.71 | 96.69 | 478.02 |
| 18.4 | 44.06 | 1017.36 | 70.88 | 456.8 |
| 28.12 | 44.89 | 1009.18 | 47.14 | 441.13 |
| 17.15 | 43.69 | 1017.05 | 63.36 | 463.88 |
| 30.69 | 73.67 | 1006.14 | 60.58 | 430.45 |
| 28.82 | 65.71 | 1014.24 | 54.3 | 449.18 |
| 21.3 | 48.92 | 1010.92 | 65.09 | 447.89 |
| 30.58 | 70.04 | 1010.4 | 48.16 | 431.59 |
| 21.17 | 52.3 | 1009.36 | 81.51 | 447.5 |
| 9.87 | 41.82 | 1033.04 | 68.57 | 475.58 |
| 22.18 | 59.8 | 1016.77 | 73.16 | 453.24 |
| 24.39 | 63.21 | 1012.59 | 80.88 | 446.4 |
| 10.73 | 44.92 | 1025.1 | 85.4 | 476.81 |
| 9.38 | 40.46 | 1019.29 | 75.77 | 474.1 |
| 20.27 | 57.76 | 1016.66 | 75.76 | 450.71 |
| 24.82 | 66.48 | 1006.4 | 70.21 | 433.62 |
| 16.55 | 41.66 | 1011.45 | 55.53 | 465.14 |
| 20.73 | 59.87 | 1019.08 | 80.48 | 445.18 |
| 9.51 | 39.22 | 1015.3 | 72.41 | 474.12 |
| 8.63 | 43.79 | 1016.08 | 83.25 | 483.91 |
| 6.48 | 40.27 | 1010.55 | 82.12 | 486.68 |
| 14.95 | 43.52 | 1022.43 | 94.75 | 464.98 |
| 5.76 | 45.87 | 1010.83 | 95.79 | 481.4 |
| 10.94 | 39.04 | 1021.81 | 86.02 | 479.2 |
| 15.87 | 41.16 | 1005.85 | 78.29 | 463.86 |
| 12.42 | 38.25 | 1012.76 | 82.23 | 472.3 |
| 29.12 | 58.84 | 1001.31 | 52.86 | 446.51 |
| 29.12 | 51.43 | 1005.93 | 60.66 | 437.71 |
| 19.08 | 41.1 | 1001.96 | 62.77 | 458.94 |
| 31.06 | 67.17 | 1007.62 | 65.54 | 437.91 |
| 5.72 | 39.33 | 1009.96 | 95.4 | 490.76 |
| 26.52 | 65.06 | 1013.4 | 51.78 | 439.66 |
| 13.84 | 44.9 | 1007.58 | 63.62 | 463.27 |
| 13.03 | 39.52 | 1016.68 | 83.09 | 473.99 |
| 25.94 | 66.49 | 1012.83 | 61.81 | 433.38 |
| 16.64 | 53.82 | 1015.13 | 68.24 | 459.01 |
| 14.13 | 40.75 | 1016.05 | 72.41 | 471.44 |
| 13.65 | 39.28 | 1012.97 | 79.64 | 471.91 |
| 14.5 | 44.47 | 1028.2 | 66.95 | 465.15 |
| 19.8 | 51.19 | 1008.25 | 91.98 | 446.66 |
| 25.2 | 63.76 | 1009.78 | 64.96 | 438.15 |
| 20.66 | 51.19 | 1008.81 | 88.93 | 447.14 |
| 12.07 | 43.71 | 1025.53 | 85.62 | 472.32 |
| 25.64 | 70.72 | 1010.16 | 84.0 | 441.68 |
| 23.33 | 72.99 | 1009.33 | 89.41 | 440.04 |
| 29.41 | 64.05 | 1009.82 | 67.4 | 444.82 |
| 16.6 | 53.16 | 1014.5 | 76.75 | 457.26 |
| 27.53 | 72.58 | 1009.13 | 89.06 | 428.83 |
| 20.62 | 43.43 | 1009.93 | 64.02 | 449.07 |
| 26.02 | 71.94 | 1009.38 | 64.12 | 435.21 |
| 12.75 | 44.2 | 1017.59 | 81.22 | 471.03 |
| 12.87 | 48.04 | 1012.47 | 100.13 | 465.56 |
| 25.77 | 62.96 | 1019.86 | 58.07 | 442.83 |
| 14.84 | 41.48 | 1017.26 | 63.42 | 460.3 |
| 7.41 | 40.71 | 1023.07 | 83.32 | 474.25 |
| 8.87 | 41.82 | 1033.3 | 74.28 | 477.97 |
| 9.69 | 40.46 | 1019.1 | 71.91 | 472.16 |
| 16.17 | 46.97 | 1014.22 | 85.8 | 456.08 |
| 26.24 | 49.82 | 1014.9 | 55.58 | 452.41 |
| 13.78 | 43.22 | 1011.31 | 69.7 | 463.71 |
| 26.3 | 67.07 | 1006.26 | 63.79 | 433.72 |
| 17.37 | 57.76 | 1016.0 | 86.59 | 456.4 |
| 23.6 | 48.98 | 1015.41 | 48.28 | 448.43 |
| 8.3 | 36.08 | 1020.63 | 80.42 | 481.6 |
| 18.86 | 42.18 | 1001.16 | 98.58 | 457.07 |
| 22.12 | 49.39 | 1019.8 | 72.83 | 451.0 |
| 28.41 | 75.6 | 1018.48 | 56.07 | 440.28 |
| 29.42 | 71.32 | 1002.26 | 67.13 | 437.47 |
| 18.61 | 67.71 | 1004.07 | 84.49 | 443.57 |
| 27.57 | 69.84 | 1004.91 | 68.37 | 426.6 |
| 12.83 | 41.5 | 1013.12 | 86.07 | 470.87 |
| 9.64 | 39.85 | 1012.9 | 83.82 | 478.37 |
| 19.13 | 58.66 | 1013.32 | 74.86 | 453.92 |
| 15.92 | 40.56 | 1020.79 | 53.52 | 470.22 |
| 24.64 | 72.24 | 1011.37 | 80.61 | 434.54 |
| 27.62 | 63.9 | 1013.11 | 43.56 | 442.89 |
| 8.9 | 36.24 | 1013.29 | 89.35 | 479.03 |
| 9.55 | 43.99 | 1020.5 | 97.28 | 476.06 |
| 10.57 | 36.71 | 1022.62 | 80.49 | 473.88 |
| 19.8 | 57.25 | 1010.84 | 88.9 | 451.75 |
| 25.63 | 56.85 | 1012.68 | 49.7 | 439.2 |
| 24.7 | 58.46 | 1015.58 | 68.64 | 439.7 |
| 15.26 | 46.18 | 1013.68 | 98.58 | 463.6 |
| 20.06 | 52.84 | 1004.21 | 82.12 | 447.47 |
| 19.84 | 56.89 | 1013.23 | 78.32 | 447.92 |
| 11.49 | 44.63 | 1020.44 | 86.04 | 471.08 |
| 23.74 | 72.43 | 1007.99 | 91.36 | 437.55 |
| 22.62 | 51.3 | 1012.36 | 81.02 | 448.27 |
| 29.53 | 72.39 | 998.47 | 76.05 | 431.69 |
| 21.32 | 48.14 | 1016.57 | 71.81 | 449.09 |
| 20.3 | 58.46 | 1015.93 | 82.13 | 448.79 |
| 16.97 | 44.92 | 1025.21 | 74.27 | 460.21 |
| 12.07 | 41.17 | 1013.54 | 71.32 | 479.28 |
| 7.46 | 41.82 | 1032.67 | 74.59 | 483.11 |
| 19.2 | 54.2 | 1011.46 | 84.44 | 450.75 |
| 28.64 | 66.54 | 1010.43 | 43.39 | 437.97 |
| 13.56 | 41.48 | 1008.53 | 87.2 | 459.76 |
| 17.4 | 44.9 | 1020.5 | 77.11 | 457.75 |
| 14.08 | 40.1 | 1015.48 | 82.81 | 469.33 |
| 27.11 | 69.75 | 1009.74 | 85.67 | 433.28 |
| 20.92 | 70.02 | 1010.23 | 95.58 | 444.64 |
| 16.18 | 44.9 | 1021.3 | 74.46 | 463.1 |
| 15.57 | 44.68 | 1022.01 | 90.02 | 460.91 |
| 10.37 | 39.04 | 1023.95 | 81.93 | 479.35 |
| 19.6 | 59.21 | 1017.65 | 86.29 | 449.23 |
| 9.22 | 40.92 | 1021.83 | 85.43 | 474.51 |
| 27.76 | 72.99 | 1007.81 | 71.66 | 435.02 |
| 28.68 | 70.72 | 1009.43 | 71.33 | 435.45 |
| 20.95 | 48.14 | 1013.3 | 67.72 | 452.38 |
| 9.06 | 39.3 | 1019.73 | 84.23 | 480.41 |
| 9.21 | 39.72 | 1019.54 | 74.44 | 478.96 |
| 13.65 | 42.74 | 1026.58 | 71.48 | 468.87 |
| 31.79 | 76.2 | 1007.89 | 56.3 | 434.01 |
| 14.32 | 44.6 | 1013.85 | 68.13 | 466.36 |
| 26.28 | 75.23 | 1011.44 | 68.35 | 435.28 |
| 7.69 | 43.02 | 1014.51 | 85.23 | 486.46 |
| 14.44 | 40.1 | 1015.51 | 79.78 | 468.19 |
| 9.19 | 41.01 | 1022.14 | 98.98 | 468.37 |
| 13.35 | 41.39 | 1019.17 | 72.87 | 474.19 |
| 23.04 | 74.22 | 1009.52 | 90.93 | 440.32 |
| 4.83 | 38.44 | 1015.35 | 72.94 | 485.32 |
| 17.29 | 42.86 | 1014.38 | 72.3 | 464.27 |
| 8.73 | 36.18 | 1013.66 | 77.74 | 479.25 |
| 26.21 | 70.32 | 1007.0 | 78.29 | 430.4 |
| 23.72 | 58.62 | 1016.65 | 69.1 | 447.49 |
| 29.27 | 64.69 | 1006.85 | 55.79 | 438.23 |
| 10.4 | 40.43 | 1025.46 | 75.09 | 492.09 |
| 12.19 | 40.75 | 1015.13 | 88.98 | 475.36 |
| 20.4 | 54.9 | 1016.68 | 64.26 | 452.56 |
| 34.3 | 74.67 | 1015.98 | 25.89 | 427.84 |
| 27.56 | 68.08 | 1010.8 | 59.18 | 433.95 |
| 30.9 | 70.8 | 1008.48 | 67.48 | 435.27 |
| 14.85 | 58.59 | 1014.04 | 89.85 | 454.62 |
| 16.42 | 40.56 | 1020.36 | 50.62 | 472.17 |
| 16.45 | 63.31 | 1015.96 | 83.97 | 452.42 |
| 10.14 | 42.02 | 1003.19 | 96.51 | 472.17 |
| 9.53 | 41.44 | 1018.01 | 80.09 | 481.83 |
| 17.01 | 49.15 | 1021.83 | 84.02 | 458.78 |
| 23.94 | 62.08 | 1022.47 | 61.97 | 447.5 |
| 15.95 | 49.25 | 1019.04 | 88.51 | 463.4 |
| 11.15 | 41.26 | 1022.67 | 81.83 | 473.57 |
| 25.56 | 70.32 | 1009.07 | 90.63 | 433.72 |
| 27.16 | 66.44 | 1011.2 | 73.37 | 431.85 |
| 26.71 | 77.95 | 1012.13 | 77.5 | 433.47 |
| 29.56 | 74.22 | 1007.45 | 57.46 | 432.84 |
| 31.19 | 70.94 | 1007.29 | 51.91 | 436.6 |
| 6.86 | 41.17 | 1020.12 | 79.14 | 490.23 |
| 12.36 | 41.74 | 1020.58 | 69.24 | 477.16 |
| 32.82 | 68.31 | 1010.44 | 41.85 | 441.06 |
| 25.3 | 70.98 | 1007.22 | 95.1 | 440.86 |
| 8.71 | 41.82 | 1033.08 | 74.53 | 477.94 |
| 13.34 | 40.8 | 1026.56 | 64.85 | 474.47 |
| 14.2 | 43.02 | 1012.18 | 57.07 | 470.67 |
| 23.74 | 65.34 | 1013.7 | 62.9 | 447.31 |
| 16.9 | 44.88 | 1018.14 | 72.21 | 466.8 |
| 28.54 | 71.94 | 1007.4 | 65.99 | 430.91 |
| 30.15 | 69.88 | 1007.2 | 73.67 | 434.75 |
| 14.33 | 42.86 | 1010.82 | 88.59 | 469.52 |
| 25.57 | 59.43 | 1008.88 | 61.19 | 438.9 |
| 30.55 | 70.04 | 1010.51 | 49.37 | 429.56 |
| 28.04 | 74.33 | 1013.53 | 48.65 | 432.92 |
| 26.39 | 49.16 | 1005.68 | 56.18 | 442.87 |
| 15.3 | 41.76 | 1022.57 | 71.56 | 466.59 |
| 6.03 | 41.14 | 1028.04 | 87.46 | 479.61 |
| 13.49 | 44.63 | 1019.12 | 70.02 | 471.08 |
| 27.67 | 59.14 | 1016.51 | 61.2 | 433.37 |
| 24.19 | 65.48 | 1018.8 | 60.54 | 443.92 |
| 24.44 | 59.14 | 1016.74 | 71.82 | 443.5 |
| 29.86 | 64.79 | 1017.37 | 44.8 | 439.89 |
| 30.2 | 69.59 | 1008.9 | 67.32 | 434.66 |
| 7.99 | 41.38 | 1021.95 | 78.77 | 487.57 |
| 9.93 | 41.62 | 1013.76 | 96.02 | 464.64 |
| 11.03 | 42.32 | 1017.26 | 90.56 | 470.92 |
| 22.34 | 63.73 | 1014.37 | 83.19 | 444.39 |
| 25.33 | 48.6 | 1002.54 | 68.45 | 442.48 |
| 18.87 | 52.08 | 1005.25 | 99.19 | 449.61 |
| 25.97 | 69.34 | 1009.43 | 88.11 | 435.02 |
| 16.58 | 43.99 | 1021.81 | 79.29 | 458.67 |
| 14.35 | 46.18 | 1016.63 | 87.76 | 461.74 |
| 25.06 | 62.39 | 1008.09 | 82.56 | 438.31 |
| 13.85 | 48.92 | 1011.68 | 79.24 | 462.38 |
| 16.09 | 44.2 | 1019.39 | 67.24 | 460.56 |
| 26.34 | 59.21 | 1013.37 | 58.98 | 439.22 |
| 23.01 | 58.79 | 1009.71 | 84.22 | 444.64 |
| 26.39 | 71.25 | 999.8 | 89.12 | 430.34 |
| 31.32 | 71.29 | 1008.37 | 50.07 | 430.46 |
| 16.64 | 45.87 | 1009.02 | 98.86 | 456.79 |
| 13.42 | 41.23 | 994.17 | 95.79 | 468.82 |
| 20.06 | 44.9 | 1008.79 | 70.06 | 448.51 |
| 14.8 | 44.71 | 1014.67 | 41.71 | 470.77 |
| 12.59 | 41.14 | 1025.79 | 86.55 | 465.74 |
| 26.7 | 66.56 | 1005.31 | 71.97 | 430.21 |
| 19.78 | 50.32 | 1008.62 | 96.4 | 449.23 |
| 15.17 | 49.15 | 1021.91 | 91.73 | 461.89 |
| 21.71 | 61.45 | 1010.97 | 91.62 | 445.72 |
| 19.09 | 39.39 | 1013.36 | 59.14 | 466.13 |
| 19.76 | 51.19 | 1008.38 | 92.56 | 448.71 |
| 14.68 | 41.23 | 998.43 | 83.71 | 469.25 |
| 21.3 | 66.86 | 1013.04 | 55.43 | 450.56 |
| 16.73 | 39.64 | 1008.94 | 74.91 | 464.46 |
| 12.26 | 41.5 | 1014.87 | 89.41 | 471.13 |
| 14.77 | 48.06 | 1010.92 | 69.81 | 461.52 |
| 18.26 | 59.15 | 1012.04 | 86.01 | 451.09 |
| 27.1 | 79.74 | 1005.43 | 86.05 | 431.51 |
| 14.72 | 40.83 | 1009.65 | 80.98 | 469.8 |
| 26.3 | 51.43 | 1012.05 | 63.62 | 442.28 |
| 16.48 | 48.92 | 1011.84 | 64.16 | 458.67 |
| 17.99 | 43.79 | 1016.13 | 75.63 | 462.4 |
| 20.34 | 59.8 | 1015.18 | 80.21 | 453.54 |
| 25.53 | 62.96 | 1019.81 | 59.7 | 444.38 |
| 31.59 | 58.9 | 1003.39 | 47.6 | 440.52 |
| 30.8 | 69.14 | 1007.68 | 63.78 | 433.62 |
| 10.75 | 45.0 | 1023.68 | 89.37 | 481.96 |
| 19.3 | 44.9 | 1008.89 | 70.55 | 452.75 |
| 4.71 | 39.42 | 1026.4 | 84.42 | 481.28 |
| 23.1 | 66.05 | 1020.28 | 80.62 | 439.03 |
| 32.63 | 73.88 | 1005.64 | 52.56 | 435.75 |
| 26.63 | 74.16 | 1009.72 | 83.26 | 436.03 |
| 24.35 | 58.49 | 1011.03 | 70.64 | 445.6 |
| 15.11 | 56.03 | 1020.27 | 89.95 | 462.65 |
| 29.1 | 50.05 | 1005.87 | 51.53 | 438.66 |
| 21.24 | 50.32 | 1008.54 | 84.83 | 447.32 |
| 6.16 | 39.48 | 1004.85 | 59.68 | 484.55 |
| 7.36 | 41.01 | 1024.9 | 97.88 | 476.8 |
| 10.44 | 39.04 | 1023.99 | 85.03 | 480.34 |
| 26.76 | 48.41 | 1010.53 | 47.38 | 440.63 |
| 16.79 | 44.6 | 1014.27 | 48.08 | 459.48 |
| 10.76 | 40.43 | 1025.98 | 79.65 | 490.78 |
| 6.07 | 38.91 | 1019.25 | 83.39 | 483.56 |
| 27.33 | 73.18 | 1012.26 | 82.18 | 429.38 |
| 27.15 | 59.21 | 1013.49 | 51.71 | 440.27 |
| 22.35 | 51.43 | 1011.34 | 77.33 | 445.34 |
| 21.82 | 65.27 | 1013.86 | 72.81 | 447.43 |
| 21.11 | 69.94 | 1004.37 | 84.26 | 439.91 |
| 19.95 | 50.59 | 1016.11 | 73.23 | 459.27 |
| 7.45 | 39.61 | 1017.88 | 79.73 | 478.89 |
| 15.36 | 41.66 | 1012.41 | 62.32 | 466.7 |
| 15.65 | 43.5 | 1021.39 | 78.58 | 463.5 |
| 25.31 | 74.33 | 1015.04 | 79.88 | 436.21 |
| 25.88 | 63.47 | 1011.95 | 65.87 | 443.94 |
| 24.6 | 63.94 | 1012.87 | 80.28 | 439.63 |
| 22.58 | 41.54 | 1013.21 | 71.33 | 460.95 |
| 19.69 | 59.14 | 1015.99 | 70.33 | 448.69 |
| 25.85 | 75.08 | 1006.24 | 57.73 | 444.63 |
| 10.06 | 37.83 | 1005.49 | 99.46 | 473.51 |
| 18.59 | 39.54 | 1008.56 | 68.61 | 462.56 |
| 18.27 | 50.16 | 1011.07 | 95.91 | 451.76 |
| 8.85 | 40.43 | 1025.68 | 80.42 | 491.81 |
| 30.04 | 68.08 | 1011.04 | 51.01 | 429.52 |
| 26.06 | 49.02 | 1007.59 | 74.08 | 437.9 |
| 14.8 | 38.73 | 1003.18 | 80.73 | 467.54 |
| 23.93 | 64.45 | 1015.35 | 54.71 | 449.97 |
| 23.72 | 66.48 | 1003.61 | 73.75 | 436.62 |
| 11.44 | 40.55 | 1023.37 | 88.43 | 477.68 |
| 20.28 | 63.86 | 1016.04 | 74.66 | 447.26 |
| 27.9 | 63.13 | 1011.8 | 70.04 | 439.76 |
| 24.74 | 59.39 | 1015.23 | 74.64 | 437.49 |
| 14.8 | 58.2 | 1018.29 | 85.11 | 455.14 |
| 8.22 | 41.03 | 1021.76 | 82.97 | 485.5 |
| 27.56 | 66.93 | 1016.81 | 55.59 | 444.1 |
| 32.07 | 70.94 | 1006.91 | 49.9 | 432.33 |
| 9.53 | 44.03 | 1008.87 | 89.99 | 471.23 |
| 13.61 | 42.34 | 1017.93 | 91.61 | 463.89 |
| 22.2 | 51.19 | 1009.2 | 82.95 | 445.54 |
| 21.36 | 59.54 | 1007.99 | 92.62 | 446.09 |
| 23.25 | 63.86 | 1017.82 | 59.64 | 445.12 |
| 23.5 | 59.21 | 1018.29 | 63.0 | 443.31 |
| 8.46 | 39.66 | 1015.14 | 85.38 | 484.16 |
| 8.19 | 40.69 | 1019.86 | 85.23 | 477.76 |
| 30.67 | 71.29 | 1008.36 | 52.08 | 430.28 |
| 32.48 | 62.04 | 1010.39 | 38.05 | 446.48 |
| 8.99 | 36.66 | 1028.11 | 71.98 | 481.03 |
| 13.77 | 47.83 | 1007.41 | 90.66 | 466.07 |
| 19.05 | 67.32 | 1013.2 | 83.14 | 447.47 |
| 21.19 | 55.5 | 1019.83 | 65.22 | 455.93 |
| 10.12 | 40.0 | 1021.15 | 91.67 | 479.62 |
| 24.93 | 47.01 | 1014.28 | 66.04 | 455.06 |
| 8.47 | 40.46 | 1019.87 | 78.19 | 475.06 |
| 24.52 | 56.85 | 1012.59 | 54.47 | 438.89 |
| 28.55 | 69.84 | 1003.38 | 67.26 | 432.7 |
| 20.58 | 50.9 | 1011.89 | 72.56 | 452.6 |
| 18.31 | 46.21 | 1010.46 | 82.15 | 451.75 |
| 27.18 | 71.06 | 1008.16 | 86.32 | 430.66 |
| 4.43 | 38.91 | 1019.04 | 88.17 | 491.9 |
| 26.02 | 74.78 | 1010.04 | 72.78 | 439.82 |
| 15.75 | 39.0 | 1015.91 | 69.58 | 460.73 |
| 22.99 | 60.95 | 1015.14 | 69.86 | 449.7 |
| 25.52 | 59.15 | 1013.88 | 65.37 | 439.42 |
| 27.04 | 65.06 | 1013.33 | 52.37 | 439.84 |
| 6.42 | 35.57 | 1025.58 | 79.63 | 485.86 |
| 17.04 | 40.12 | 1011.81 | 83.14 | 458.1 |
| 10.79 | 39.82 | 1012.89 | 88.25 | 479.92 |
| 20.41 | 56.03 | 1019.94 | 55.85 | 458.29 |
| 7.36 | 40.07 | 1017.29 | 52.55 | 489.45 |
| 28.08 | 73.42 | 1012.17 | 62.74 | 434.0 |
| 24.74 | 69.13 | 1010.69 | 90.08 | 431.24 |
| 28.32 | 47.93 | 1003.26 | 54.5 | 439.5 |
| 16.71 | 40.56 | 1019.48 | 49.88 | 467.46 |
| 30.7 | 71.58 | 1010.0 | 48.96 | 429.27 |
| 18.42 | 58.95 | 1016.95 | 86.77 | 452.1 |
| 10.62 | 42.02 | 999.83 | 96.66 | 472.41 |
| 22.18 | 69.05 | 1002.75 | 70.84 | 442.14 |
| 22.38 | 49.3 | 1003.56 | 83.83 | 441.0 |
| 13.94 | 41.58 | 1020.76 | 68.22 | 463.07 |
| 21.24 | 60.84 | 1017.99 | 82.22 | 445.71 |
| 6.76 | 39.81 | 1017.11 | 87.9 | 483.16 |
| 26.73 | 68.84 | 1010.75 | 66.83 | 440.45 |
| 7.24 | 38.06 | 1020.6 | 85.36 | 481.83 |
| 10.84 | 40.62 | 1015.53 | 60.9 | 467.6 |
| 19.32 | 52.84 | 1004.29 | 83.51 | 450.88 |
| 29.0 | 69.13 | 1001.22 | 52.96 | 425.5 |
| 23.38 | 54.42 | 1013.95 | 73.02 | 451.87 |
| 31.17 | 69.51 | 1010.51 | 43.11 | 428.94 |
| 26.17 | 48.6 | 1002.59 | 61.41 | 439.86 |
| 30.9 | 73.42 | 1011.21 | 65.32 | 433.44 |
| 24.92 | 73.68 | 1015.12 | 93.68 | 438.23 |
| 32.77 | 71.32 | 1007.68 | 42.39 | 436.95 |
| 14.37 | 40.56 | 1021.67 | 68.18 | 470.19 |
| 8.36 | 40.22 | 1011.6 | 89.18 | 484.66 |
| 31.45 | 68.27 | 1007.56 | 64.79 | 430.81 |
| 31.6 | 73.17 | 1010.05 | 43.48 | 433.37 |
| 17.9 | 48.98 | 1014.17 | 80.4 | 453.02 |
| 20.35 | 50.9 | 1012.6 | 72.43 | 453.5 |
| 16.21 | 41.23 | 995.88 | 80.0 | 463.09 |
| 19.36 | 44.6 | 1016.25 | 45.65 | 464.56 |
| 21.04 | 65.46 | 1017.22 | 63.02 | 452.12 |
| 14.05 | 40.69 | 1015.66 | 74.39 | 470.9 |
| 23.48 | 64.15 | 1021.08 | 57.77 | 450.89 |
| 21.91 | 63.76 | 1009.85 | 76.8 | 445.04 |
| 24.42 | 63.07 | 1011.49 | 67.39 | 444.72 |
| 14.26 | 40.92 | 1022.07 | 73.96 | 460.38 |
| 21.38 | 58.33 | 1013.05 | 72.75 | 446.8 |
| 15.71 | 44.06 | 1018.34 | 71.69 | 465.05 |
| 5.78 | 40.62 | 1016.55 | 84.98 | 484.13 |
| 6.77 | 39.81 | 1017.01 | 87.68 | 488.27 |
| 23.84 | 49.21 | 1013.85 | 50.36 | 447.09 |
| 21.17 | 58.16 | 1017.16 | 68.11 | 452.02 |
| 19.94 | 58.96 | 1014.16 | 66.27 | 455.55 |
| 8.73 | 41.92 | 1029.41 | 89.72 | 480.99 |
| 16.39 | 41.67 | 1012.96 | 61.07 | 467.68 |
| col_name | data_type | comment |
|---|---|---|
| AT | double | null |
| V | double | null |
| AP | double | null |
| RH | double | null |
| PE | double | null |
| summary | AT | V | AP | RH | PE |
|---|---|---|---|---|---|
| count | 9568 | 9568 | 9568 | 9568 | 9568 |
| mean | 19.65123118729102 | 54.30580372073601 | 1013.2590781772603 | 73.30897784280926 | 454.3650094063554 |
| stddev | 7.4524732296110825 | 12.707892998326784 | 5.938783705811581 | 14.600268756728964 | 17.066994999803402 |
| min | 1.81 | 25.36 | 992.89 | 25.56 | 420.26 |
| max | 37.11 | 81.56 | 1033.3 | 100.16 | 495.76 |
| Temperature | Power |
|---|---|
| 14.96 | 463.26 |
| 25.18 | 444.37 |
| 5.11 | 488.56 |
| 20.86 | 446.48 |
| 10.82 | 473.9 |
| 26.27 | 443.67 |
| 15.89 | 467.35 |
| 9.48 | 478.42 |
| 14.64 | 475.98 |
| 11.74 | 477.5 |
| 17.99 | 453.02 |
| 20.14 | 453.99 |
| 24.34 | 440.29 |
| 25.71 | 451.28 |
| 26.19 | 433.99 |
| 21.42 | 462.19 |
| 18.21 | 467.54 |
| 11.04 | 477.2 |
| 14.45 | 459.85 |
| 13.97 | 464.3 |
| 17.76 | 468.27 |
| 5.41 | 495.24 |
| 7.76 | 483.8 |
| 27.23 | 443.61 |
| 27.36 | 436.06 |
| 27.47 | 443.25 |
| 14.6 | 464.16 |
| 7.91 | 475.52 |
| 5.81 | 484.41 |
| 30.53 | 437.89 |
| 23.87 | 445.11 |
| 26.09 | 438.86 |
| 29.27 | 440.98 |
| 27.38 | 436.65 |
| 24.81 | 444.26 |
| 12.75 | 465.86 |
| 24.66 | 444.37 |
| 16.38 | 450.69 |
| 13.91 | 469.02 |
| 23.18 | 448.86 |
| 22.47 | 447.14 |
| 13.39 | 469.18 |
| 9.28 | 482.8 |
| 11.82 | 476.7 |
| 10.27 | 474.99 |
| 22.92 | 444.22 |
| 16.0 | 461.33 |
| 21.22 | 448.06 |
| 13.46 | 474.6 |
| 9.39 | 473.05 |
| 31.07 | 432.06 |
| 12.82 | 467.41 |
| 32.57 | 430.12 |
| 8.11 | 473.62 |
| 13.92 | 471.81 |
| 23.04 | 442.99 |
| 27.31 | 442.77 |
| 5.91 | 491.49 |
| 25.26 | 447.46 |
| 27.97 | 446.11 |
| 26.08 | 442.44 |
| 29.01 | 446.22 |
| 12.18 | 471.49 |
| 13.76 | 463.5 |
| 25.5 | 440.01 |
| 28.26 | 441.03 |
| 21.39 | 452.68 |
| 7.26 | 474.91 |
| 10.54 | 478.77 |
| 27.71 | 434.2 |
| 23.11 | 437.91 |
| 7.51 | 477.61 |
| 26.46 | 431.65 |
| 29.34 | 430.57 |
| 10.32 | 481.09 |
| 22.74 | 445.56 |
| 13.48 | 475.74 |
| 25.52 | 435.12 |
| 21.58 | 446.15 |
| 27.66 | 436.64 |
| 26.96 | 436.69 |
| 12.29 | 468.75 |
| 15.86 | 466.6 |
| 13.87 | 465.48 |
| 24.09 | 441.34 |
| 20.45 | 441.83 |
| 15.07 | 464.7 |
| 32.72 | 437.99 |
| 18.23 | 459.12 |
| 35.56 | 429.69 |
| 18.36 | 459.8 |
| 26.35 | 433.63 |
| 25.92 | 442.84 |
| 8.01 | 485.13 |
| 19.63 | 459.12 |
| 20.02 | 445.31 |
| 10.08 | 480.8 |
| 27.23 | 432.55 |
| 23.37 | 443.86 |
| 18.74 | 449.77 |
| 14.81 | 470.71 |
| 23.1 | 452.17 |
| 10.72 | 478.29 |
| 29.46 | 428.54 |
| 8.1 | 478.27 |
| 27.29 | 439.58 |
| 17.1 | 457.32 |
| 11.49 | 475.51 |
| 23.69 | 439.66 |
| 13.51 | 471.99 |
| 9.64 | 479.81 |
| 25.65 | 434.78 |
| 21.59 | 446.58 |
| 27.98 | 437.76 |
| 18.8 | 459.36 |
| 18.28 | 462.28 |
| 13.55 | 464.33 |
| 22.99 | 444.36 |
| 23.94 | 438.64 |
| 13.74 | 470.49 |
| 21.3 | 455.13 |
| 27.54 | 450.22 |
| 24.81 | 440.43 |
| 4.97 | 482.98 |
| 15.22 | 460.44 |
| 23.88 | 444.97 |
| 33.01 | 433.94 |
| 25.98 | 439.73 |
| 28.18 | 434.48 |
| 21.67 | 442.33 |
| 17.67 | 457.67 |
| 21.37 | 454.66 |
| 28.69 | 432.21 |
| 16.61 | 457.66 |
| 27.91 | 435.21 |
| 20.97 | 448.22 |
| 10.8 | 475.51 |
| 20.61 | 446.53 |
| 25.45 | 441.3 |
| 30.16 | 433.54 |
| 4.99 | 472.52 |
| 10.51 | 474.77 |
| 33.79 | 435.1 |
| 21.34 | 450.74 |
| 23.4 | 442.7 |
| 32.21 | 426.56 |
| 14.26 | 463.71 |
| 27.71 | 447.06 |
| 21.95 | 452.27 |
| 25.76 | 445.78 |
| 23.68 | 438.65 |
| 8.28 | 480.15 |
| 23.44 | 447.19 |
| 25.32 | 443.04 |
| 3.94 | 488.81 |
| 17.3 | 455.75 |
| 18.2 | 455.86 |
| 21.43 | 457.68 |
| 11.16 | 479.11 |
| 30.38 | 432.84 |
| 23.36 | 448.37 |
| 21.69 | 447.06 |
| 23.62 | 443.53 |
| 21.87 | 445.21 |
| 29.25 | 441.7 |
| 20.03 | 450.93 |
| 18.14 | 451.44 |
| 24.23 | 441.29 |
| 18.11 | 458.85 |
| 6.57 | 481.46 |
| 12.56 | 467.19 |
| 13.4 | 461.54 |
| 27.1 | 439.08 |
| 14.28 | 467.22 |
| 16.29 | 468.8 |
| 31.24 | 426.93 |
| 10.57 | 474.65 |
| 13.8 | 468.97 |
| 25.3 | 433.97 |
| 18.06 | 450.53 |
| 25.42 | 444.51 |
| 15.07 | 469.03 |
| 11.75 | 466.56 |
| 20.23 | 457.57 |
| 27.31 | 440.13 |
| 28.57 | 433.24 |
| 17.9 | 452.55 |
| 23.83 | 443.29 |
| 27.92 | 431.76 |
| 17.34 | 454.97 |
| 17.94 | 456.7 |
| 6.4 | 486.03 |
| 11.78 | 472.79 |
| 20.28 | 452.03 |
| 21.04 | 443.41 |
| 25.11 | 441.93 |
| 30.28 | 432.64 |
| 8.14 | 480.25 |
| 16.86 | 466.68 |
| 6.25 | 494.39 |
| 22.35 | 454.72 |
| 17.98 | 448.71 |
| 21.19 | 469.76 |
| 20.94 | 450.71 |
| 24.23 | 444.01 |
| 19.18 | 453.2 |
| 20.88 | 450.87 |
| 23.67 | 441.73 |
| 14.12 | 465.09 |
| 25.23 | 447.28 |
| 6.54 | 491.16 |
| 20.08 | 450.98 |
| 24.67 | 446.3 |
| 27.82 | 436.48 |
| 15.55 | 460.84 |
| 24.26 | 442.56 |
| 13.45 | 467.3 |
| 11.06 | 479.13 |
| 24.91 | 441.15 |
| 22.39 | 445.52 |
| 11.95 | 475.4 |
| 14.85 | 469.3 |
| 10.11 | 463.57 |
| 23.67 | 445.32 |
| 16.14 | 461.03 |
| 15.11 | 466.74 |
| 24.14 | 444.04 |
| 30.08 | 434.01 |
| 14.77 | 465.23 |
| 27.6 | 440.6 |
| 13.89 | 466.74 |
| 26.85 | 433.48 |
| 12.41 | 473.59 |
| 13.08 | 474.81 |
| 18.93 | 454.75 |
| 20.5 | 452.94 |
| 30.72 | 435.83 |
| 7.55 | 482.19 |
| 13.49 | 466.66 |
| 15.62 | 462.59 |
| 24.8 | 447.82 |
| 10.03 | 462.73 |
| 22.43 | 447.98 |
| 14.95 | 462.72 |
| 24.78 | 442.42 |
| 23.2 | 444.69 |
| 14.01 | 466.7 |
| 19.4 | 453.84 |
| 30.15 | 436.92 |
| 6.91 | 486.37 |
| 29.04 | 440.43 |
| 26.02 | 446.82 |
| 5.89 | 484.91 |
| 26.52 | 437.76 |
| 28.53 | 438.91 |
| 16.59 | 464.19 |
| 22.95 | 442.19 |
| 23.96 | 446.86 |
| 17.48 | 457.15 |
| 6.69 | 482.57 |
| 10.25 | 476.03 |
| 28.87 | 428.89 |
| 12.04 | 472.7 |
| 22.58 | 445.6 |
| 15.12 | 464.78 |
| 25.48 | 440.42 |
| 27.87 | 428.41 |
| 23.72 | 438.5 |
| 25.0 | 438.28 |
| 8.42 | 476.29 |
| 22.46 | 448.46 |
| 29.92 | 438.99 |
| 11.68 | 471.8 |
| 14.04 | 471.81 |
| 19.86 | 449.82 |
| 25.99 | 442.14 |
| 23.42 | 441.46 |
| 10.6 | 477.62 |
| 20.97 | 446.76 |
| 14.14 | 472.52 |
| 8.56 | 471.58 |
| 24.86 | 440.85 |
| 29.0 | 431.37 |
| 27.59 | 437.33 |
| 10.45 | 469.22 |
| 8.51 | 471.11 |
| 29.82 | 439.17 |
| 22.56 | 445.33 |
| 11.38 | 473.71 |
| 20.25 | 452.66 |
| 22.42 | 440.99 |
| 14.85 | 467.42 |
| 25.62 | 444.14 |
| 19.85 | 457.17 |
| 13.67 | 467.87 |
| 24.39 | 442.04 |
| 16.07 | 471.36 |
| 11.6 | 460.7 |
| 31.38 | 431.33 |
| 29.91 | 432.6 |
| 19.67 | 447.61 |
| 27.18 | 443.87 |
| 21.39 | 446.87 |
| 10.45 | 465.74 |
| 19.46 | 447.86 |
| 23.55 | 447.65 |
| 23.35 | 437.87 |
| 9.26 | 483.51 |
| 10.3 | 479.65 |
| 20.94 | 455.16 |
| 23.13 | 431.91 |
| 12.77 | 470.68 |
| 28.29 | 429.28 |
| 19.13 | 450.81 |
| 24.44 | 437.73 |
| 20.32 | 460.21 |
| 20.54 | 442.86 |
| 12.16 | 482.99 |
| 28.09 | 440.0 |
| 9.25 | 478.48 |
| 21.75 | 455.28 |
| 23.7 | 436.94 |
| 16.22 | 461.06 |
| 24.75 | 438.28 |
| 10.48 | 472.61 |
| 29.53 | 426.85 |
| 12.59 | 470.18 |
| 23.5 | 455.38 |
| 29.01 | 428.32 |
| 9.75 | 480.35 |
| 19.55 | 455.56 |
| 21.05 | 447.66 |
| 24.72 | 443.06 |
| 21.19 | 452.43 |
| 10.77 | 477.81 |
| 28.68 | 431.66 |
| 29.87 | 431.8 |
| 22.99 | 446.67 |
| 24.66 | 445.26 |
| 32.63 | 425.72 |
| 31.38 | 430.58 |
| 23.87 | 439.86 |
| 25.6 | 441.11 |
| 27.62 | 434.72 |
| 30.1 | 434.01 |
| 12.19 | 475.64 |
| 13.11 | 460.44 |
| 28.29 | 436.4 |
| 13.45 | 461.03 |
| 10.98 | 479.08 |
| 26.48 | 435.76 |
| 13.07 | 460.14 |
| 25.56 | 442.2 |
| 22.68 | 447.69 |
| 28.86 | 431.15 |
| 22.7 | 445.0 |
| 27.89 | 431.59 |
| 13.78 | 467.22 |
| 28.14 | 445.33 |
| 11.8 | 470.57 |
| 10.71 | 473.77 |
| 24.54 | 447.67 |
| 11.54 | 474.29 |
| 29.47 | 437.14 |
| 29.24 | 432.56 |
| 14.51 | 459.14 |
| 22.91 | 446.19 |
| 27.02 | 428.1 |
| 13.49 | 468.46 |
| 30.24 | 435.02 |
| 23.19 | 445.52 |
| 17.73 | 462.69 |
| 18.62 | 455.75 |
| 12.85 | 463.74 |
| 32.33 | 439.79 |
| 25.09 | 443.26 |
| 29.45 | 432.04 |
| 16.91 | 465.86 |
| 14.09 | 465.6 |
| 10.73 | 469.43 |
| 23.2 | 440.75 |
| 8.21 | 481.32 |
| 9.3 | 479.87 |
| 16.97 | 458.59 |
| 23.69 | 438.62 |
| 25.13 | 445.59 |
| 9.86 | 481.87 |
| 11.33 | 475.01 |
| 26.95 | 436.54 |
| 15.0 | 456.63 |
| 20.76 | 451.69 |
| 14.29 | 463.04 |
| 19.74 | 446.1 |
| 26.68 | 438.67 |
| 14.24 | 466.88 |
| 21.98 | 444.6 |
| 22.75 | 440.26 |
| 8.34 | 483.92 |
| 11.8 | 475.19 |
| 8.81 | 479.24 |
| 30.05 | 434.92 |
| 16.01 | 454.16 |
| 21.75 | 447.58 |
| 13.94 | 467.9 |
| 29.25 | 426.29 |
| 22.33 | 447.02 |
| 16.43 | 455.85 |
| 11.5 | 476.46 |
| 23.53 | 437.48 |
| 21.86 | 452.77 |
| 6.17 | 491.54 |
| 30.19 | 438.41 |
| 11.67 | 476.1 |
| 15.34 | 464.58 |
| 11.5 | 467.74 |
| 25.53 | 442.12 |
| 21.27 | 453.34 |
| 28.37 | 425.29 |
| 28.39 | 449.63 |
| 13.78 | 462.88 |
| 14.6 | 464.67 |
| 5.1 | 489.96 |
| 7.0 | 482.38 |
| 26.3 | 437.95 |
| 30.56 | 429.2 |
| 21.09 | 453.34 |
| 28.21 | 442.47 |
| 15.84 | 462.6 |
| 10.03 | 478.79 |
| 20.37 | 456.11 |
| 21.19 | 450.33 |
| 33.73 | 434.83 |
| 29.87 | 433.43 |
| 19.62 | 456.02 |
| 9.93 | 485.23 |
| 9.43 | 473.57 |
| 14.24 | 469.94 |
| 12.97 | 452.07 |
| 7.6 | 475.32 |
| 8.39 | 480.69 |
| 25.41 | 444.01 |
| 18.43 | 465.17 |
| 10.31 | 480.61 |
| 11.29 | 476.04 |
| 22.61 | 441.76 |
| 29.34 | 428.24 |
| 18.87 | 444.77 |
| 13.21 | 463.1 |
| 11.3 | 470.5 |
| 29.23 | 431.0 |
| 27.76 | 430.68 |
| 29.26 | 436.42 |
| 25.72 | 452.33 |
| 23.43 | 440.16 |
| 25.6 | 435.75 |
| 22.3 | 449.74 |
| 27.91 | 430.73 |
| 30.35 | 432.75 |
| 21.78 | 446.79 |
| 7.19 | 486.35 |
| 20.88 | 453.18 |
| 24.19 | 458.31 |
| 9.98 | 480.26 |
| 23.47 | 448.65 |
| 26.35 | 458.41 |
| 29.89 | 435.39 |
| 19.29 | 450.21 |
| 17.48 | 459.59 |
| 25.21 | 445.84 |
| 23.3 | 441.08 |
| 15.42 | 467.33 |
| 21.44 | 444.19 |
| 29.45 | 432.96 |
| 29.69 | 438.09 |
| 15.52 | 467.9 |
| 11.47 | 475.72 |
| 9.77 | 477.51 |
| 22.6 | 435.13 |
| 8.24 | 477.9 |
| 17.01 | 457.26 |
| 19.64 | 467.53 |
| 10.61 | 465.15 |
| 12.04 | 474.28 |
| 29.19 | 444.49 |
| 21.75 | 452.84 |
| 23.66 | 435.38 |
| 27.05 | 433.57 |
| 29.63 | 435.27 |
| 18.2 | 468.49 |
| 32.22 | 433.07 |
| 26.88 | 430.63 |
| 29.05 | 440.74 |
| 8.9 | 474.49 |
| 18.93 | 449.74 |
| 27.49 | 436.73 |
| 23.1 | 434.58 |
| 11.22 | 473.93 |
| 31.97 | 435.99 |
| 13.32 | 466.83 |
| 31.68 | 427.22 |
| 23.69 | 444.07 |
| 13.83 | 469.57 |
| 18.32 | 459.89 |
| 11.05 | 479.59 |
| 22.03 | 440.92 |
| 10.23 | 480.87 |
| 23.92 | 441.9 |
| 29.38 | 430.2 |
| 17.35 | 465.16 |
| 9.81 | 471.32 |
| 4.97 | 485.43 |
| 5.15 | 495.35 |
| 21.54 | 449.12 |
| 7.94 | 480.53 |
| 18.77 | 457.07 |
| 21.69 | 443.67 |
| 10.07 | 477.52 |
| 13.83 | 472.95 |
| 10.45 | 472.54 |
| 11.56 | 469.17 |
| 23.64 | 435.21 |
| 10.48 | 477.78 |
| 13.09 | 475.89 |
| 10.67 | 483.9 |
| 12.57 | 476.2 |
| 14.45 | 462.16 |
| 14.22 | 471.05 |
| 6.97 | 484.71 |
| 20.61 | 446.34 |
| 14.67 | 469.02 |
| 29.06 | 432.12 |
| 14.38 | 467.28 |
| 32.51 | 429.66 |
| 11.79 | 469.49 |
| 8.65 | 485.87 |
| 9.75 | 481.95 |
| 9.11 | 479.03 |
| 23.39 | 434.5 |
| 14.3 | 464.9 |
| 17.49 | 452.71 |
| 31.1 | 429.74 |
| 19.77 | 457.09 |
| 28.61 | 446.77 |
| 13.52 | 460.76 |
| 13.52 | 471.95 |
| 17.57 | 453.29 |
| 28.18 | 441.61 |
| 14.29 | 464.73 |
| 18.12 | 464.68 |
| 31.27 | 430.59 |
| 26.24 | 438.01 |
| 7.44 | 479.08 |
| 29.78 | 436.39 |
| 23.37 | 447.07 |
| 10.62 | 479.91 |
| 5.84 | 489.05 |
| 14.51 | 463.17 |
| 11.31 | 471.26 |
| 11.25 | 480.49 |
| 9.18 | 473.78 |
| 19.82 | 455.5 |
| 24.77 | 446.27 |
| 9.66 | 482.2 |
| 21.96 | 452.48 |
| 18.59 | 464.48 |
| 24.75 | 438.1 |
| 24.37 | 445.6 |
| 29.6 | 442.43 |
| 25.32 | 436.67 |
| 16.15 | 466.56 |
| 15.74 | 457.29 |
| 5.97 | 487.03 |
| 15.84 | 464.93 |
| 14.84 | 466.0 |
| 12.25 | 469.52 |
| 27.38 | 428.88 |
| 8.76 | 474.3 |
| 15.54 | 461.06 |
| 18.71 | 465.57 |
| 13.06 | 467.67 |
| 12.72 | 466.99 |
| 19.83 | 463.72 |
| 27.23 | 443.78 |
| 24.27 | 445.23 |
| 11.8 | 464.43 |
| 6.76 | 484.36 |
| 25.99 | 442.16 |
| 16.3 | 464.11 |
| 16.5 | 462.48 |
| 10.59 | 477.49 |
| 26.05 | 437.04 |
| 19.5 | 457.09 |
| 22.21 | 450.6 |
| 17.86 | 465.78 |
| 29.96 | 427.1 |
| 19.08 | 459.81 |
| 23.59 | 447.36 |
| 3.38 | 488.92 |
| 26.39 | 433.36 |
| 8.99 | 483.35 |
| 10.91 | 469.53 |
| 13.08 | 476.96 |
| 23.95 | 440.75 |
| 15.64 | 462.55 |
| 18.78 | 448.04 |
| 20.65 | 455.24 |
| 4.96 | 494.75 |
| 23.51 | 444.58 |
| 5.99 | 484.82 |
| 23.65 | 442.9 |
| 5.17 | 485.46 |
| 26.38 | 457.81 |
| 6.02 | 481.92 |
| 23.2 | 443.23 |
| 8.57 | 474.29 |
| 30.72 | 430.46 |
| 21.52 | 455.71 |
| 22.93 | 438.34 |
| 5.71 | 485.83 |
| 18.62 | 452.82 |
| 27.88 | 435.04 |
| 22.32 | 451.21 |
| 14.55 | 465.81 |
| 17.83 | 458.42 |
| 9.68 | 470.22 |
| 19.41 | 449.24 |
| 13.22 | 471.43 |
| 12.24 | 473.26 |
| 19.21 | 452.82 |
| 29.74 | 432.69 |
| 23.28 | 444.13 |
| 8.02 | 467.21 |
| 22.47 | 445.98 |
| 27.51 | 436.91 |
| 17.51 | 455.01 |
| 23.22 | 437.11 |
| 11.73 | 477.06 |
| 21.19 | 441.71 |
| 5.48 | 495.76 |
| 24.26 | 445.63 |
| 12.32 | 464.72 |
| 31.26 | 438.03 |
| 32.09 | 434.78 |
| 24.98 | 444.67 |
| 27.48 | 452.24 |
| 21.04 | 450.92 |
| 27.75 | 436.53 |
| 22.79 | 435.53 |
| 24.22 | 440.01 |
| 27.06 | 443.1 |
| 29.25 | 427.49 |
| 26.86 | 436.25 |
| 29.64 | 440.74 |
| 19.92 | 443.54 |
| 18.5 | 459.42 |
| 23.71 | 439.66 |
| 14.39 | 464.15 |
| 19.3 | 459.1 |
| 24.65 | 455.68 |
| 13.5 | 469.08 |
| 9.82 | 478.02 |
| 18.4 | 456.8 |
| 28.12 | 441.13 |
| 17.15 | 463.88 |
| 30.69 | 430.45 |
| 28.82 | 449.18 |
| 21.3 | 447.89 |
| 30.58 | 431.59 |
| 21.17 | 447.5 |
| 9.87 | 475.58 |
| 22.18 | 453.24 |
| 24.39 | 446.4 |
| 10.73 | 476.81 |
| 9.38 | 474.1 |
| 20.27 | 450.71 |
| 24.82 | 433.62 |
| 16.55 | 465.14 |
| 20.73 | 445.18 |
| 9.51 | 474.12 |
| 8.63 | 483.91 |
| 6.48 | 486.68 |
| 14.95 | 464.98 |
| 5.76 | 481.4 |
| 10.94 | 479.2 |
| 15.87 | 463.86 |
| 12.42 | 472.3 |
| 29.12 | 446.51 |
| 29.12 | 437.71 |
| 19.08 | 458.94 |
| 31.06 | 437.91 |
| 5.72 | 490.76 |
| 26.52 | 439.66 |
| 13.84 | 463.27 |
| 13.03 | 473.99 |
| 25.94 | 433.38 |
| 16.64 | 459.01 |
| 14.13 | 471.44 |
| 13.65 | 471.91 |
| 14.5 | 465.15 |
| 19.8 | 446.66 |
| 25.2 | 438.15 |
| 20.66 | 447.14 |
| 12.07 | 472.32 |
| 25.64 | 441.68 |
| 23.33 | 440.04 |
| 29.41 | 444.82 |
| 16.6 | 457.26 |
| 27.53 | 428.83 |
| 20.62 | 449.07 |
| 26.02 | 435.21 |
| 12.75 | 471.03 |
| 12.87 | 465.56 |
| 25.77 | 442.83 |
| 14.84 | 460.3 |
| 7.41 | 474.25 |
| 8.87 | 477.97 |
| 9.69 | 472.16 |
| 16.17 | 456.08 |
| 26.24 | 452.41 |
| 13.78 | 463.71 |
| 26.3 | 433.72 |
| 17.37 | 456.4 |
| 23.6 | 448.43 |
| 8.3 | 481.6 |
| 18.86 | 457.07 |
| 22.12 | 451.0 |
| 28.41 | 440.28 |
| 29.42 | 437.47 |
| 18.61 | 443.57 |
| 27.57 | 426.6 |
| 12.83 | 470.87 |
| 9.64 | 478.37 |
| 19.13 | 453.92 |
| 15.92 | 470.22 |
| 24.64 | 434.54 |
| 27.62 | 442.89 |
| 8.9 | 479.03 |
| 9.55 | 476.06 |
| 10.57 | 473.88 |
| 19.8 | 451.75 |
| 25.63 | 439.2 |
| 24.7 | 439.7 |
| 15.26 | 463.6 |
| 20.06 | 447.47 |
| 19.84 | 447.92 |
| 11.49 | 471.08 |
| 23.74 | 437.55 |
| 22.62 | 448.27 |
| 29.53 | 431.69 |
| 21.32 | 449.09 |
| 20.3 | 448.79 |
| 16.97 | 460.21 |
| 12.07 | 479.28 |
| 7.46 | 483.11 |
| 19.2 | 450.75 |
| 28.64 | 437.97 |
| 13.56 | 459.76 |
| 17.4 | 457.75 |
| 14.08 | 469.33 |
| 27.11 | 433.28 |
| 20.92 | 444.64 |
| 16.18 | 463.1 |
| 15.57 | 460.91 |
| 10.37 | 479.35 |
| 19.6 | 449.23 |
| 9.22 | 474.51 |
| 27.76 | 435.02 |
| 28.68 | 435.45 |
| 20.95 | 452.38 |
| 9.06 | 480.41 |
| 9.21 | 478.96 |
| 13.65 | 468.87 |
| 31.79 | 434.01 |
| 14.32 | 466.36 |
| 26.28 | 435.28 |
| 7.69 | 486.46 |
| 14.44 | 468.19 |
| 9.19 | 468.37 |
| 13.35 | 474.19 |
| 23.04 | 440.32 |
| 4.83 | 485.32 |
| 17.29 | 464.27 |
| 8.73 | 479.25 |
| 26.21 | 430.4 |
| 23.72 | 447.49 |
| 29.27 | 438.23 |
| 10.4 | 492.09 |
| 12.19 | 475.36 |
| 20.4 | 452.56 |
| 34.3 | 427.84 |
| 27.56 | 433.95 |
| 30.9 | 435.27 |
| 14.85 | 454.62 |
| 16.42 | 472.17 |
| 16.45 | 452.42 |
| 10.14 | 472.17 |
| 9.53 | 481.83 |
| 17.01 | 458.78 |
| 23.94 | 447.5 |
| 15.95 | 463.4 |
| 11.15 | 473.57 |
| 25.56 | 433.72 |
| 27.16 | 431.85 |
| 26.71 | 433.47 |
| 29.56 | 432.84 |
| 31.19 | 436.6 |
| 6.86 | 490.23 |
| 12.36 | 477.16 |
| 32.82 | 441.06 |
| 25.3 | 440.86 |
| 8.71 | 477.94 |
| 13.34 | 474.47 |
| 14.2 | 470.67 |
| 23.74 | 447.31 |
| 16.9 | 466.8 |
| 28.54 | 430.91 |
| 30.15 | 434.75 |
| 14.33 | 469.52 |
| 25.57 | 438.9 |
| 30.55 | 429.56 |
| 28.04 | 432.92 |
| 26.39 | 442.87 |
| 15.3 | 466.59 |
| 6.03 | 479.61 |
| 13.49 | 471.08 |
| 27.67 | 433.37 |
| 24.19 | 443.92 |
| 24.44 | 443.5 |
| 29.86 | 439.89 |
| 30.2 | 434.66 |
| 7.99 | 487.57 |
| 9.93 | 464.64 |
| 11.03 | 470.92 |
| 22.34 | 444.39 |
| 25.33 | 442.48 |
| 18.87 | 449.61 |
| 25.97 | 435.02 |
| 16.58 | 458.67 |
| 14.35 | 461.74 |
| 25.06 | 438.31 |
| 13.85 | 462.38 |
| 16.09 | 460.56 |
| 26.34 | 439.22 |
| 23.01 | 444.64 |
| 26.39 | 430.34 |
| 31.32 | 430.46 |
| 16.64 | 456.79 |
| 13.42 | 468.82 |
| 20.06 | 448.51 |
| 14.8 | 470.77 |
| 12.59 | 465.74 |
| 26.7 | 430.21 |
| 19.78 | 449.23 |
| 15.17 | 461.89 |
| 21.71 | 445.72 |
| 19.09 | 466.13 |
| 19.76 | 448.71 |
| 14.68 | 469.25 |
| 21.3 | 450.56 |
| 16.73 | 464.46 |
| 12.26 | 471.13 |
| 14.77 | 461.52 |
| 18.26 | 451.09 |
| 27.1 | 431.51 |
| 14.72 | 469.8 |
| 26.3 | 442.28 |
| 16.48 | 458.67 |
| 17.99 | 462.4 |
| 20.34 | 453.54 |
| 25.53 | 444.38 |
| 31.59 | 440.52 |
| 30.8 | 433.62 |
| 10.75 | 481.96 |
| 19.3 | 452.75 |
| 4.71 | 481.28 |
| 23.1 | 439.03 |
| 32.63 | 435.75 |
| 26.63 | 436.03 |
| 24.35 | 445.6 |
| 15.11 | 462.65 |
| 29.1 | 438.66 |
| 21.24 | 447.32 |
| 6.16 | 484.55 |
| 7.36 | 476.8 |
| 10.44 | 480.34 |
| 26.76 | 440.63 |
| 16.79 | 459.48 |
| 10.76 | 490.78 |
| 6.07 | 483.56 |
| 27.33 | 429.38 |
| 27.15 | 440.27 |
| 22.35 | 445.34 |
| 21.82 | 447.43 |
| 21.11 | 439.91 |
| 19.95 | 459.27 |
| 7.45 | 478.89 |
| 15.36 | 466.7 |
| 15.65 | 463.5 |
| 25.31 | 436.21 |
| 25.88 | 443.94 |
| 24.6 | 439.63 |
| 22.58 | 460.95 |
| 19.69 | 448.69 |
| 25.85 | 444.63 |
| 10.06 | 473.51 |
| 18.59 | 462.56 |
| 18.27 | 451.76 |
| 8.85 | 491.81 |
| 30.04 | 429.52 |
| 26.06 | 437.9 |
| 14.8 | 467.54 |
| 23.93 | 449.97 |
| 23.72 | 436.62 |
| 11.44 | 477.68 |
| 20.28 | 447.26 |
| 27.9 | 439.76 |
| 24.74 | 437.49 |
| 14.8 | 455.14 |
| 8.22 | 485.5 |
| 27.56 | 444.1 |
| 32.07 | 432.33 |
| 9.53 | 471.23 |
| 13.61 | 463.89 |
| 22.2 | 445.54 |
| 21.36 | 446.09 |
| 23.25 | 445.12 |
| 23.5 | 443.31 |
| 8.46 | 484.16 |
| 8.19 | 477.76 |
| 30.67 | 430.28 |
| 32.48 | 446.48 |
| 8.99 | 481.03 |
| 13.77 | 466.07 |
| 19.05 | 447.47 |
| 21.19 | 455.93 |
| 10.12 | 479.62 |
| 24.93 | 455.06 |
| 8.47 | 475.06 |
| 24.52 | 438.89 |
| 28.55 | 432.7 |
| 20.58 | 452.6 |
| 18.31 | 451.75 |
| 27.18 | 430.66 |
| 4.43 | 491.9 |
| 26.02 | 439.82 |
| 15.75 | 460.73 |
| 22.99 | 449.7 |
| 25.52 | 439.42 |
| 27.04 | 439.84 |
| 6.42 | 485.86 |
| 17.04 | 458.1 |
| 10.79 | 479.92 |
| 20.41 | 458.29 |
| 7.36 | 489.45 |
| 28.08 | 434.0 |
| 24.74 | 431.24 |
| 28.32 | 439.5 |
| 16.71 | 467.46 |
| 30.7 | 429.27 |
| 18.42 | 452.1 |
| 10.62 | 472.41 |
| 22.18 | 442.14 |
| 22.38 | 441.0 |
| 13.94 | 463.07 |
| 21.24 | 445.71 |
| 6.76 | 483.16 |
| 26.73 | 440.45 |
| 7.24 | 481.83 |
| 10.84 | 467.6 |
| 19.32 | 450.88 |
| 29.0 | 425.5 |
| 23.38 | 451.87 |
| 31.17 | 428.94 |
| 26.17 | 439.86 |
| 30.9 | 433.44 |
| 24.92 | 438.23 |
| 32.77 | 436.95 |
| 14.37 | 470.19 |
| 8.36 | 484.66 |
| 31.45 | 430.81 |
| 31.6 | 433.37 |
| 17.9 | 453.02 |
| 20.35 | 453.5 |
| 16.21 | 463.09 |
| 19.36 | 464.56 |
| 21.04 | 452.12 |
| 14.05 | 470.9 |
| 23.48 | 450.89 |
| 21.91 | 445.04 |
| 24.42 | 444.72 |
| 14.26 | 460.38 |
| 21.38 | 446.8 |
| 15.71 | 465.05 |
| 5.78 | 484.13 |
| 6.77 | 488.27 |
| 23.84 | 447.09 |
| 21.17 | 452.02 |
| 19.94 | 455.55 |
| 8.73 | 480.99 |
| 16.39 | 467.68 |
| ExhaustVaccum | Power |
|---|---|
| 41.76 | 463.26 |
| 62.96 | 444.37 |
| 39.4 | 488.56 |
| 57.32 | 446.48 |
| 37.5 | 473.9 |
| 59.44 | 443.67 |
| 43.96 | 467.35 |
| 44.71 | 478.42 |
| 45.0 | 475.98 |
| 43.56 | 477.5 |
| 43.72 | 453.02 |
| 46.93 | 453.99 |
| 73.5 | 440.29 |
| 58.59 | 451.28 |
| 69.34 | 433.99 |
| 43.79 | 462.19 |
| 45.0 | 467.54 |
| 41.74 | 477.2 |
| 52.75 | 459.85 |
| 38.47 | 464.3 |
| 42.42 | 468.27 |
| 40.07 | 495.24 |
| 42.28 | 483.8 |
| 63.9 | 443.61 |
| 48.6 | 436.06 |
| 70.72 | 443.25 |
| 39.31 | 464.16 |
| 39.96 | 475.52 |
| 35.79 | 484.41 |
| 65.18 | 437.89 |
| 63.94 | 445.11 |
| 58.41 | 438.86 |
| 66.85 | 440.98 |
| 74.16 | 436.65 |
| 63.94 | 444.26 |
| 44.03 | 465.86 |
| 63.73 | 444.37 |
| 47.45 | 450.69 |
| 39.35 | 469.02 |
| 51.3 | 448.86 |
| 47.45 | 447.14 |
| 44.85 | 469.18 |
| 41.54 | 482.8 |
| 42.86 | 476.7 |
| 40.64 | 474.99 |
| 63.94 | 444.22 |
| 37.87 | 461.33 |
| 43.43 | 448.06 |
| 44.71 | 474.6 |
| 40.11 | 473.05 |
| 73.5 | 432.06 |
| 38.62 | 467.41 |
| 78.92 | 430.12 |
| 42.18 | 473.62 |
| 39.39 | 471.81 |
| 59.43 | 442.99 |
| 64.44 | 442.77 |
| 39.33 | 491.49 |
| 61.08 | 447.46 |
| 58.84 | 446.11 |
| 52.3 | 442.44 |
| 65.71 | 446.22 |
| 40.1 | 471.49 |
| 45.87 | 463.5 |
| 58.79 | 440.01 |
| 65.34 | 441.03 |
| 62.96 | 452.68 |
| 40.69 | 474.91 |
| 34.03 | 478.77 |
| 74.34 | 434.2 |
| 68.3 | 437.91 |
| 41.01 | 477.61 |
| 74.67 | 431.65 |
| 74.34 | 430.57 |
| 42.28 | 481.09 |
| 61.02 | 445.56 |
| 39.85 | 475.74 |
| 69.75 | 435.12 |
| 67.25 | 446.15 |
| 76.86 | 436.64 |
| 69.45 | 436.69 |
| 42.18 | 468.75 |
| 43.02 | 466.6 |
| 45.08 | 465.48 |
| 73.68 | 441.34 |
| 69.45 | 441.83 |
| 39.3 | 464.7 |
| 69.75 | 437.99 |
| 58.96 | 459.12 |
| 68.94 | 429.69 |
| 51.43 | 459.8 |
| 64.05 | 433.63 |
| 60.95 | 442.84 |
| 41.66 | 485.13 |
| 52.72 | 459.12 |
| 67.32 | 445.31 |
| 40.72 | 480.8 |
| 66.48 | 432.55 |
| 63.77 | 443.86 |
| 59.21 | 449.77 |
| 43.69 | 470.71 |
| 51.3 | 452.17 |
| 41.38 | 478.29 |
| 71.94 | 428.54 |
| 40.64 | 478.27 |
| 62.66 | 439.58 |
| 49.69 | 457.32 |
| 44.2 | 475.51 |
| 65.59 | 439.66 |
| 40.89 | 471.99 |
| 39.35 | 479.81 |
| 78.92 | 434.78 |
| 61.87 | 446.58 |
| 58.33 | 437.76 |
| 39.72 | 459.36 |
| 44.71 | 462.28 |
| 43.48 | 464.33 |
| 46.21 | 444.36 |
| 59.39 | 438.64 |
| 34.03 | 470.49 |
| 41.1 | 455.13 |
| 66.93 | 450.22 |
| 63.73 | 440.43 |
| 42.85 | 482.98 |
| 50.88 | 460.44 |
| 54.2 | 444.97 |
| 68.67 | 433.94 |
| 73.18 | 439.73 |
| 73.88 | 434.48 |
| 60.84 | 442.33 |
| 45.09 | 457.67 |
| 57.76 | 454.66 |
| 67.25 | 432.21 |
| 43.77 | 457.66 |
| 63.76 | 435.21 |
| 47.43 | 448.22 |
| 41.66 | 475.51 |
| 62.91 | 446.53 |
| 57.32 | 441.3 |
| 69.34 | 433.54 |
| 39.04 | 472.52 |
| 44.78 | 474.77 |
| 69.05 | 435.1 |
| 59.8 | 450.74 |
| 65.06 | 442.7 |
| 68.14 | 426.56 |
| 42.32 | 463.71 |
| 66.93 | 447.06 |
| 57.76 | 452.27 |
| 63.94 | 445.78 |
| 68.3 | 438.65 |
| 40.77 | 480.15 |
| 62.52 | 447.19 |
| 48.41 | 443.04 |
| 39.9 | 488.81 |
| 57.76 | 455.75 |
| 49.39 | 455.86 |
| 46.97 | 457.68 |
| 40.05 | 479.11 |
| 74.16 | 432.84 |
| 62.52 | 448.37 |
| 47.45 | 447.06 |
| 49.21 | 443.53 |
| 61.45 | 445.21 |
| 66.51 | 441.7 |
| 66.86 | 450.93 |
| 49.78 | 451.44 |
| 56.89 | 441.29 |
| 44.85 | 458.85 |
| 43.65 | 481.46 |
| 43.41 | 467.19 |
| 41.58 | 461.54 |
| 52.84 | 439.08 |
| 42.74 | 467.22 |
| 44.34 | 468.8 |
| 71.98 | 426.93 |
| 37.73 | 474.65 |
| 44.21 | 468.97 |
| 71.58 | 433.97 |
| 50.16 | 450.53 |
| 59.04 | 444.51 |
| 40.69 | 469.03 |
| 71.14 | 466.56 |
| 52.05 | 457.57 |
| 59.54 | 440.13 |
| 69.84 | 433.24 |
| 43.72 | 452.55 |
| 71.37 | 443.29 |
| 74.99 | 431.76 |
| 44.78 | 454.97 |
| 63.07 | 456.7 |
| 39.9 | 486.03 |
| 39.96 | 472.79 |
| 57.25 | 452.03 |
| 54.2 | 443.41 |
| 67.32 | 441.93 |
| 70.98 | 432.64 |
| 36.24 | 480.25 |
| 39.63 | 466.68 |
| 40.07 | 494.39 |
| 54.42 | 454.72 |
| 56.85 | 448.71 |
| 42.48 | 469.76 |
| 44.89 | 450.71 |
| 58.79 | 444.01 |
| 58.2 | 453.2 |
| 57.85 | 450.87 |
| 63.86 | 441.73 |
| 39.52 | 465.09 |
| 64.63 | 447.28 |
| 39.33 | 491.16 |
| 62.52 | 450.98 |
| 63.56 | 446.3 |
| 79.74 | 436.48 |
| 42.03 | 460.84 |
| 69.51 | 442.56 |
| 41.49 | 467.3 |
| 40.64 | 479.13 |
| 52.3 | 441.15 |
| 59.04 | 445.52 |
| 40.69 | 475.4 |
| 40.69 | 469.3 |
| 41.62 | 463.57 |
| 68.67 | 445.32 |
| 44.21 | 461.03 |
| 43.13 | 466.74 |
| 59.87 | 444.04 |
| 67.25 | 434.01 |
| 44.9 | 465.23 |
| 69.34 | 440.6 |
| 44.84 | 466.74 |
| 75.6 | 433.48 |
| 40.96 | 473.59 |
| 41.74 | 474.81 |
| 44.06 | 454.75 |
| 49.69 | 452.94 |
| 69.13 | 435.83 |
| 39.22 | 482.19 |
| 44.47 | 466.66 |
| 40.12 | 462.59 |
| 64.63 | 447.82 |
| 41.62 | 462.73 |
| 63.21 | 447.98 |
| 39.31 | 462.72 |
| 58.46 | 442.42 |
| 48.41 | 444.69 |
| 39.0 | 466.7 |
| 64.63 | 453.84 |
| 67.32 | 436.92 |
| 36.08 | 486.37 |
| 60.07 | 440.43 |
| 63.07 | 446.82 |
| 39.48 | 484.91 |
| 71.64 | 437.76 |
| 68.08 | 438.91 |
| 39.54 | 464.19 |
| 67.79 | 442.19 |
| 47.43 | 446.86 |
| 44.2 | 457.15 |
| 43.65 | 482.57 |
| 41.26 | 476.03 |
| 72.58 | 428.89 |
| 40.23 | 472.7 |
| 52.3 | 445.6 |
| 52.05 | 464.78 |
| 58.95 | 440.42 |
| 70.79 | 428.41 |
| 70.47 | 438.5 |
| 59.43 | 438.28 |
| 40.64 | 476.29 |
| 58.49 | 448.46 |
| 57.19 | 438.99 |
| 39.22 | 471.8 |
| 42.44 | 471.81 |
| 59.14 | 449.82 |
| 68.08 | 442.14 |
| 58.79 | 441.46 |
| 40.22 | 477.62 |
| 61.87 | 446.76 |
| 39.82 | 472.52 |
| 40.71 | 471.58 |
| 72.39 | 440.85 |
| 77.54 | 431.37 |
| 71.97 | 437.33 |
| 40.71 | 469.22 |
| 40.78 | 471.11 |
| 66.51 | 439.17 |
| 62.26 | 445.33 |
| 39.22 | 473.71 |
| 57.76 | 452.66 |
| 59.43 | 440.99 |
| 38.91 | 467.42 |
| 58.82 | 444.14 |
| 56.53 | 457.17 |
| 54.3 | 467.87 |
| 70.72 | 442.04 |
| 44.58 | 471.36 |
| 39.1 | 460.7 |
| 70.83 | 431.33 |
| 76.86 | 432.6 |
| 59.39 | 447.61 |
| 64.79 | 443.87 |
| 52.3 | 446.87 |
| 41.01 | 465.74 |
| 56.89 | 447.86 |
| 62.96 | 447.65 |
| 63.47 | 437.87 |
| 41.66 | 483.51 |
| 41.46 | 479.65 |
| 58.16 | 455.16 |
| 71.25 | 431.91 |
| 41.5 | 470.68 |
| 69.13 | 429.28 |
| 59.21 | 450.81 |
| 73.5 | 437.73 |
| 44.6 | 460.21 |
| 69.05 | 442.86 |
| 45.0 | 482.99 |
| 65.27 | 440.0 |
| 41.82 | 478.48 |
| 49.82 | 455.28 |
| 66.56 | 436.94 |
| 37.87 | 461.06 |
| 69.45 | 438.28 |
| 39.58 | 472.61 |
| 70.79 | 426.85 |
| 39.72 | 470.18 |
| 54.42 | 455.38 |
| 66.56 | 428.32 |
| 42.49 | 480.35 |
| 56.53 | 455.56 |
| 58.33 | 447.66 |
| 68.67 | 443.06 |
| 58.86 | 452.43 |
| 41.54 | 477.81 |
| 73.77 | 431.66 |
| 73.91 | 431.8 |
| 68.67 | 446.67 |
| 60.29 | 445.26 |
| 69.89 | 425.72 |
| 72.29 | 430.58 |
| 60.27 | 439.86 |
| 59.15 | 441.11 |
| 71.14 | 434.72 |
| 67.45 | 434.01 |
| 41.17 | 475.64 |
| 41.58 | 460.44 |
| 68.67 | 436.4 |
| 40.73 | 461.03 |
| 41.54 | 479.08 |
| 69.14 | 435.76 |
| 45.51 | 460.14 |
| 75.6 | 442.2 |
| 50.78 | 447.69 |
| 73.67 | 431.15 |
| 63.56 | 445.0 |
| 73.21 | 431.59 |
| 44.47 | 467.22 |
| 51.43 | 445.33 |
| 45.09 | 470.57 |
| 39.61 | 473.77 |
| 60.29 | 447.67 |
| 40.05 | 474.29 |
| 71.32 | 437.14 |
| 69.05 | 432.56 |
| 41.79 | 459.14 |
| 60.07 | 446.19 |
| 71.77 | 428.1 |
| 44.47 | 468.46 |
| 66.75 | 435.02 |
| 48.6 | 445.52 |
| 40.55 | 462.69 |
| 61.27 | 455.75 |
| 40.0 | 463.74 |
| 69.68 | 439.79 |
| 58.95 | 443.26 |
| 69.13 | 432.04 |
| 43.96 | 465.86 |
| 45.87 | 465.6 |
| 25.36 | 469.43 |
| 49.3 | 440.75 |
| 38.91 | 481.32 |
| 40.56 | 479.87 |
| 39.16 | 458.59 |
| 71.97 | 438.62 |
| 59.44 | 445.59 |
| 43.56 | 481.87 |
| 41.5 | 475.01 |
| 48.41 | 436.54 |
| 40.66 | 456.63 |
| 62.52 | 451.69 |
| 39.59 | 463.04 |
| 67.71 | 446.1 |
| 59.92 | 438.67 |
| 41.4 | 466.88 |
| 48.41 | 444.6 |
| 59.39 | 440.26 |
| 40.96 | 483.92 |
| 41.2 | 475.19 |
| 44.68 | 479.24 |
| 73.68 | 434.92 |
| 65.46 | 454.16 |
| 58.79 | 447.58 |
| 41.26 | 467.9 |
| 69.13 | 426.29 |
| 45.87 | 447.02 |
| 41.79 | 455.85 |
| 40.22 | 476.46 |
| 68.94 | 437.48 |
| 49.21 | 452.77 |
| 39.33 | 491.54 |
| 64.79 | 438.41 |
| 41.93 | 476.1 |
| 36.99 | 464.58 |
| 40.78 | 467.74 |
| 57.17 | 442.12 |
| 57.5 | 453.34 |
| 69.13 | 425.29 |
| 51.43 | 449.63 |
| 45.78 | 462.88 |
| 42.32 | 464.67 |
| 35.57 | 489.96 |
| 38.08 | 482.38 |
| 77.95 | 437.95 |
| 71.98 | 429.2 |
| 46.63 | 453.34 |
| 70.02 | 442.47 |
| 49.69 | 462.6 |
| 40.96 | 478.79 |
| 52.05 | 456.11 |
| 50.16 | 450.33 |
| 69.88 | 434.83 |
| 73.68 | 433.43 |
| 62.96 | 456.02 |
| 40.67 | 485.23 |
| 37.14 | 473.57 |
| 39.58 | 469.94 |
| 49.83 | 452.07 |
| 41.04 | 475.32 |
| 36.24 | 480.69 |
| 48.06 | 444.01 |
| 56.03 | 465.17 |
| 39.82 | 480.61 |
| 41.5 | 476.04 |
| 49.3 | 441.76 |
| 71.98 | 428.24 |
| 67.71 | 444.77 |
| 45.87 | 463.1 |
| 44.6 | 470.5 |
| 72.99 | 431.0 |
| 69.4 | 430.68 |
| 67.17 | 436.42 |
| 49.82 | 452.33 |
| 63.94 | 440.16 |
| 63.76 | 435.75 |
| 44.57 | 449.74 |
| 72.24 | 430.73 |
| 77.17 | 432.75 |
| 47.43 | 446.79 |
| 41.39 | 486.35 |
| 59.8 | 453.18 |
| 50.23 | 458.31 |
| 41.54 | 480.26 |
| 51.3 | 448.65 |
| 49.5 | 458.41 |
| 64.69 | 435.39 |
| 50.16 | 450.21 |
| 43.14 | 459.59 |
| 75.6 | 445.84 |
| 48.78 | 441.08 |
| 37.85 | 467.33 |
| 63.09 | 444.19 |
| 68.27 | 432.96 |
| 47.93 | 438.09 |
| 36.99 | 467.9 |
| 43.67 | 475.72 |
| 34.69 | 477.51 |
| 69.84 | 435.13 |
| 39.61 | 477.9 |
| 44.2 | 457.26 |
| 44.6 | 467.53 |
| 41.58 | 465.15 |
| 40.1 | 474.28 |
| 65.71 | 444.49 |
| 45.09 | 452.84 |
| 77.54 | 435.38 |
| 75.33 | 433.57 |
| 69.71 | 435.27 |
| 39.63 | 468.49 |
| 70.8 | 433.07 |
| 73.56 | 430.63 |
| 65.74 | 440.74 |
| 39.96 | 474.49 |
| 48.6 | 449.74 |
| 63.76 | 436.73 |
| 70.79 | 434.58 |
| 43.13 | 473.93 |
| 79.74 | 435.99 |
| 43.22 | 466.83 |
| 68.24 | 427.22 |
| 63.77 | 444.07 |
| 41.49 | 469.57 |
| 66.51 | 459.89 |
| 40.71 | 479.59 |
| 64.69 | 440.92 |
| 41.46 | 480.87 |
| 66.54 | 441.9 |
| 69.68 | 430.2 |
| 42.86 | 465.16 |
| 44.45 | 471.32 |
| 40.64 | 485.43 |
| 40.07 | 495.35 |
| 58.49 | 449.12 |
| 42.02 | 480.53 |
| 50.66 | 457.07 |
| 69.94 | 443.67 |
| 44.68 | 477.52 |
| 39.64 | 472.95 |
| 39.69 | 472.54 |
| 40.71 | 469.17 |
| 70.04 | 435.21 |
| 40.22 | 477.78 |
| 39.85 | 475.89 |
| 40.23 | 483.9 |
| 39.16 | 476.2 |
| 43.34 | 462.16 |
| 37.85 | 471.05 |
| 41.26 | 484.71 |
| 63.86 | 446.34 |
| 42.28 | 469.02 |
| 72.86 | 432.12 |
| 40.1 | 467.28 |
| 69.98 | 429.66 |
| 45.09 | 469.49 |
| 40.56 | 485.87 |
| 40.81 | 481.95 |
| 40.02 | 479.03 |
| 69.13 | 434.5 |
| 54.3 | 464.9 |
| 63.94 | 452.71 |
| 69.51 | 429.74 |
| 56.65 | 457.09 |
| 72.29 | 446.77 |
| 41.48 | 460.76 |
| 40.83 | 471.95 |
| 46.21 | 453.29 |
| 60.07 | 441.61 |
| 46.18 | 464.73 |
| 43.69 | 464.68 |
| 73.91 | 430.59 |
| 77.95 | 438.01 |
| 41.04 | 479.08 |
| 74.78 | 436.39 |
| 65.46 | 447.07 |
| 39.58 | 479.91 |
| 43.02 | 489.05 |
| 53.82 | 463.17 |
| 42.02 | 471.26 |
| 40.67 | 480.49 |
| 39.42 | 473.78 |
| 58.16 | 455.5 |
| 58.41 | 446.27 |
| 41.06 | 482.2 |
| 59.8 | 452.48 |
| 43.14 | 464.48 |
| 69.89 | 438.1 |
| 63.47 | 445.6 |
| 67.79 | 442.43 |
| 61.25 | 436.67 |
| 41.85 | 466.56 |
| 71.14 | 457.29 |
| 36.25 | 487.03 |
| 52.72 | 464.93 |
| 44.63 | 466.0 |
| 48.79 | 469.52 |
| 70.04 | 428.88 |
| 41.48 | 474.3 |
| 39.31 | 461.06 |
| 39.39 | 465.57 |
| 41.78 | 467.67 |
| 40.71 | 466.99 |
| 39.39 | 463.72 |
| 49.16 | 443.78 |
| 68.28 | 445.23 |
| 40.66 | 464.43 |
| 36.25 | 484.36 |
| 63.07 | 442.16 |
| 39.63 | 464.11 |
| 49.39 | 462.48 |
| 42.49 | 477.49 |
| 65.59 | 437.04 |
| 40.79 | 457.09 |
| 45.01 | 450.6 |
| 45.0 | 465.78 |
| 70.04 | 427.1 |
| 44.63 | 459.81 |
| 47.43 | 447.36 |
| 39.64 | 488.92 |
| 66.49 | 433.36 |
| 39.04 | 483.35 |
| 41.04 | 469.53 |
| 39.82 | 476.96 |
| 58.46 | 440.75 |
| 43.71 | 462.55 |
| 54.2 | 448.04 |
| 50.59 | 455.24 |
| 40.07 | 494.75 |
| 57.32 | 444.58 |
| 35.79 | 484.82 |
| 66.05 | 442.9 |
| 39.33 | 485.46 |
| 49.5 | 457.81 |
| 43.65 | 481.92 |
| 61.02 | 443.23 |
| 39.69 | 474.29 |
| 71.58 | 430.46 |
| 50.66 | 455.71 |
| 62.26 | 438.34 |
| 41.31 | 485.83 |
| 44.06 | 452.82 |
| 68.94 | 435.04 |
| 59.8 | 451.21 |
| 42.74 | 465.81 |
| 44.92 | 458.42 |
| 39.96 | 470.22 |
| 49.39 | 449.24 |
| 44.92 | 471.43 |
| 44.92 | 473.26 |
| 58.49 | 452.82 |
| 70.32 | 432.69 |
| 60.84 | 444.13 |
| 41.92 | 467.21 |
| 48.6 | 445.98 |
| 73.77 | 436.91 |
| 44.9 | 455.01 |
| 66.56 | 437.11 |
| 40.64 | 477.06 |
| 67.71 | 441.71 |
| 40.07 | 495.76 |
| 66.44 | 445.63 |
| 41.62 | 464.72 |
| 68.94 | 438.03 |
| 72.86 | 434.78 |
| 60.32 | 444.67 |
| 61.41 | 452.24 |
| 45.09 | 450.92 |
| 70.4 | 436.53 |
| 71.77 | 435.53 |
| 68.51 | 440.01 |
| 64.45 | 443.1 |
| 71.94 | 427.49 |
| 68.08 | 436.25 |
| 67.79 | 440.74 |
| 63.31 | 443.54 |
| 51.43 | 459.42 |
| 60.23 | 439.66 |
| 44.84 | 464.15 |
| 56.65 | 459.1 |
| 52.36 | 455.68 |
| 45.51 | 469.08 |
| 41.26 | 478.02 |
| 44.06 | 456.8 |
| 44.89 | 441.13 |
| 43.69 | 463.88 |
| 73.67 | 430.45 |
| 65.71 | 449.18 |
| 48.92 | 447.89 |
| 70.04 | 431.59 |
| 52.3 | 447.5 |
| 41.82 | 475.58 |
| 59.8 | 453.24 |
| 63.21 | 446.4 |
| 44.92 | 476.81 |
| 40.46 | 474.1 |
| 57.76 | 450.71 |
| 66.48 | 433.62 |
| 41.66 | 465.14 |
| 59.87 | 445.18 |
| 39.22 | 474.12 |
| 43.79 | 483.91 |
| 40.27 | 486.68 |
| 43.52 | 464.98 |
| 45.87 | 481.4 |
| 39.04 | 479.2 |
| 41.16 | 463.86 |
| 38.25 | 472.3 |
| 58.84 | 446.51 |
| 51.43 | 437.71 |
| 41.1 | 458.94 |
| 67.17 | 437.91 |
| 39.33 | 490.76 |
| 65.06 | 439.66 |
| 44.9 | 463.27 |
| 39.52 | 473.99 |
| 66.49 | 433.38 |
| 53.82 | 459.01 |
| 40.75 | 471.44 |
| 39.28 | 471.91 |
| 44.47 | 465.15 |
| 51.19 | 446.66 |
| 63.76 | 438.15 |
| 51.19 | 447.14 |
| 43.71 | 472.32 |
| 70.72 | 441.68 |
| 72.99 | 440.04 |
| 64.05 | 444.82 |
| 53.16 | 457.26 |
| 72.58 | 428.83 |
| 43.43 | 449.07 |
| 71.94 | 435.21 |
| 44.2 | 471.03 |
| 48.04 | 465.56 |
| 62.96 | 442.83 |
| 41.48 | 460.3 |
| 40.71 | 474.25 |
| 41.82 | 477.97 |
| 40.46 | 472.16 |
| 46.97 | 456.08 |
| 49.82 | 452.41 |
| 43.22 | 463.71 |
| 67.07 | 433.72 |
| 57.76 | 456.4 |
| 48.98 | 448.43 |
| 36.08 | 481.6 |
| 42.18 | 457.07 |
| 49.39 | 451.0 |
| 75.6 | 440.28 |
| 71.32 | 437.47 |
| 67.71 | 443.57 |
| 69.84 | 426.6 |
| 41.5 | 470.87 |
| 39.85 | 478.37 |
| 58.66 | 453.92 |
| 40.56 | 470.22 |
| 72.24 | 434.54 |
| 63.9 | 442.89 |
| 36.24 | 479.03 |
| 43.99 | 476.06 |
| 36.71 | 473.88 |
| 57.25 | 451.75 |
| 56.85 | 439.2 |
| 58.46 | 439.7 |
| 46.18 | 463.6 |
| 52.84 | 447.47 |
| 56.89 | 447.92 |
| 44.63 | 471.08 |
| 72.43 | 437.55 |
| 51.3 | 448.27 |
| 72.39 | 431.69 |
| 48.14 | 449.09 |
| 58.46 | 448.79 |
| 44.92 | 460.21 |
| 41.17 | 479.28 |
| 41.82 | 483.11 |
| 54.2 | 450.75 |
| 66.54 | 437.97 |
| 41.48 | 459.76 |
| 44.9 | 457.75 |
| 40.1 | 469.33 |
| 69.75 | 433.28 |
| 70.02 | 444.64 |
| 44.9 | 463.1 |
| 44.68 | 460.91 |
| 39.04 | 479.35 |
| 59.21 | 449.23 |
| 40.92 | 474.51 |
| 72.99 | 435.02 |
| 70.72 | 435.45 |
| 48.14 | 452.38 |
| 39.3 | 480.41 |
| 39.72 | 478.96 |
| 42.74 | 468.87 |
| 76.2 | 434.01 |
| 44.6 | 466.36 |
| 75.23 | 435.28 |
| 43.02 | 486.46 |
| 40.1 | 468.19 |
| 41.01 | 468.37 |
| 41.39 | 474.19 |
| 74.22 | 440.32 |
| 38.44 | 485.32 |
| 42.86 | 464.27 |
| 36.18 | 479.25 |
| 70.32 | 430.4 |
| 58.62 | 447.49 |
| 64.69 | 438.23 |
| 40.43 | 492.09 |
| 40.75 | 475.36 |
| 54.9 | 452.56 |
| 74.67 | 427.84 |
| 68.08 | 433.95 |
| 70.8 | 435.27 |
| 58.59 | 454.62 |
| 40.56 | 472.17 |
| 63.31 | 452.42 |
| 42.02 | 472.17 |
| 41.44 | 481.83 |
| 49.15 | 458.78 |
| 62.08 | 447.5 |
| 49.25 | 463.4 |
| 41.26 | 473.57 |
| 70.32 | 433.72 |
| 66.44 | 431.85 |
| 77.95 | 433.47 |
| 74.22 | 432.84 |
| 70.94 | 436.6 |
| 41.17 | 490.23 |
| 41.74 | 477.16 |
| 68.31 | 441.06 |
| 70.98 | 440.86 |
| 41.82 | 477.94 |
| 40.8 | 474.47 |
| 43.02 | 470.67 |
| 65.34 | 447.31 |
| 44.88 | 466.8 |
| 71.94 | 430.91 |
| 69.88 | 434.75 |
| 42.86 | 469.52 |
| 59.43 | 438.9 |
| 70.04 | 429.56 |
| 74.33 | 432.92 |
| 49.16 | 442.87 |
| 41.76 | 466.59 |
| 41.14 | 479.61 |
| 44.63 | 471.08 |
| 59.14 | 433.37 |
| 65.48 | 443.92 |
| 59.14 | 443.5 |
| 64.79 | 439.89 |
| 69.59 | 434.66 |
| 41.38 | 487.57 |
| 41.62 | 464.64 |
| 42.32 | 470.92 |
| 63.73 | 444.39 |
| 48.6 | 442.48 |
| 52.08 | 449.61 |
| 69.34 | 435.02 |
| 43.99 | 458.67 |
| 46.18 | 461.74 |
| 62.39 | 438.31 |
| 48.92 | 462.38 |
| 44.2 | 460.56 |
| 59.21 | 439.22 |
| 58.79 | 444.64 |
| 71.25 | 430.34 |
| 71.29 | 430.46 |
| 45.87 | 456.79 |
| 41.23 | 468.82 |
| 44.9 | 448.51 |
| 44.71 | 470.77 |
| 41.14 | 465.74 |
| 66.56 | 430.21 |
| 50.32 | 449.23 |
| 49.15 | 461.89 |
| 61.45 | 445.72 |
| 39.39 | 466.13 |
| 51.19 | 448.71 |
| 41.23 | 469.25 |
| 66.86 | 450.56 |
| 39.64 | 464.46 |
| 41.5 | 471.13 |
| 48.06 | 461.52 |
| 59.15 | 451.09 |
| 79.74 | 431.51 |
| 40.83 | 469.8 |
| 51.43 | 442.28 |
| 48.92 | 458.67 |
| 43.79 | 462.4 |
| 59.8 | 453.54 |
| 62.96 | 444.38 |
| 58.9 | 440.52 |
| 69.14 | 433.62 |
| 45.0 | 481.96 |
| 44.9 | 452.75 |
| 39.42 | 481.28 |
| 66.05 | 439.03 |
| 73.88 | 435.75 |
| 74.16 | 436.03 |
| 58.49 | 445.6 |
| 56.03 | 462.65 |
| 50.05 | 438.66 |
| 50.32 | 447.32 |
| 39.48 | 484.55 |
| 41.01 | 476.8 |
| 39.04 | 480.34 |
| 48.41 | 440.63 |
| 44.6 | 459.48 |
| 40.43 | 490.78 |
| 38.91 | 483.56 |
| 73.18 | 429.38 |
| 59.21 | 440.27 |
| 51.43 | 445.34 |
| 65.27 | 447.43 |
| 69.94 | 439.91 |
| 50.59 | 459.27 |
| 39.61 | 478.89 |
| 41.66 | 466.7 |
| 43.5 | 463.5 |
| 74.33 | 436.21 |
| 63.47 | 443.94 |
| 63.94 | 439.63 |
| 41.54 | 460.95 |
| 59.14 | 448.69 |
| 75.08 | 444.63 |
| 37.83 | 473.51 |
| 39.54 | 462.56 |
| 50.16 | 451.76 |
| 40.43 | 491.81 |
| 68.08 | 429.52 |
| 49.02 | 437.9 |
| 38.73 | 467.54 |
| 64.45 | 449.97 |
| 66.48 | 436.62 |
| 40.55 | 477.68 |
| 63.86 | 447.26 |
| 63.13 | 439.76 |
| 59.39 | 437.49 |
| 58.2 | 455.14 |
| 41.03 | 485.5 |
| 66.93 | 444.1 |
| 70.94 | 432.33 |
| 44.03 | 471.23 |
| 42.34 | 463.89 |
| 51.19 | 445.54 |
| 59.54 | 446.09 |
| 63.86 | 445.12 |
| 59.21 | 443.31 |
| 39.66 | 484.16 |
| 40.69 | 477.76 |
| 71.29 | 430.28 |
| 62.04 | 446.48 |
| 36.66 | 481.03 |
| 47.83 | 466.07 |
| 67.32 | 447.47 |
| 55.5 | 455.93 |
| 40.0 | 479.62 |
| 47.01 | 455.06 |
| 40.46 | 475.06 |
| 56.85 | 438.89 |
| 69.84 | 432.7 |
| 50.9 | 452.6 |
| 46.21 | 451.75 |
| 71.06 | 430.66 |
| 38.91 | 491.9 |
| 74.78 | 439.82 |
| 39.0 | 460.73 |
| 60.95 | 449.7 |
| 59.15 | 439.42 |
| 65.06 | 439.84 |
| 35.57 | 485.86 |
| 40.12 | 458.1 |
| 39.82 | 479.92 |
| 56.03 | 458.29 |
| 40.07 | 489.45 |
| 73.42 | 434.0 |
| 69.13 | 431.24 |
| 47.93 | 439.5 |
| 40.56 | 467.46 |
| 71.58 | 429.27 |
| 58.95 | 452.1 |
| 42.02 | 472.41 |
| 69.05 | 442.14 |
| 49.3 | 441.0 |
| 41.58 | 463.07 |
| 60.84 | 445.71 |
| 39.81 | 483.16 |
| 68.84 | 440.45 |
| 38.06 | 481.83 |
| 40.62 | 467.6 |
| 52.84 | 450.88 |
| 69.13 | 425.5 |
| 54.42 | 451.87 |
| 69.51 | 428.94 |
| 48.6 | 439.86 |
| 73.42 | 433.44 |
| 73.68 | 438.23 |
| 71.32 | 436.95 |
| 40.56 | 470.19 |
| 40.22 | 484.66 |
| 68.27 | 430.81 |
| 73.17 | 433.37 |
| 48.98 | 453.02 |
| 50.9 | 453.5 |
| 41.23 | 463.09 |
| 44.6 | 464.56 |
| 65.46 | 452.12 |
| 40.69 | 470.9 |
| 64.15 | 450.89 |
| 63.76 | 445.04 |
| 63.07 | 444.72 |
| 40.92 | 460.38 |
| 58.33 | 446.8 |
| 44.06 | 465.05 |
| 40.62 | 484.13 |
| 39.81 | 488.27 |
| 49.21 | 447.09 |
| 58.16 | 452.02 |
| 58.96 | 455.55 |
| 41.92 | 480.99 |
| 41.67 | 467.68 |
| Pressure | Power |
|---|---|
| 1024.07 | 463.26 |
| 1020.04 | 444.37 |
| 1012.16 | 488.56 |
| 1010.24 | 446.48 |
| 1009.23 | 473.9 |
| 1012.23 | 443.67 |
| 1014.02 | 467.35 |
| 1019.12 | 478.42 |
| 1021.78 | 475.98 |
| 1015.14 | 477.5 |
| 1008.64 | 453.02 |
| 1014.66 | 453.99 |
| 1011.31 | 440.29 |
| 1012.77 | 451.28 |
| 1009.48 | 433.99 |
| 1015.76 | 462.19 |
| 1022.86 | 467.54 |
| 1022.6 | 477.2 |
| 1023.97 | 459.85 |
| 1015.15 | 464.3 |
| 1009.09 | 468.27 |
| 1019.16 | 495.24 |
| 1008.52 | 483.8 |
| 1014.3 | 443.61 |
| 1003.18 | 436.06 |
| 1009.97 | 443.25 |
| 1011.11 | 464.16 |
| 1023.57 | 475.52 |
| 1012.14 | 484.41 |
| 1012.69 | 437.89 |
| 1019.02 | 445.11 |
| 1013.64 | 438.86 |
| 1011.11 | 440.98 |
| 1010.08 | 436.65 |
| 1018.76 | 444.26 |
| 1007.29 | 465.86 |
| 1011.4 | 444.37 |
| 1010.08 | 450.69 |
| 1014.69 | 469.02 |
| 1012.04 | 448.86 |
| 1007.62 | 447.14 |
| 1017.24 | 469.18 |
| 1018.33 | 482.8 |
| 1014.12 | 476.7 |
| 1020.63 | 474.99 |
| 1019.28 | 444.22 |
| 1020.24 | 461.33 |
| 1010.96 | 448.06 |
| 1014.51 | 474.6 |
| 1029.14 | 473.05 |
| 1010.58 | 432.06 |
| 1018.71 | 467.41 |
| 1011.6 | 430.12 |
| 1014.82 | 473.62 |
| 1012.94 | 471.81 |
| 1010.23 | 442.99 |
| 1014.65 | 442.77 |
| 1010.18 | 491.49 |
| 1013.68 | 447.46 |
| 1002.25 | 446.11 |
| 1007.03 | 442.44 |
| 1013.61 | 446.22 |
| 1016.67 | 471.49 |
| 1008.89 | 463.5 |
| 1016.02 | 440.01 |
| 1014.56 | 441.03 |
| 1019.49 | 452.68 |
| 1020.43 | 474.91 |
| 1018.71 | 478.77 |
| 998.14 | 434.2 |
| 1017.83 | 437.91 |
| 1024.61 | 477.61 |
| 1016.65 | 431.65 |
| 998.58 | 430.57 |
| 1008.82 | 481.09 |
| 1009.56 | 445.56 |
| 1012.71 | 475.74 |
| 1010.36 | 435.12 |
| 1017.39 | 446.15 |
| 1001.31 | 436.64 |
| 1013.89 | 436.69 |
| 1016.53 | 468.75 |
| 1012.18 | 466.6 |
| 1024.42 | 465.48 |
| 1014.93 | 441.34 |
| 1012.53 | 441.83 |
| 1019.0 | 464.7 |
| 1009.6 | 437.99 |
| 1015.55 | 459.12 |
| 1006.56 | 429.69 |
| 1010.57 | 459.8 |
| 1009.81 | 433.63 |
| 1014.62 | 442.84 |
| 1014.49 | 485.13 |
| 1025.09 | 459.12 |
| 1012.05 | 445.31 |
| 1022.7 | 480.8 |
| 1005.23 | 432.55 |
| 1013.42 | 443.86 |
| 1018.3 | 449.77 |
| 1017.19 | 470.71 |
| 1011.93 | 452.17 |
| 1021.6 | 478.29 |
| 1006.96 | 428.54 |
| 1020.66 | 478.27 |
| 1007.63 | 439.58 |
| 1005.53 | 457.32 |
| 1018.79 | 475.51 |
| 1010.85 | 439.66 |
| 1011.03 | 471.99 |
| 1015.1 | 479.81 |
| 1010.83 | 434.78 |
| 1011.18 | 446.58 |
| 1013.92 | 437.76 |
| 1001.24 | 459.36 |
| 1016.99 | 462.28 |
| 1016.08 | 464.33 |
| 1010.71 | 444.36 |
| 1014.32 | 438.64 |
| 1018.69 | 470.49 |
| 1001.86 | 455.13 |
| 1017.06 | 450.22 |
| 1009.34 | 440.43 |
| 1014.02 | 482.98 |
| 1014.19 | 460.44 |
| 1012.81 | 444.97 |
| 1005.2 | 433.94 |
| 1012.28 | 439.73 |
| 1005.89 | 434.48 |
| 1017.93 | 442.33 |
| 1014.26 | 457.67 |
| 1018.8 | 454.66 |
| 1017.71 | 432.21 |
| 1012.25 | 457.66 |
| 1010.27 | 435.21 |
| 1007.64 | 448.22 |
| 1013.79 | 475.51 |
| 1013.24 | 446.53 |
| 1011.7 | 441.3 |
| 1007.67 | 433.54 |
| 1020.45 | 472.52 |
| 1012.59 | 474.77 |
| 1001.62 | 435.1 |
| 1016.92 | 450.74 |
| 1014.32 | 442.7 |
| 1003.34 | 426.56 |
| 1016.0 | 463.71 |
| 1016.85 | 447.06 |
| 1018.02 | 452.27 |
| 1018.49 | 445.78 |
| 1017.93 | 438.65 |
| 1011.55 | 480.15 |
| 1016.46 | 447.19 |
| 1008.47 | 443.04 |
| 1008.06 | 488.81 |
| 1016.26 | 455.75 |
| 1018.83 | 455.86 |
| 1013.94 | 457.68 |
| 1014.95 | 479.11 |
| 1007.44 | 432.84 |
| 1016.18 | 448.37 |
| 1007.56 | 447.06 |
| 1014.1 | 443.53 |
| 1011.13 | 445.21 |
| 1015.53 | 441.7 |
| 1013.05 | 450.93 |
| 1002.95 | 451.44 |
| 1012.32 | 441.29 |
| 1014.48 | 458.85 |
| 1018.24 | 481.46 |
| 1016.93 | 467.19 |
| 1020.5 | 461.54 |
| 1006.28 | 439.08 |
| 1028.79 | 467.22 |
| 1019.49 | 468.8 |
| 1004.66 | 426.93 |
| 1024.36 | 474.65 |
| 1022.93 | 468.97 |
| 1010.18 | 433.97 |
| 1009.52 | 450.53 |
| 1011.98 | 444.51 |
| 1015.29 | 469.03 |
| 1019.36 | 466.56 |
| 1012.15 | 457.57 |
| 1006.24 | 440.13 |
| 1003.57 | 433.24 |
| 1008.64 | 452.55 |
| 1002.04 | 443.29 |
| 1005.47 | 431.76 |
| 1007.81 | 454.97 |
| 1012.42 | 456.7 |
| 1007.75 | 486.03 |
| 1011.37 | 472.79 |
| 1010.12 | 452.03 |
| 1012.26 | 443.41 |
| 1014.49 | 441.93 |
| 1007.51 | 432.64 |
| 1013.15 | 480.25 |
| 1004.47 | 466.68 |
| 1020.19 | 494.39 |
| 1012.46 | 454.72 |
| 1012.28 | 448.71 |
| 1013.43 | 469.76 |
| 1009.64 | 450.71 |
| 1009.8 | 444.01 |
| 1017.46 | 453.2 |
| 1012.39 | 450.87 |
| 1019.67 | 441.73 |
| 1018.41 | 465.09 |
| 1020.59 | 447.28 |
| 1011.54 | 491.16 |
| 1017.99 | 450.98 |
| 1013.75 | 446.3 |
| 1008.37 | 436.48 |
| 1017.41 | 460.84 |
| 1013.43 | 442.56 |
| 1020.19 | 467.3 |
| 1021.47 | 479.13 |
| 1008.72 | 441.15 |
| 1011.78 | 445.52 |
| 1015.62 | 475.4 |
| 1014.91 | 469.3 |
| 1017.17 | 463.57 |
| 1006.71 | 445.32 |
| 1020.36 | 461.03 |
| 1014.99 | 466.74 |
| 1018.47 | 444.04 |
| 1017.6 | 434.01 |
| 1020.5 | 465.23 |
| 1009.63 | 440.6 |
| 1023.66 | 466.74 |
| 1017.43 | 433.48 |
| 1023.36 | 473.59 |
| 1020.75 | 474.81 |
| 1017.58 | 454.75 |
| 1009.6 | 452.94 |
| 1009.94 | 435.83 |
| 1014.53 | 482.19 |
| 1030.46 | 466.66 |
| 1013.03 | 462.59 |
| 1020.69 | 447.82 |
| 1014.55 | 462.73 |
| 1012.06 | 447.98 |
| 1009.15 | 462.72 |
| 1016.82 | 442.42 |
| 1008.64 | 444.69 |
| 1016.73 | 466.7 |
| 1020.38 | 453.84 |
| 1013.83 | 436.92 |
| 1021.82 | 486.37 |
| 1015.42 | 440.43 |
| 1010.94 | 446.82 |
| 1005.11 | 484.91 |
| 1008.27 | 437.76 |
| 1013.27 | 438.91 |
| 1007.97 | 464.19 |
| 1009.89 | 442.19 |
| 1008.38 | 446.86 |
| 1018.89 | 457.15 |
| 1020.14 | 482.57 |
| 1007.44 | 476.03 |
| 1008.69 | 428.89 |
| 1018.07 | 472.7 |
| 1009.04 | 445.6 |
| 1014.63 | 464.78 |
| 1017.02 | 440.42 |
| 1003.96 | 428.41 |
| 1010.65 | 438.5 |
| 1007.84 | 438.28 |
| 1022.35 | 476.29 |
| 1011.5 | 448.46 |
| 1008.62 | 438.99 |
| 1017.9 | 471.8 |
| 1012.74 | 471.81 |
| 1016.12 | 449.82 |
| 1013.13 | 442.14 |
| 1009.74 | 441.46 |
| 1011.37 | 477.62 |
| 1011.45 | 446.76 |
| 1012.46 | 472.52 |
| 1021.27 | 471.58 |
| 1001.15 | 440.85 |
| 1011.33 | 431.37 |
| 1008.64 | 437.33 |
| 1015.68 | 469.22 |
| 1023.51 | 471.11 |
| 1010.98 | 439.17 |
| 1012.11 | 445.33 |
| 1018.62 | 473.71 |
| 1016.28 | 452.66 |
| 1007.12 | 440.99 |
| 1014.48 | 467.42 |
| 1010.02 | 444.14 |
| 1020.57 | 457.17 |
| 1015.92 | 467.87 |
| 1009.78 | 442.04 |
| 1019.52 | 471.36 |
| 1009.81 | 460.7 |
| 1010.35 | 431.33 |
| 998.59 | 432.6 |
| 1014.07 | 447.61 |
| 1016.27 | 443.87 |
| 1009.2 | 446.87 |
| 1020.57 | 465.74 |
| 1014.02 | 447.86 |
| 1020.16 | 447.65 |
| 1011.78 | 437.87 |
| 1016.87 | 483.51 |
| 1018.21 | 479.65 |
| 1016.88 | 455.16 |
| 1002.49 | 431.91 |
| 1014.13 | 470.68 |
| 1009.29 | 429.28 |
| 1018.32 | 450.81 |
| 1011.49 | 437.73 |
| 1015.16 | 460.21 |
| 1001.6 | 442.86 |
| 1021.51 | 482.99 |
| 1013.27 | 440.0 |
| 1033.25 | 478.48 |
| 1015.01 | 455.28 |
| 1002.07 | 436.94 |
| 1022.36 | 461.06 |
| 1013.97 | 438.28 |
| 1011.81 | 472.61 |
| 1003.7 | 426.85 |
| 1017.76 | 470.18 |
| 1012.31 | 455.38 |
| 1006.44 | 428.32 |
| 1010.57 | 480.35 |
| 1020.2 | 455.56 |
| 1013.14 | 447.66 |
| 1006.74 | 443.06 |
| 1014.19 | 452.43 |
| 1019.94 | 477.81 |
| 1004.72 | 431.66 |
| 1004.53 | 431.8 |
| 1006.65 | 446.67 |
| 1018.0 | 445.26 |
| 1013.85 | 425.72 |
| 1008.73 | 430.58 |
| 1018.94 | 439.86 |
| 1013.31 | 441.11 |
| 1011.6 | 434.72 |
| 1014.23 | 434.01 |
| 1019.43 | 475.64 |
| 1020.43 | 460.44 |
| 1005.46 | 436.4 |
| 1018.7 | 461.03 |
| 1019.94 | 479.08 |
| 1009.31 | 435.76 |
| 1015.22 | 460.14 |
| 1017.37 | 442.2 |
| 1008.83 | 447.69 |
| 1006.65 | 431.15 |
| 1014.32 | 445.0 |
| 1001.32 | 431.59 |
| 1027.94 | 467.22 |
| 1012.16 | 445.33 |
| 1013.21 | 470.57 |
| 1018.72 | 473.77 |
| 1017.42 | 447.67 |
| 1014.78 | 474.29 |
| 1008.07 | 437.14 |
| 1003.12 | 432.56 |
| 1009.72 | 459.14 |
| 1016.03 | 446.19 |
| 1006.38 | 428.1 |
| 1030.18 | 468.46 |
| 1017.95 | 435.02 |
| 1002.38 | 445.52 |
| 1003.36 | 462.69 |
| 1019.26 | 455.75 |
| 1015.89 | 463.74 |
| 1011.95 | 439.79 |
| 1016.99 | 443.26 |
| 1009.3 | 432.04 |
| 1013.32 | 465.86 |
| 1009.05 | 465.6 |
| 1009.35 | 469.43 |
| 1003.4 | 440.75 |
| 1015.82 | 481.32 |
| 1022.64 | 479.87 |
| 1005.7 | 458.59 |
| 1009.62 | 438.62 |
| 1012.38 | 445.59 |
| 1015.13 | 481.87 |
| 1013.58 | 475.01 |
| 1008.53 | 436.54 |
| 1016.28 | 456.63 |
| 1015.63 | 451.69 |
| 1010.93 | 463.04 |
| 1007.68 | 446.1 |
| 1009.94 | 438.67 |
| 1019.7 | 466.88 |
| 1008.42 | 444.6 |
| 1015.4 | 440.26 |
| 1023.28 | 483.92 |
| 1017.18 | 475.19 |
| 1023.06 | 479.24 |
| 1014.95 | 434.92 |
| 1014.0 | 454.16 |
| 1012.42 | 447.58 |
| 1021.67 | 467.9 |
| 1010.27 | 426.29 |
| 1007.8 | 447.02 |
| 1005.47 | 455.85 |
| 1010.31 | 476.46 |
| 1007.53 | 437.48 |
| 1014.61 | 452.77 |
| 1012.57 | 491.54 |
| 1017.22 | 438.41 |
| 1019.81 | 476.1 |
| 1007.87 | 464.58 |
| 1023.91 | 467.74 |
| 1010.0 | 442.12 |
| 1014.53 | 453.34 |
| 1010.44 | 425.29 |
| 1011.74 | 449.63 |
| 1025.27 | 462.88 |
| 1015.71 | 464.67 |
| 1027.17 | 489.96 |
| 1020.27 | 482.38 |
| 1009.45 | 437.95 |
| 1004.74 | 429.2 |
| 1013.03 | 453.34 |
| 1010.58 | 442.47 |
| 1015.14 | 462.6 |
| 1024.57 | 478.79 |
| 1012.34 | 456.11 |
| 1005.81 | 450.33 |
| 1007.21 | 434.83 |
| 1015.1 | 433.43 |
| 1020.76 | 456.02 |
| 1018.08 | 485.23 |
| 1013.03 | 473.57 |
| 1011.17 | 469.94 |
| 1008.69 | 452.07 |
| 1021.82 | 475.32 |
| 1013.39 | 480.69 |
| 1013.12 | 444.01 |
| 1020.41 | 465.17 |
| 1012.87 | 480.61 |
| 1013.39 | 476.04 |
| 1003.51 | 441.76 |
| 1005.19 | 428.24 |
| 1004.0 | 444.77 |
| 1008.58 | 463.1 |
| 1018.19 | 470.5 |
| 1007.04 | 431.0 |
| 1004.27 | 430.68 |
| 1006.6 | 436.42 |
| 1016.19 | 452.33 |
| 1010.64 | 440.16 |
| 1010.18 | 435.75 |
| 1008.48 | 449.74 |
| 1010.74 | 430.73 |
| 1009.55 | 432.75 |
| 1007.88 | 446.79 |
| 1018.12 | 486.35 |
| 1015.66 | 453.18 |
| 1015.73 | 458.31 |
| 1019.7 | 480.26 |
| 1011.89 | 448.65 |
| 1012.67 | 458.41 |
| 1006.37 | 435.39 |
| 1010.49 | 450.21 |
| 1018.68 | 459.59 |
| 1017.19 | 445.84 |
| 1018.17 | 441.08 |
| 1009.89 | 467.33 |
| 1016.56 | 444.19 |
| 1007.96 | 432.96 |
| 1002.85 | 438.09 |
| 1006.86 | 467.9 |
| 1012.68 | 475.72 |
| 1027.72 | 477.51 |
| 1006.37 | 435.13 |
| 1017.99 | 477.9 |
| 1019.18 | 457.26 |
| 1015.88 | 467.53 |
| 1021.08 | 465.15 |
| 1014.42 | 474.28 |
| 1013.85 | 444.49 |
| 1014.15 | 452.84 |
| 1008.5 | 435.38 |
| 1003.88 | 433.57 |
| 1009.04 | 435.27 |
| 1005.35 | 468.49 |
| 1009.9 | 433.07 |
| 1004.85 | 430.63 |
| 1013.29 | 440.74 |
| 1026.31 | 474.49 |
| 1005.72 | 449.74 |
| 1010.09 | 436.73 |
| 1006.53 | 434.58 |
| 1017.24 | 473.93 |
| 1007.03 | 435.99 |
| 1009.45 | 466.83 |
| 1005.29 | 427.22 |
| 1013.39 | 444.07 |
| 1020.11 | 469.57 |
| 1015.18 | 459.89 |
| 1024.91 | 479.59 |
| 1007.21 | 440.92 |
| 1020.45 | 480.87 |
| 1009.93 | 441.9 |
| 1011.35 | 430.2 |
| 1014.62 | 465.16 |
| 1021.19 | 471.32 |
| 1020.91 | 485.43 |
| 1012.27 | 495.35 |
| 1010.85 | 449.12 |
| 1006.22 | 480.53 |
| 1014.89 | 457.07 |
| 1010.7 | 443.67 |
| 1023.44 | 477.52 |
| 1012.52 | 472.95 |
| 1003.92 | 472.54 |
| 1015.85 | 469.17 |
| 1011.09 | 435.21 |
| 1004.81 | 477.78 |
| 1012.86 | 475.89 |
| 1017.75 | 483.9 |
| 1016.53 | 476.2 |
| 1015.47 | 462.16 |
| 1011.24 | 471.05 |
| 1010.6 | 484.71 |
| 1015.43 | 446.34 |
| 1007.21 | 469.02 |
| 1004.23 | 432.12 |
| 1015.51 | 467.28 |
| 1013.29 | 429.66 |
| 1013.16 | 469.49 |
| 1023.23 | 485.87 |
| 1026.0 | 481.95 |
| 1031.1 | 479.03 |
| 1010.99 | 434.5 |
| 1015.16 | 464.9 |
| 1020.02 | 452.71 |
| 1010.84 | 429.74 |
| 1020.67 | 457.09 |
| 1011.61 | 446.77 |
| 1014.46 | 460.76 |
| 1008.31 | 471.95 |
| 1014.09 | 453.29 |
| 1016.34 | 441.61 |
| 1017.01 | 464.73 |
| 1016.91 | 464.68 |
| 1003.72 | 430.59 |
| 1014.19 | 438.01 |
| 1021.84 | 479.08 |
| 1009.28 | 436.39 |
| 1016.25 | 447.07 |
| 1011.9 | 479.91 |
| 1013.88 | 489.05 |
| 1016.46 | 463.17 |
| 1001.18 | 471.26 |
| 1011.64 | 480.49 |
| 1025.41 | 473.78 |
| 1016.76 | 455.5 |
| 1013.78 | 446.27 |
| 1021.21 | 482.2 |
| 1016.72 | 452.48 |
| 1011.92 | 464.48 |
| 1015.29 | 438.1 |
| 1012.77 | 445.6 |
| 1010.37 | 442.43 |
| 1011.56 | 436.67 |
| 1016.54 | 466.56 |
| 1019.65 | 457.29 |
| 1029.65 | 487.03 |
| 1026.45 | 464.93 |
| 1019.28 | 466.0 |
| 1017.44 | 469.52 |
| 1011.18 | 428.88 |
| 1018.49 | 474.3 |
| 1009.69 | 461.06 |
| 1014.09 | 465.57 |
| 1012.3 | 467.67 |
| 1016.02 | 466.99 |
| 1013.73 | 463.72 |
| 1004.03 | 443.78 |
| 1005.43 | 445.23 |
| 1017.13 | 464.43 |
| 1028.31 | 484.36 |
| 1012.5 | 442.16 |
| 1004.64 | 464.11 |
| 1018.35 | 462.48 |
| 1009.59 | 477.49 |
| 1012.78 | 437.04 |
| 1003.8 | 457.09 |
| 1012.22 | 450.6 |
| 1023.25 | 465.78 |
| 1010.15 | 427.1 |
| 1020.14 | 459.81 |
| 1006.64 | 447.36 |
| 1011.0 | 488.92 |
| 1012.96 | 433.36 |
| 1021.99 | 483.35 |
| 1026.57 | 469.53 |
| 1012.27 | 476.96 |
| 1017.5 | 440.75 |
| 1024.51 | 462.55 |
| 1012.05 | 448.04 |
| 1016.22 | 455.24 |
| 1011.8 | 494.75 |
| 1012.55 | 444.58 |
| 1011.56 | 484.82 |
| 1019.6 | 442.9 |
| 1009.68 | 485.46 |
| 1012.82 | 457.81 |
| 1013.85 | 481.92 |
| 1009.63 | 443.23 |
| 1000.91 | 474.29 |
| 1009.98 | 430.46 |
| 1013.56 | 455.71 |
| 1011.25 | 438.34 |
| 1003.24 | 485.83 |
| 1017.76 | 452.82 |
| 1007.68 | 435.04 |
| 1016.82 | 451.21 |
| 1028.41 | 465.81 |
| 1025.04 | 458.42 |
| 1026.09 | 470.22 |
| 1020.84 | 449.24 |
| 1023.84 | 471.43 |
| 1023.74 | 473.26 |
| 1011.7 | 452.82 |
| 1008.1 | 432.69 |
| 1017.91 | 444.13 |
| 1029.8 | 467.21 |
| 1002.33 | 445.98 |
| 1002.42 | 436.91 |
| 1009.05 | 455.01 |
| 1002.47 | 437.11 |
| 1020.68 | 477.06 |
| 1006.65 | 441.71 |
| 1019.63 | 495.76 |
| 1011.33 | 445.63 |
| 1012.88 | 464.72 |
| 1005.94 | 438.03 |
| 1003.47 | 434.78 |
| 1015.63 | 444.67 |
| 1012.2 | 452.24 |
| 1014.19 | 450.92 |
| 1006.65 | 436.53 |
| 1005.75 | 435.53 |
| 1013.23 | 440.01 |
| 1008.72 | 443.1 |
| 1007.18 | 427.49 |
| 1012.99 | 436.25 |
| 1009.99 | 440.74 |
| 1015.02 | 443.54 |
| 1010.82 | 459.42 |
| 1009.76 | 439.66 |
| 1023.55 | 464.15 |
| 1020.55 | 459.1 |
| 1014.76 | 455.68 |
| 1015.33 | 469.08 |
| 1007.71 | 478.02 |
| 1017.36 | 456.8 |
| 1009.18 | 441.13 |
| 1017.05 | 463.88 |
| 1006.14 | 430.45 |
| 1014.24 | 449.18 |
| 1010.92 | 447.89 |
| 1010.4 | 431.59 |
| 1009.36 | 447.5 |
| 1033.04 | 475.58 |
| 1016.77 | 453.24 |
| 1012.59 | 446.4 |
| 1025.1 | 476.81 |
| 1019.29 | 474.1 |
| 1016.66 | 450.71 |
| 1006.4 | 433.62 |
| 1011.45 | 465.14 |
| 1019.08 | 445.18 |
| 1015.3 | 474.12 |
| 1016.08 | 483.91 |
| 1010.55 | 486.68 |
| 1022.43 | 464.98 |
| 1010.83 | 481.4 |
| 1021.81 | 479.2 |
| 1005.85 | 463.86 |
| 1012.76 | 472.3 |
| 1001.31 | 446.51 |
| 1005.93 | 437.71 |
| 1001.96 | 458.94 |
| 1007.62 | 437.91 |
| 1009.96 | 490.76 |
| 1013.4 | 439.66 |
| 1007.58 | 463.27 |
| 1016.68 | 473.99 |
| 1012.83 | 433.38 |
| 1015.13 | 459.01 |
| 1016.05 | 471.44 |
| 1012.97 | 471.91 |
| 1028.2 | 465.15 |
| 1008.25 | 446.66 |
| 1009.78 | 438.15 |
| 1008.81 | 447.14 |
| 1025.53 | 472.32 |
| 1010.16 | 441.68 |
| 1009.33 | 440.04 |
| 1009.82 | 444.82 |
| 1014.5 | 457.26 |
| 1009.13 | 428.83 |
| 1009.93 | 449.07 |
| 1009.38 | 435.21 |
| 1017.59 | 471.03 |
| 1012.47 | 465.56 |
| 1019.86 | 442.83 |
| 1017.26 | 460.3 |
| 1023.07 | 474.25 |
| 1033.3 | 477.97 |
| 1019.1 | 472.16 |
| 1014.22 | 456.08 |
| 1014.9 | 452.41 |
| 1011.31 | 463.71 |
| 1006.26 | 433.72 |
| 1016.0 | 456.4 |
| 1015.41 | 448.43 |
| 1020.63 | 481.6 |
| 1001.16 | 457.07 |
| 1019.8 | 451.0 |
| 1018.48 | 440.28 |
| 1002.26 | 437.47 |
| 1004.07 | 443.57 |
| 1004.91 | 426.6 |
| 1013.12 | 470.87 |
| 1012.9 | 478.37 |
| 1013.32 | 453.92 |
| 1020.79 | 470.22 |
| 1011.37 | 434.54 |
| 1013.11 | 442.89 |
| 1013.29 | 479.03 |
| 1020.5 | 476.06 |
| 1022.62 | 473.88 |
| 1010.84 | 451.75 |
| 1012.68 | 439.2 |
| 1015.58 | 439.7 |
| 1013.68 | 463.6 |
| 1004.21 | 447.47 |
| 1013.23 | 447.92 |
| 1020.44 | 471.08 |
| 1007.99 | 437.55 |
| 1012.36 | 448.27 |
| 998.47 | 431.69 |
| 1016.57 | 449.09 |
| 1015.93 | 448.79 |
| 1025.21 | 460.21 |
| 1013.54 | 479.28 |
| 1032.67 | 483.11 |
| 1011.46 | 450.75 |
| 1010.43 | 437.97 |
| 1008.53 | 459.76 |
| 1020.5 | 457.75 |
| 1015.48 | 469.33 |
| 1009.74 | 433.28 |
| 1010.23 | 444.64 |
| 1021.3 | 463.1 |
| 1022.01 | 460.91 |
| 1023.95 | 479.35 |
| 1017.65 | 449.23 |
| 1021.83 | 474.51 |
| 1007.81 | 435.02 |
| 1009.43 | 435.45 |
| 1013.3 | 452.38 |
| 1019.73 | 480.41 |
| 1019.54 | 478.96 |
| 1026.58 | 468.87 |
| 1007.89 | 434.01 |
| 1013.85 | 466.36 |
| 1011.44 | 435.28 |
| 1014.51 | 486.46 |
| 1015.51 | 468.19 |
| 1022.14 | 468.37 |
| 1019.17 | 474.19 |
| 1009.52 | 440.32 |
| 1015.35 | 485.32 |
| 1014.38 | 464.27 |
| 1013.66 | 479.25 |
| 1007.0 | 430.4 |
| 1016.65 | 447.49 |
| 1006.85 | 438.23 |
| 1025.46 | 492.09 |
| 1015.13 | 475.36 |
| 1016.68 | 452.56 |
| 1015.98 | 427.84 |
| 1010.8 | 433.95 |
| 1008.48 | 435.27 |
| 1014.04 | 454.62 |
| 1020.36 | 472.17 |
| 1015.96 | 452.42 |
| 1003.19 | 472.17 |
| 1018.01 | 481.83 |
| 1021.83 | 458.78 |
| 1022.47 | 447.5 |
| 1019.04 | 463.4 |
| 1022.67 | 473.57 |
| 1009.07 | 433.72 |
| 1011.2 | 431.85 |
| 1012.13 | 433.47 |
| 1007.45 | 432.84 |
| 1007.29 | 436.6 |
| 1020.12 | 490.23 |
| 1020.58 | 477.16 |
| 1010.44 | 441.06 |
| 1007.22 | 440.86 |
| 1033.08 | 477.94 |
| 1026.56 | 474.47 |
| 1012.18 | 470.67 |
| 1013.7 | 447.31 |
| 1018.14 | 466.8 |
| 1007.4 | 430.91 |
| 1007.2 | 434.75 |
| 1010.82 | 469.52 |
| 1008.88 | 438.9 |
| 1010.51 | 429.56 |
| 1013.53 | 432.92 |
| 1005.68 | 442.87 |
| 1022.57 | 466.59 |
| 1028.04 | 479.61 |
| 1019.12 | 471.08 |
| 1016.51 | 433.37 |
| 1018.8 | 443.92 |
| 1016.74 | 443.5 |
| 1017.37 | 439.89 |
| 1008.9 | 434.66 |
| 1021.95 | 487.57 |
| 1013.76 | 464.64 |
| 1017.26 | 470.92 |
| 1014.37 | 444.39 |
| 1002.54 | 442.48 |
| 1005.25 | 449.61 |
| 1009.43 | 435.02 |
| 1021.81 | 458.67 |
| 1016.63 | 461.74 |
| 1008.09 | 438.31 |
| 1011.68 | 462.38 |
| 1019.39 | 460.56 |
| 1013.37 | 439.22 |
| 1009.71 | 444.64 |
| 999.8 | 430.34 |
| 1008.37 | 430.46 |
| 1009.02 | 456.79 |
| 994.17 | 468.82 |
| 1008.79 | 448.51 |
| 1014.67 | 470.77 |
| 1025.79 | 465.74 |
| 1005.31 | 430.21 |
| 1008.62 | 449.23 |
| 1021.91 | 461.89 |
| 1010.97 | 445.72 |
| 1013.36 | 466.13 |
| 1008.38 | 448.71 |
| 998.43 | 469.25 |
| 1013.04 | 450.56 |
| 1008.94 | 464.46 |
| 1014.87 | 471.13 |
| 1010.92 | 461.52 |
| 1012.04 | 451.09 |
| 1005.43 | 431.51 |
| 1009.65 | 469.8 |
| 1012.05 | 442.28 |
| 1011.84 | 458.67 |
| 1016.13 | 462.4 |
| 1015.18 | 453.54 |
| 1019.81 | 444.38 |
| 1003.39 | 440.52 |
| 1007.68 | 433.62 |
| 1023.68 | 481.96 |
| 1008.89 | 452.75 |
| 1026.4 | 481.28 |
| 1020.28 | 439.03 |
| 1005.64 | 435.75 |
| 1009.72 | 436.03 |
| 1011.03 | 445.6 |
| 1020.27 | 462.65 |
| 1005.87 | 438.66 |
| 1008.54 | 447.32 |
| 1004.85 | 484.55 |
| 1024.9 | 476.8 |
| 1023.99 | 480.34 |
| 1010.53 | 440.63 |
| 1014.27 | 459.48 |
| 1025.98 | 490.78 |
| 1019.25 | 483.56 |
| 1012.26 | 429.38 |
| 1013.49 | 440.27 |
| 1011.34 | 445.34 |
| 1013.86 | 447.43 |
| 1004.37 | 439.91 |
| 1016.11 | 459.27 |
| 1017.88 | 478.89 |
| 1012.41 | 466.7 |
| 1021.39 | 463.5 |
| 1015.04 | 436.21 |
| 1011.95 | 443.94 |
| 1012.87 | 439.63 |
| 1013.21 | 460.95 |
| 1015.99 | 448.69 |
| 1006.24 | 444.63 |
| 1005.49 | 473.51 |
| 1008.56 | 462.56 |
| 1011.07 | 451.76 |
| 1025.68 | 491.81 |
| 1011.04 | 429.52 |
| 1007.59 | 437.9 |
| 1003.18 | 467.54 |
| 1015.35 | 449.97 |
| 1003.61 | 436.62 |
| 1023.37 | 477.68 |
| 1016.04 | 447.26 |
| 1011.8 | 439.76 |
| 1015.23 | 437.49 |
| 1018.29 | 455.14 |
| 1021.76 | 485.5 |
| 1016.81 | 444.1 |
| 1006.91 | 432.33 |
| 1008.87 | 471.23 |
| 1017.93 | 463.89 |
| 1009.2 | 445.54 |
| 1007.99 | 446.09 |
| 1017.82 | 445.12 |
| 1018.29 | 443.31 |
| 1015.14 | 484.16 |
| 1019.86 | 477.76 |
| 1008.36 | 430.28 |
| 1010.39 | 446.48 |
| 1028.11 | 481.03 |
| 1007.41 | 466.07 |
| 1013.2 | 447.47 |
| 1019.83 | 455.93 |
| 1021.15 | 479.62 |
| 1014.28 | 455.06 |
| 1019.87 | 475.06 |
| 1012.59 | 438.89 |
| 1003.38 | 432.7 |
| 1011.89 | 452.6 |
| 1010.46 | 451.75 |
| 1008.16 | 430.66 |
| 1019.04 | 491.9 |
| 1010.04 | 439.82 |
| 1015.91 | 460.73 |
| 1015.14 | 449.7 |
| 1013.88 | 439.42 |
| 1013.33 | 439.84 |
| 1025.58 | 485.86 |
| 1011.81 | 458.1 |
| 1012.89 | 479.92 |
| 1019.94 | 458.29 |
| 1017.29 | 489.45 |
| 1012.17 | 434.0 |
| 1010.69 | 431.24 |
| 1003.26 | 439.5 |
| 1019.48 | 467.46 |
| 1010.0 | 429.27 |
| 1016.95 | 452.1 |
| 999.83 | 472.41 |
| 1002.75 | 442.14 |
| 1003.56 | 441.0 |
| 1020.76 | 463.07 |
| 1017.99 | 445.71 |
| 1017.11 | 483.16 |
| 1010.75 | 440.45 |
| 1020.6 | 481.83 |
| 1015.53 | 467.6 |
| 1004.29 | 450.88 |
| 1001.22 | 425.5 |
| 1013.95 | 451.87 |
| 1010.51 | 428.94 |
| 1002.59 | 439.86 |
| 1011.21 | 433.44 |
| 1015.12 | 438.23 |
| 1007.68 | 436.95 |
| 1021.67 | 470.19 |
| 1011.6 | 484.66 |
| 1007.56 | 430.81 |
| 1010.05 | 433.37 |
| 1014.17 | 453.02 |
| 1012.6 | 453.5 |
| 995.88 | 463.09 |
| 1016.25 | 464.56 |
| 1017.22 | 452.12 |
| 1015.66 | 470.9 |
| 1021.08 | 450.89 |
| 1009.85 | 445.04 |
| 1011.49 | 444.72 |
| 1022.07 | 460.38 |
| 1013.05 | 446.8 |
| 1018.34 | 465.05 |
| 1016.55 | 484.13 |
| 1017.01 | 488.27 |
| 1013.85 | 447.09 |
| 1017.16 | 452.02 |
| 1014.16 | 455.55 |
| 1029.41 | 480.99 |
| 1012.96 | 467.68 |
| Humidity | Power |
|---|---|
| 73.17 | 463.26 |
| 59.08 | 444.37 |
| 92.14 | 488.56 |
| 76.64 | 446.48 |
| 96.62 | 473.9 |
| 58.77 | 443.67 |
| 75.24 | 467.35 |
| 66.43 | 478.42 |
| 41.25 | 475.98 |
| 70.72 | 477.5 |
| 75.04 | 453.02 |
| 64.22 | 453.99 |
| 84.15 | 440.29 |
| 61.83 | 451.28 |
| 87.59 | 433.99 |
| 43.08 | 462.19 |
| 48.84 | 467.54 |
| 77.51 | 477.2 |
| 63.59 | 459.85 |
| 55.28 | 464.3 |
| 66.26 | 468.27 |
| 64.77 | 495.24 |
| 83.31 | 483.8 |
| 47.19 | 443.61 |
| 54.93 | 436.06 |
| 74.62 | 443.25 |
| 72.52 | 464.16 |
| 88.44 | 475.52 |
| 92.28 | 484.41 |
| 41.85 | 437.89 |
| 44.28 | 445.11 |
| 64.58 | 438.86 |
| 63.25 | 440.98 |
| 78.61 | 436.65 |
| 44.51 | 444.26 |
| 89.46 | 465.86 |
| 74.52 | 444.37 |
| 88.86 | 450.69 |
| 75.51 | 469.02 |
| 78.64 | 448.86 |
| 76.65 | 447.14 |
| 80.44 | 469.18 |
| 79.89 | 482.8 |
| 88.28 | 476.7 |
| 84.6 | 474.99 |
| 42.69 | 444.22 |
| 78.41 | 461.33 |
| 61.07 | 448.06 |
| 50.0 | 474.6 |
| 77.29 | 473.05 |
| 43.66 | 432.06 |
| 83.8 | 467.41 |
| 66.47 | 430.12 |
| 93.09 | 473.62 |
| 80.52 | 471.81 |
| 68.99 | 442.99 |
| 57.27 | 442.77 |
| 95.53 | 491.49 |
| 71.72 | 447.46 |
| 57.88 | 446.11 |
| 63.34 | 442.44 |
| 48.07 | 446.22 |
| 91.87 | 471.49 |
| 87.27 | 463.5 |
| 64.4 | 440.01 |
| 43.4 | 441.03 |
| 72.24 | 452.68 |
| 90.22 | 474.91 |
| 74.0 | 478.77 |
| 71.85 | 434.2 |
| 86.62 | 437.91 |
| 97.41 | 477.61 |
| 84.44 | 431.65 |
| 81.55 | 430.57 |
| 75.66 | 481.09 |
| 79.41 | 445.56 |
| 58.91 | 475.74 |
| 90.06 | 435.12 |
| 79.0 | 446.15 |
| 69.47 | 436.64 |
| 51.47 | 436.69 |
| 83.13 | 468.75 |
| 40.33 | 466.6 |
| 81.69 | 465.48 |
| 94.55 | 441.34 |
| 91.81 | 441.83 |
| 63.62 | 464.7 |
| 49.35 | 437.99 |
| 69.61 | 459.12 |
| 38.75 | 429.69 |
| 90.17 | 459.8 |
| 81.24 | 433.63 |
| 48.46 | 442.84 |
| 76.72 | 485.13 |
| 51.16 | 459.12 |
| 76.34 | 445.31 |
| 67.3 | 480.8 |
| 52.38 | 432.55 |
| 76.44 | 443.86 |
| 91.55 | 449.77 |
| 71.9 | 470.71 |
| 80.05 | 452.17 |
| 63.77 | 478.29 |
| 62.26 | 428.54 |
| 89.04 | 478.27 |
| 58.02 | 439.58 |
| 81.82 | 457.32 |
| 91.14 | 475.51 |
| 88.92 | 439.66 |
| 84.83 | 471.99 |
| 91.76 | 479.81 |
| 86.56 | 434.78 |
| 57.21 | 446.58 |
| 54.25 | 437.76 |
| 63.8 | 459.36 |
| 33.71 | 462.28 |
| 67.25 | 464.33 |
| 60.11 | 444.36 |
| 74.55 | 438.64 |
| 67.34 | 470.49 |
| 42.75 | 455.13 |
| 55.2 | 450.22 |
| 83.61 | 440.43 |
| 88.78 | 482.98 |
| 100.12 | 460.44 |
| 64.52 | 444.97 |
| 51.41 | 433.94 |
| 85.78 | 439.73 |
| 75.41 | 434.48 |
| 81.63 | 442.33 |
| 51.92 | 457.67 |
| 70.12 | 454.66 |
| 53.83 | 432.21 |
| 77.23 | 457.66 |
| 65.67 | 435.21 |
| 71.18 | 448.22 |
| 81.96 | 475.51 |
| 79.54 | 446.53 |
| 47.09 | 441.3 |
| 57.69 | 433.54 |
| 78.89 | 472.52 |
| 85.29 | 474.77 |
| 40.13 | 435.1 |
| 77.06 | 450.74 |
| 67.38 | 442.7 |
| 62.44 | 426.56 |
| 77.43 | 463.71 |
| 58.77 | 447.06 |
| 67.72 | 452.27 |
| 42.14 | 445.78 |
| 84.16 | 438.65 |
| 89.79 | 480.15 |
| 67.21 | 447.19 |
| 72.14 | 443.04 |
| 97.49 | 488.81 |
| 87.74 | 455.75 |
| 96.3 | 455.86 |
| 61.25 | 457.68 |
| 88.38 | 479.11 |
| 74.77 | 432.84 |
| 68.18 | 448.37 |
| 77.2 | 447.06 |
| 49.54 | 443.53 |
| 92.22 | 445.21 |
| 33.65 | 441.7 |
| 64.59 | 450.93 |
| 100.09 | 451.44 |
| 68.04 | 441.29 |
| 48.94 | 458.85 |
| 74.47 | 481.46 |
| 81.02 | 467.19 |
| 71.17 | 461.54 |
| 53.85 | 439.08 |
| 70.67 | 467.22 |
| 59.36 | 468.8 |
| 57.17 | 426.93 |
| 70.29 | 474.65 |
| 83.37 | 468.97 |
| 87.36 | 433.97 |
| 100.09 | 450.53 |
| 68.78 | 444.51 |
| 70.98 | 469.03 |
| 75.68 | 466.56 |
| 47.49 | 457.57 |
| 71.99 | 440.13 |
| 66.55 | 433.24 |
| 74.73 | 452.55 |
| 64.78 | 443.29 |
| 75.13 | 431.76 |
| 56.38 | 454.97 |
| 94.35 | 456.7 |
| 86.55 | 486.03 |
| 82.95 | 472.79 |
| 88.42 | 452.03 |
| 85.61 | 443.41 |
| 58.39 | 441.93 |
| 74.28 | 432.64 |
| 87.85 | 480.25 |
| 83.5 | 466.68 |
| 65.24 | 494.39 |
| 75.01 | 454.72 |
| 84.52 | 448.71 |
| 80.52 | 469.76 |
| 75.14 | 450.71 |
| 75.75 | 444.01 |
| 76.72 | 453.2 |
| 85.47 | 450.87 |
| 57.95 | 441.73 |
| 78.32 | 465.09 |
| 52.2 | 447.28 |
| 93.69 | 491.16 |
| 75.74 | 450.98 |
| 67.56 | 446.3 |
| 69.46 | 436.48 |
| 74.58 | 460.84 |
| 53.23 | 442.56 |
| 88.72 | 467.3 |
| 96.16 | 479.13 |
| 68.26 | 441.15 |
| 86.39 | 445.52 |
| 85.34 | 475.4 |
| 72.64 | 469.3 |
| 97.82 | 463.57 |
| 77.22 | 445.32 |
| 80.59 | 461.03 |
| 46.91 | 466.74 |
| 57.76 | 444.04 |
| 53.09 | 434.01 |
| 84.31 | 465.23 |
| 71.58 | 440.6 |
| 92.97 | 466.74 |
| 74.55 | 433.48 |
| 78.96 | 473.59 |
| 64.44 | 474.81 |
| 68.23 | 454.75 |
| 70.81 | 452.94 |
| 61.66 | 435.83 |
| 77.76 | 482.19 |
| 69.49 | 466.66 |
| 96.26 | 462.59 |
| 55.74 | 447.82 |
| 95.61 | 462.73 |
| 84.75 | 447.98 |
| 75.3 | 462.72 |
| 67.5 | 442.42 |
| 80.92 | 444.69 |
| 79.23 | 466.7 |
| 81.1 | 453.84 |
| 32.8 | 436.92 |
| 84.31 | 486.37 |
| 46.15 | 440.43 |
| 53.96 | 446.82 |
| 59.83 | 484.91 |
| 75.3 | 437.76 |
| 42.53 | 438.91 |
| 70.58 | 464.19 |
| 91.69 | 442.19 |
| 63.55 | 446.86 |
| 61.51 | 457.15 |
| 69.55 | 482.57 |
| 98.08 | 476.03 |
| 79.34 | 428.89 |
| 81.28 | 472.7 |
| 78.99 | 445.6 |
| 80.38 | 464.78 |
| 51.16 | 440.42 |
| 72.17 | 428.41 |
| 75.39 | 438.5 |
| 68.91 | 438.28 |
| 96.38 | 476.29 |
| 70.54 | 448.46 |
| 45.8 | 438.99 |
| 57.95 | 471.8 |
| 81.89 | 471.81 |
| 69.32 | 449.82 |
| 59.14 | 442.14 |
| 81.54 | 441.46 |
| 85.81 | 477.62 |
| 65.41 | 446.76 |
| 81.15 | 472.52 |
| 95.87 | 471.58 |
| 90.24 | 440.85 |
| 75.13 | 431.37 |
| 88.22 | 437.33 |
| 81.48 | 469.22 |
| 89.84 | 471.11 |
| 43.57 | 439.17 |
| 63.16 | 445.33 |
| 57.14 | 473.71 |
| 77.76 | 452.66 |
| 90.56 | 440.99 |
| 60.98 | 467.42 |
| 70.31 | 444.14 |
| 74.05 | 457.17 |
| 75.42 | 467.87 |
| 82.25 | 442.04 |
| 67.95 | 471.36 |
| 100.09 | 460.7 |
| 47.28 | 431.33 |
| 72.41 | 432.6 |
| 77.67 | 447.61 |
| 63.7 | 443.87 |
| 79.77 | 446.87 |
| 93.84 | 465.74 |
| 84.95 | 447.86 |
| 70.16 | 447.65 |
| 84.24 | 437.87 |
| 73.32 | 483.51 |
| 86.17 | 479.65 |
| 65.43 | 455.16 |
| 94.59 | 431.91 |
| 86.8 | 470.68 |
| 58.18 | 429.28 |
| 89.66 | 450.81 |
| 87.39 | 437.73 |
| 36.35 | 460.21 |
| 79.62 | 442.86 |
| 50.52 | 482.99 |
| 51.96 | 440.0 |
| 74.73 | 478.48 |
| 78.33 | 455.28 |
| 85.19 | 436.94 |
| 83.13 | 461.06 |
| 53.49 | 438.28 |
| 88.86 | 472.61 |
| 60.89 | 426.85 |
| 61.14 | 470.18 |
| 68.29 | 455.38 |
| 57.62 | 428.32 |
| 83.63 | 480.35 |
| 78.1 | 455.56 |
| 66.34 | 447.66 |
| 79.02 | 443.06 |
| 68.96 | 452.43 |
| 71.13 | 477.81 |
| 87.01 | 431.66 |
| 74.3 | 431.8 |
| 77.62 | 446.67 |
| 59.56 | 445.26 |
| 41.66 | 425.72 |
| 73.27 | 430.58 |
| 77.16 | 439.86 |
| 67.02 | 441.11 |
| 52.8 | 434.72 |
| 39.04 | 434.01 |
| 65.47 | 475.64 |
| 74.32 | 460.44 |
| 69.22 | 436.4 |
| 93.88 | 461.03 |
| 69.83 | 479.08 |
| 84.11 | 435.76 |
| 78.65 | 460.14 |
| 69.31 | 442.2 |
| 70.3 | 447.69 |
| 68.23 | 431.15 |
| 71.76 | 445.0 |
| 85.88 | 431.59 |
| 71.09 | 467.22 |
| 52.67 | 445.33 |
| 89.68 | 470.57 |
| 73.66 | 473.77 |
| 58.94 | 447.67 |
| 87.05 | 474.29 |
| 67.0 | 437.14 |
| 43.18 | 432.56 |
| 80.62 | 459.14 |
| 59.72 | 446.19 |
| 72.1 | 428.1 |
| 69.15 | 468.46 |
| 55.66 | 435.02 |
| 61.19 | 445.52 |
| 74.62 | 462.69 |
| 73.35 | 455.75 |
| 68.85 | 463.74 |
| 39.89 | 439.79 |
| 53.16 | 443.26 |
| 52.97 | 432.04 |
| 79.87 | 465.86 |
| 84.09 | 465.6 |
| 100.15 | 469.43 |
| 79.77 | 440.75 |
| 88.99 | 481.32 |
| 76.14 | 479.87 |
| 69.13 | 458.59 |
| 93.03 | 438.62 |
| 77.92 | 445.59 |
| 74.89 | 481.87 |
| 88.7 | 475.01 |
| 62.94 | 436.54 |
| 89.62 | 456.63 |
| 81.04 | 451.69 |
| 94.53 | 463.04 |
| 64.02 | 446.1 |
| 70.57 | 438.67 |
| 70.32 | 466.88 |
| 84.86 | 444.6 |
| 81.41 | 440.26 |
| 89.45 | 483.92 |
| 82.71 | 475.19 |
| 93.93 | 479.24 |
| 70.6 | 434.92 |
| 87.68 | 454.16 |
| 87.58 | 447.58 |
| 74.4 | 467.9 |
| 67.35 | 426.29 |
| 63.61 | 447.02 |
| 76.89 | 455.85 |
| 78.08 | 476.46 |
| 69.17 | 437.48 |
| 53.31 | 452.77 |
| 93.32 | 491.54 |
| 42.47 | 438.41 |
| 82.58 | 476.1 |
| 94.59 | 464.58 |
| 86.31 | 467.74 |
| 72.57 | 442.12 |
| 80.76 | 453.34 |
| 71.93 | 425.29 |
| 47.54 | 449.63 |
| 95.72 | 462.88 |
| 77.03 | 464.67 |
| 80.49 | 489.96 |
| 77.67 | 482.38 |
| 78.72 | 437.95 |
| 58.77 | 429.2 |
| 74.8 | 453.34 |
| 51.34 | 442.47 |
| 90.41 | 462.6 |
| 91.1 | 478.79 |
| 62.57 | 456.11 |
| 84.27 | 450.33 |
| 42.93 | 434.83 |
| 40.96 | 433.43 |
| 76.53 | 456.02 |
| 69.74 | 485.23 |
| 74.99 | 473.57 |
| 70.45 | 469.94 |
| 91.49 | 452.07 |
| 88.97 | 475.32 |
| 89.13 | 480.69 |
| 46.52 | 444.01 |
| 60.55 | 465.17 |
| 88.71 | 480.61 |
| 89.15 | 476.04 |
| 83.02 | 441.76 |
| 75.19 | 428.24 |
| 87.35 | 444.77 |
| 85.66 | 463.1 |
| 91.66 | 470.5 |
| 63.47 | 431.0 |
| 72.25 | 430.68 |
| 70.58 | 436.42 |
| 60.1 | 452.33 |
| 89.29 | 440.16 |
| 67.43 | 435.75 |
| 67.58 | 449.74 |
| 70.8 | 430.73 |
| 63.62 | 432.75 |
| 66.68 | 446.79 |
| 90.76 | 486.35 |
| 75.34 | 453.18 |
| 59.77 | 458.31 |
| 80.79 | 480.26 |
| 74.1 | 448.65 |
| 41.34 | 458.41 |
| 58.78 | 435.39 |
| 97.78 | 450.21 |
| 74.85 | 459.59 |
| 69.84 | 445.84 |
| 75.36 | 441.08 |
| 85.8 | 467.33 |
| 90.11 | 444.19 |
| 61.63 | 432.96 |
| 44.76 | 438.09 |
| 89.7 | 467.9 |
| 72.51 | 475.72 |
| 74.98 | 477.51 |
| 79.59 | 435.13 |
| 78.42 | 477.9 |
| 61.23 | 457.26 |
| 47.56 | 467.53 |
| 93.06 | 465.15 |
| 89.65 | 474.28 |
| 50.5 | 444.49 |
| 44.84 | 452.84 |
| 85.32 | 435.38 |
| 82.94 | 433.57 |
| 67.26 | 435.27 |
| 79.05 | 468.49 |
| 62.03 | 433.07 |
| 94.36 | 430.63 |
| 60.02 | 440.74 |
| 95.46 | 474.49 |
| 84.92 | 449.74 |
| 62.8 | 436.73 |
| 90.81 | 434.58 |
| 80.9 | 473.93 |
| 55.84 | 435.99 |
| 75.3 | 466.83 |
| 37.34 | 427.22 |
| 79.5 | 444.07 |
| 87.29 | 469.57 |
| 81.5 | 459.89 |
| 76.42 | 479.59 |
| 75.75 | 440.92 |
| 84.95 | 480.87 |
| 62.37 | 441.9 |
| 49.25 | 430.2 |
| 74.16 | 465.16 |
| 90.55 | 471.32 |
| 94.28 | 485.43 |
| 63.31 | 495.35 |
| 78.9 | 449.12 |
| 90.97 | 480.53 |
| 87.34 | 457.07 |
| 80.8 | 443.67 |
| 90.95 | 477.52 |
| 69.97 | 472.95 |
| 89.45 | 472.54 |
| 76.08 | 469.17 |
| 83.35 | 435.21 |
| 92.16 | 477.78 |
| 58.42 | 475.89 |
| 85.06 | 483.9 |
| 88.91 | 476.2 |
| 83.33 | 462.16 |
| 88.49 | 471.05 |
| 96.88 | 484.71 |
| 73.86 | 446.34 |
| 65.17 | 469.02 |
| 69.41 | 432.12 |
| 81.23 | 467.28 |
| 54.07 | 429.66 |
| 89.17 | 469.49 |
| 78.85 | 485.87 |
| 84.44 | 481.95 |
| 83.02 | 479.03 |
| 90.66 | 434.5 |
| 75.29 | 464.9 |
| 82.6 | 452.71 |
| 45.4 | 429.74 |
| 66.33 | 457.09 |
| 45.33 | 446.77 |
| 67.12 | 460.76 |
| 84.14 | 471.95 |
| 80.81 | 453.29 |
| 49.13 | 441.61 |
| 87.29 | 464.73 |
| 52.95 | 464.68 |
| 68.92 | 430.59 |
| 85.21 | 438.01 |
| 88.56 | 479.08 |
| 55.09 | 436.39 |
| 48.64 | 447.07 |
| 87.85 | 479.91 |
| 87.42 | 489.05 |
| 62.75 | 463.17 |
| 94.86 | 471.26 |
| 63.54 | 480.49 |
| 69.46 | 473.78 |
| 74.66 | 455.5 |
| 80.57 | 446.27 |
| 84.7 | 482.2 |
| 72.6 | 452.48 |
| 52.63 | 464.48 |
| 82.01 | 438.1 |
| 75.22 | 445.6 |
| 51.05 | 442.43 |
| 80.1 | 436.67 |
| 81.58 | 466.56 |
| 65.94 | 457.29 |
| 86.74 | 487.03 |
| 62.57 | 464.93 |
| 57.37 | 466.0 |
| 88.91 | 469.52 |
| 72.26 | 428.88 |
| 74.98 | 474.3 |
| 71.19 | 461.06 |
| 62.82 | 465.57 |
| 55.31 | 467.67 |
| 71.57 | 466.99 |
| 59.16 | 463.72 |
| 40.8 | 443.78 |
| 67.63 | 445.23 |
| 97.2 | 464.43 |
| 91.16 | 484.36 |
| 64.81 | 442.16 |
| 85.61 | 464.11 |
| 93.42 | 462.48 |
| 77.36 | 477.49 |
| 67.03 | 437.04 |
| 89.45 | 457.09 |
| 54.84 | 450.6 |
| 53.48 | 465.78 |
| 54.47 | 427.1 |
| 43.36 | 459.81 |
| 48.92 | 447.36 |
| 81.22 | 488.92 |
| 60.35 | 433.36 |
| 75.98 | 483.35 |
| 74.24 | 469.53 |
| 85.21 | 476.96 |
| 68.46 | 440.75 |
| 78.31 | 462.55 |
| 89.25 | 448.04 |
| 68.57 | 455.24 |
| 67.38 | 494.75 |
| 53.6 | 444.58 |
| 91.69 | 484.82 |
| 78.21 | 442.9 |
| 94.19 | 485.46 |
| 37.19 | 457.81 |
| 83.53 | 481.92 |
| 79.45 | 443.23 |
| 99.9 | 474.29 |
| 50.39 | 430.46 |
| 74.33 | 455.71 |
| 83.66 | 438.34 |
| 89.48 | 485.83 |
| 64.59 | 452.82 |
| 75.68 | 435.04 |
| 64.18 | 451.21 |
| 70.09 | 465.81 |
| 70.58 | 458.42 |
| 99.28 | 470.22 |
| 81.89 | 449.24 |
| 87.99 | 471.43 |
| 88.21 | 473.26 |
| 91.29 | 452.82 |
| 52.72 | 432.69 |
| 67.5 | 444.13 |
| 92.05 | 467.21 |
| 63.23 | 445.98 |
| 90.88 | 436.91 |
| 74.91 | 455.01 |
| 85.39 | 437.11 |
| 96.98 | 477.06 |
| 56.28 | 441.71 |
| 65.62 | 495.76 |
| 55.32 | 445.63 |
| 88.88 | 464.72 |
| 39.49 | 438.03 |
| 54.59 | 434.78 |
| 57.19 | 444.67 |
| 45.06 | 452.24 |
| 40.62 | 450.92 |
| 90.21 | 436.53 |
| 90.91 | 435.53 |
| 74.96 | 440.01 |
| 54.21 | 443.1 |
| 63.62 | 427.49 |
| 50.04 | 436.25 |
| 51.23 | 440.74 |
| 82.71 | 443.54 |
| 92.04 | 459.42 |
| 90.67 | 439.66 |
| 91.14 | 464.15 |
| 70.43 | 459.1 |
| 66.63 | 455.68 |
| 86.95 | 469.08 |
| 96.69 | 478.02 |
| 70.88 | 456.8 |
| 47.14 | 441.13 |
| 63.36 | 463.88 |
| 60.58 | 430.45 |
| 54.3 | 449.18 |
| 65.09 | 447.89 |
| 48.16 | 431.59 |
| 81.51 | 447.5 |
| 68.57 | 475.58 |
| 73.16 | 453.24 |
| 80.88 | 446.4 |
| 85.4 | 476.81 |
| 75.77 | 474.1 |
| 75.76 | 450.71 |
| 70.21 | 433.62 |
| 55.53 | 465.14 |
| 80.48 | 445.18 |
| 72.41 | 474.12 |
| 83.25 | 483.91 |
| 82.12 | 486.68 |
| 94.75 | 464.98 |
| 95.79 | 481.4 |
| 86.02 | 479.2 |
| 78.29 | 463.86 |
| 82.23 | 472.3 |
| 52.86 | 446.51 |
| 60.66 | 437.71 |
| 62.77 | 458.94 |
| 65.54 | 437.91 |
| 95.4 | 490.76 |
| 51.78 | 439.66 |
| 63.62 | 463.27 |
| 83.09 | 473.99 |
| 61.81 | 433.38 |
| 68.24 | 459.01 |
| 72.41 | 471.44 |
| 79.64 | 471.91 |
| 66.95 | 465.15 |
| 91.98 | 446.66 |
| 64.96 | 438.15 |
| 88.93 | 447.14 |
| 85.62 | 472.32 |
| 84.0 | 441.68 |
| 89.41 | 440.04 |
| 67.4 | 444.82 |
| 76.75 | 457.26 |
| 89.06 | 428.83 |
| 64.02 | 449.07 |
| 64.12 | 435.21 |
| 81.22 | 471.03 |
| 100.13 | 465.56 |
| 58.07 | 442.83 |
| 63.42 | 460.3 |
| 83.32 | 474.25 |
| 74.28 | 477.97 |
| 71.91 | 472.16 |
| 85.8 | 456.08 |
| 55.58 | 452.41 |
| 69.7 | 463.71 |
| 63.79 | 433.72 |
| 86.59 | 456.4 |
| 48.28 | 448.43 |
| 80.42 | 481.6 |
| 98.58 | 457.07 |
| 72.83 | 451.0 |
| 56.07 | 440.28 |
| 67.13 | 437.47 |
| 84.49 | 443.57 |
| 68.37 | 426.6 |
| 86.07 | 470.87 |
| 83.82 | 478.37 |
| 74.86 | 453.92 |
| 53.52 | 470.22 |
| 80.61 | 434.54 |
| 43.56 | 442.89 |
| 89.35 | 479.03 |
| 97.28 | 476.06 |
| 80.49 | 473.88 |
| 88.9 | 451.75 |
| 49.7 | 439.2 |
| 68.64 | 439.7 |
| 98.58 | 463.6 |
| 82.12 | 447.47 |
| 78.32 | 447.92 |
| 86.04 | 471.08 |
| 91.36 | 437.55 |
| 81.02 | 448.27 |
| 76.05 | 431.69 |
| 71.81 | 449.09 |
| 82.13 | 448.79 |
| 74.27 | 460.21 |
| 71.32 | 479.28 |
| 74.59 | 483.11 |
| 84.44 | 450.75 |
| 43.39 | 437.97 |
| 87.2 | 459.76 |
| 77.11 | 457.75 |
| 82.81 | 469.33 |
| 85.67 | 433.28 |
| 95.58 | 444.64 |
| 74.46 | 463.1 |
| 90.02 | 460.91 |
| 81.93 | 479.35 |
| 86.29 | 449.23 |
| 85.43 | 474.51 |
| 71.66 | 435.02 |
| 71.33 | 435.45 |
| 67.72 | 452.38 |
| 84.23 | 480.41 |
| 74.44 | 478.96 |
| 71.48 | 468.87 |
| 56.3 | 434.01 |
| 68.13 | 466.36 |
| 68.35 | 435.28 |
| 85.23 | 486.46 |
| 79.78 | 468.19 |
| 98.98 | 468.37 |
| 72.87 | 474.19 |
| 90.93 | 440.32 |
| 72.94 | 485.32 |
| 72.3 | 464.27 |
| 77.74 | 479.25 |
| 78.29 | 430.4 |
| 69.1 | 447.49 |
| 55.79 | 438.23 |
| 75.09 | 492.09 |
| 88.98 | 475.36 |
| 64.26 | 452.56 |
| 25.89 | 427.84 |
| 59.18 | 433.95 |
| 67.48 | 435.27 |
| 89.85 | 454.62 |
| 50.62 | 472.17 |
| 83.97 | 452.42 |
| 96.51 | 472.17 |
| 80.09 | 481.83 |
| 84.02 | 458.78 |
| 61.97 | 447.5 |
| 88.51 | 463.4 |
| 81.83 | 473.57 |
| 90.63 | 433.72 |
| 73.37 | 431.85 |
| 77.5 | 433.47 |
| 57.46 | 432.84 |
| 51.91 | 436.6 |
| 79.14 | 490.23 |
| 69.24 | 477.16 |
| 41.85 | 441.06 |
| 95.1 | 440.86 |
| 74.53 | 477.94 |
| 64.85 | 474.47 |
| 57.07 | 470.67 |
| 62.9 | 447.31 |
| 72.21 | 466.8 |
| 65.99 | 430.91 |
| 73.67 | 434.75 |
| 88.59 | 469.52 |
| 61.19 | 438.9 |
| 49.37 | 429.56 |
| 48.65 | 432.92 |
| 56.18 | 442.87 |
| 71.56 | 466.59 |
| 87.46 | 479.61 |
| 70.02 | 471.08 |
| 61.2 | 433.37 |
| 60.54 | 443.92 |
| 71.82 | 443.5 |
| 44.8 | 439.89 |
| 67.32 | 434.66 |
| 78.77 | 487.57 |
| 96.02 | 464.64 |
| 90.56 | 470.92 |
| 83.19 | 444.39 |
| 68.45 | 442.48 |
| 99.19 | 449.61 |
| 88.11 | 435.02 |
| 79.29 | 458.67 |
| 87.76 | 461.74 |
| 82.56 | 438.31 |
| 79.24 | 462.38 |
| 67.24 | 460.56 |
| 58.98 | 439.22 |
| 84.22 | 444.64 |
| 89.12 | 430.34 |
| 50.07 | 430.46 |
| 98.86 | 456.79 |
| 95.79 | 468.82 |
| 70.06 | 448.51 |
| 41.71 | 470.77 |
| 86.55 | 465.74 |
| 71.97 | 430.21 |
| 96.4 | 449.23 |
| 91.73 | 461.89 |
| 91.62 | 445.72 |
| 59.14 | 466.13 |
| 92.56 | 448.71 |
| 83.71 | 469.25 |
| 55.43 | 450.56 |
| 74.91 | 464.46 |
| 89.41 | 471.13 |
| 69.81 | 461.52 |
| 86.01 | 451.09 |
| 86.05 | 431.51 |
| 80.98 | 469.8 |
| 63.62 | 442.28 |
| 64.16 | 458.67 |
| 75.63 | 462.4 |
| 80.21 | 453.54 |
| 59.7 | 444.38 |
| 47.6 | 440.52 |
| 63.78 | 433.62 |
| 89.37 | 481.96 |
| 70.55 | 452.75 |
| 84.42 | 481.28 |
| 80.62 | 439.03 |
| 52.56 | 435.75 |
| 83.26 | 436.03 |
| 70.64 | 445.6 |
| 89.95 | 462.65 |
| 51.53 | 438.66 |
| 84.83 | 447.32 |
| 59.68 | 484.55 |
| 97.88 | 476.8 |
| 85.03 | 480.34 |
| 47.38 | 440.63 |
| 48.08 | 459.48 |
| 79.65 | 490.78 |
| 83.39 | 483.56 |
| 82.18 | 429.38 |
| 51.71 | 440.27 |
| 77.33 | 445.34 |
| 72.81 | 447.43 |
| 84.26 | 439.91 |
| 73.23 | 459.27 |
| 79.73 | 478.89 |
| 62.32 | 466.7 |
| 78.58 | 463.5 |
| 79.88 | 436.21 |
| 65.87 | 443.94 |
| 80.28 | 439.63 |
| 71.33 | 460.95 |
| 70.33 | 448.69 |
| 57.73 | 444.63 |
| 99.46 | 473.51 |
| 68.61 | 462.56 |
| 95.91 | 451.76 |
| 80.42 | 491.81 |
| 51.01 | 429.52 |
| 74.08 | 437.9 |
| 80.73 | 467.54 |
| 54.71 | 449.97 |
| 73.75 | 436.62 |
| 88.43 | 477.68 |
| 74.66 | 447.26 |
| 70.04 | 439.76 |
| 74.64 | 437.49 |
| 85.11 | 455.14 |
| 82.97 | 485.5 |
| 55.59 | 444.1 |
| 49.9 | 432.33 |
| 89.99 | 471.23 |
| 91.61 | 463.89 |
| 82.95 | 445.54 |
| 92.62 | 446.09 |
| 59.64 | 445.12 |
| 63.0 | 443.31 |
| 85.38 | 484.16 |
| 85.23 | 477.76 |
| 52.08 | 430.28 |
| 38.05 | 446.48 |
| 71.98 | 481.03 |
| 90.66 | 466.07 |
| 83.14 | 447.47 |
| 65.22 | 455.93 |
| 91.67 | 479.62 |
| 66.04 | 455.06 |
| 78.19 | 475.06 |
| 54.47 | 438.89 |
| 67.26 | 432.7 |
| 72.56 | 452.6 |
| 82.15 | 451.75 |
| 86.32 | 430.66 |
| 88.17 | 491.9 |
| 72.78 | 439.82 |
| 69.58 | 460.73 |
| 69.86 | 449.7 |
| 65.37 | 439.42 |
| 52.37 | 439.84 |
| 79.63 | 485.86 |
| 83.14 | 458.1 |
| 88.25 | 479.92 |
| 55.85 | 458.29 |
| 52.55 | 489.45 |
| 62.74 | 434.0 |
| 90.08 | 431.24 |
| 54.5 | 439.5 |
| 49.88 | 467.46 |
| 48.96 | 429.27 |
| 86.77 | 452.1 |
| 96.66 | 472.41 |
| 70.84 | 442.14 |
| 83.83 | 441.0 |
| 68.22 | 463.07 |
| 82.22 | 445.71 |
| 87.9 | 483.16 |
| 66.83 | 440.45 |
| 85.36 | 481.83 |
| 60.9 | 467.6 |
| 83.51 | 450.88 |
| 52.96 | 425.5 |
| 73.02 | 451.87 |
| 43.11 | 428.94 |
| 61.41 | 439.86 |
| 65.32 | 433.44 |
| 93.68 | 438.23 |
| 42.39 | 436.95 |
| 68.18 | 470.19 |
| 89.18 | 484.66 |
| 64.79 | 430.81 |
| 43.48 | 433.37 |
| 80.4 | 453.02 |
| 72.43 | 453.5 |
| 80.0 | 463.09 |
| 45.65 | 464.56 |
| 63.02 | 452.12 |
| 74.39 | 470.9 |
| 57.77 | 450.89 |
| 76.8 | 445.04 |
| 67.39 | 444.72 |
| 73.96 | 460.38 |
| 72.75 | 446.8 |
| 71.69 | 465.05 |
| 84.98 | 484.13 |
| 87.68 | 488.27 |
| 50.36 | 447.09 |
| 68.11 | 452.02 |
| 66.27 | 455.55 |
| 89.72 | 480.99 |
| 61.07 | 467.68 |
| RH | PE |
|---|---|
| 73.17 | 463.26 |
| 59.08 | 444.37 |
| 92.14 | 488.56 |
| 76.64 | 446.48 |
| 96.62 | 473.9 |
| 58.77 | 443.67 |
| 75.24 | 467.35 |
| 66.43 | 478.42 |
| 41.25 | 475.98 |
| 70.72 | 477.5 |
| 75.04 | 453.02 |
| 64.22 | 453.99 |
| 84.15 | 440.29 |
| 61.83 | 451.28 |
| 87.59 | 433.99 |
| 43.08 | 462.19 |
| 48.84 | 467.54 |
| 77.51 | 477.2 |
| 63.59 | 459.85 |
| 55.28 | 464.3 |
| 66.26 | 468.27 |
| 64.77 | 495.24 |
| 83.31 | 483.8 |
| 47.19 | 443.61 |
| 54.93 | 436.06 |
| 74.62 | 443.25 |
| 72.52 | 464.16 |
| 88.44 | 475.52 |
| 92.28 | 484.41 |
| 41.85 | 437.89 |
| 44.28 | 445.11 |
| 64.58 | 438.86 |
| 63.25 | 440.98 |
| 78.61 | 436.65 |
| 44.51 | 444.26 |
| 89.46 | 465.86 |
| 74.52 | 444.37 |
| 88.86 | 450.69 |
| 75.51 | 469.02 |
| 78.64 | 448.86 |
| 76.65 | 447.14 |
| 80.44 | 469.18 |
| 79.89 | 482.8 |
| 88.28 | 476.7 |
| 84.6 | 474.99 |
| 42.69 | 444.22 |
| 78.41 | 461.33 |
| 61.07 | 448.06 |
| 50.0 | 474.6 |
| 77.29 | 473.05 |
| 43.66 | 432.06 |
| 83.8 | 467.41 |
| 66.47 | 430.12 |
| 93.09 | 473.62 |
| 80.52 | 471.81 |
| 68.99 | 442.99 |
| 57.27 | 442.77 |
| 95.53 | 491.49 |
| 71.72 | 447.46 |
| 57.88 | 446.11 |
| 63.34 | 442.44 |
| 48.07 | 446.22 |
| 91.87 | 471.49 |
| 87.27 | 463.5 |
| 64.4 | 440.01 |
| 43.4 | 441.03 |
| 72.24 | 452.68 |
| 90.22 | 474.91 |
| 74.0 | 478.77 |
| 71.85 | 434.2 |
| 86.62 | 437.91 |
| 97.41 | 477.61 |
| 84.44 | 431.65 |
| 81.55 | 430.57 |
| 75.66 | 481.09 |
| 79.41 | 445.56 |
| 58.91 | 475.74 |
| 90.06 | 435.12 |
| 79.0 | 446.15 |
| 69.47 | 436.64 |
| 51.47 | 436.69 |
| 83.13 | 468.75 |
| 40.33 | 466.6 |
| 81.69 | 465.48 |
| 94.55 | 441.34 |
| 91.81 | 441.83 |
| 63.62 | 464.7 |
| 49.35 | 437.99 |
| 69.61 | 459.12 |
| 38.75 | 429.69 |
| 90.17 | 459.8 |
| 81.24 | 433.63 |
| 48.46 | 442.84 |
| 76.72 | 485.13 |
| 51.16 | 459.12 |
| 76.34 | 445.31 |
| 67.3 | 480.8 |
| 52.38 | 432.55 |
| 76.44 | 443.86 |
| 91.55 | 449.77 |
| 71.9 | 470.71 |
| 80.05 | 452.17 |
| 63.77 | 478.29 |
| 62.26 | 428.54 |
| 89.04 | 478.27 |
| 58.02 | 439.58 |
| 81.82 | 457.32 |
| 91.14 | 475.51 |
| 88.92 | 439.66 |
| 84.83 | 471.99 |
| 91.76 | 479.81 |
| 86.56 | 434.78 |
| 57.21 | 446.58 |
| 54.25 | 437.76 |
| 63.8 | 459.36 |
| 33.71 | 462.28 |
| 67.25 | 464.33 |
| 60.11 | 444.36 |
| 74.55 | 438.64 |
| 67.34 | 470.49 |
| 42.75 | 455.13 |
| 55.2 | 450.22 |
| 83.61 | 440.43 |
| 88.78 | 482.98 |
| 100.12 | 460.44 |
| 64.52 | 444.97 |
| 51.41 | 433.94 |
| 85.78 | 439.73 |
| 75.41 | 434.48 |
| 81.63 | 442.33 |
| 51.92 | 457.67 |
| 70.12 | 454.66 |
| 53.83 | 432.21 |
| 77.23 | 457.66 |
| 65.67 | 435.21 |
| 71.18 | 448.22 |
| 81.96 | 475.51 |
| 79.54 | 446.53 |
| 47.09 | 441.3 |
| 57.69 | 433.54 |
| 78.89 | 472.52 |
| 85.29 | 474.77 |
| 40.13 | 435.1 |
| 77.06 | 450.74 |
| 67.38 | 442.7 |
| 62.44 | 426.56 |
| 77.43 | 463.71 |
| 58.77 | 447.06 |
| 67.72 | 452.27 |
| 42.14 | 445.78 |
| 84.16 | 438.65 |
| 89.79 | 480.15 |
| 67.21 | 447.19 |
| 72.14 | 443.04 |
| 97.49 | 488.81 |
| 87.74 | 455.75 |
| 96.3 | 455.86 |
| 61.25 | 457.68 |
| 88.38 | 479.11 |
| 74.77 | 432.84 |
| 68.18 | 448.37 |
| 77.2 | 447.06 |
| 49.54 | 443.53 |
| 92.22 | 445.21 |
| 33.65 | 441.7 |
| 64.59 | 450.93 |
| 100.09 | 451.44 |
| 68.04 | 441.29 |
| 48.94 | 458.85 |
| 74.47 | 481.46 |
| 81.02 | 467.19 |
| 71.17 | 461.54 |
| 53.85 | 439.08 |
| 70.67 | 467.22 |
| 59.36 | 468.8 |
| 57.17 | 426.93 |
| 70.29 | 474.65 |
| 83.37 | 468.97 |
| 87.36 | 433.97 |
| 100.09 | 450.53 |
| 68.78 | 444.51 |
| 70.98 | 469.03 |
| 75.68 | 466.56 |
| 47.49 | 457.57 |
| 71.99 | 440.13 |
| 66.55 | 433.24 |
| 74.73 | 452.55 |
| 64.78 | 443.29 |
| 75.13 | 431.76 |
| 56.38 | 454.97 |
| 94.35 | 456.7 |
| 86.55 | 486.03 |
| 82.95 | 472.79 |
| 88.42 | 452.03 |
| 85.61 | 443.41 |
| 58.39 | 441.93 |
| 74.28 | 432.64 |
| 87.85 | 480.25 |
| 83.5 | 466.68 |
| 65.24 | 494.39 |
| 75.01 | 454.72 |
| 84.52 | 448.71 |
| 80.52 | 469.76 |
| 75.14 | 450.71 |
| 75.75 | 444.01 |
| 76.72 | 453.2 |
| 85.47 | 450.87 |
| 57.95 | 441.73 |
| 78.32 | 465.09 |
| 52.2 | 447.28 |
| 93.69 | 491.16 |
| 75.74 | 450.98 |
| 67.56 | 446.3 |
| 69.46 | 436.48 |
| 74.58 | 460.84 |
| 53.23 | 442.56 |
| 88.72 | 467.3 |
| 96.16 | 479.13 |
| 68.26 | 441.15 |
| 86.39 | 445.52 |
| 85.34 | 475.4 |
| 72.64 | 469.3 |
| 97.82 | 463.57 |
| 77.22 | 445.32 |
| 80.59 | 461.03 |
| 46.91 | 466.74 |
| 57.76 | 444.04 |
| 53.09 | 434.01 |
| 84.31 | 465.23 |
| 71.58 | 440.6 |
| 92.97 | 466.74 |
| 74.55 | 433.48 |
| 78.96 | 473.59 |
| 64.44 | 474.81 |
| 68.23 | 454.75 |
| 70.81 | 452.94 |
| 61.66 | 435.83 |
| 77.76 | 482.19 |
| 69.49 | 466.66 |
| 96.26 | 462.59 |
| 55.74 | 447.82 |
| 95.61 | 462.73 |
| 84.75 | 447.98 |
| 75.3 | 462.72 |
| 67.5 | 442.42 |
| 80.92 | 444.69 |
| 79.23 | 466.7 |
| 81.1 | 453.84 |
| 32.8 | 436.92 |
| 84.31 | 486.37 |
| 46.15 | 440.43 |
| 53.96 | 446.82 |
| 59.83 | 484.91 |
| 75.3 | 437.76 |
| 42.53 | 438.91 |
| 70.58 | 464.19 |
| 91.69 | 442.19 |
| 63.55 | 446.86 |
| 61.51 | 457.15 |
| 69.55 | 482.57 |
| 98.08 | 476.03 |
| 79.34 | 428.89 |
| 81.28 | 472.7 |
| 78.99 | 445.6 |
| 80.38 | 464.78 |
| 51.16 | 440.42 |
| 72.17 | 428.41 |
| 75.39 | 438.5 |
| 68.91 | 438.28 |
| 96.38 | 476.29 |
| 70.54 | 448.46 |
| 45.8 | 438.99 |
| 57.95 | 471.8 |
| 81.89 | 471.81 |
| 69.32 | 449.82 |
| 59.14 | 442.14 |
| 81.54 | 441.46 |
| 85.81 | 477.62 |
| 65.41 | 446.76 |
| 81.15 | 472.52 |
| 95.87 | 471.58 |
| 90.24 | 440.85 |
| 75.13 | 431.37 |
| 88.22 | 437.33 |
| 81.48 | 469.22 |
| 89.84 | 471.11 |
| 43.57 | 439.17 |
| 63.16 | 445.33 |
| 57.14 | 473.71 |
| 77.76 | 452.66 |
| 90.56 | 440.99 |
| 60.98 | 467.42 |
| 70.31 | 444.14 |
| 74.05 | 457.17 |
| 75.42 | 467.87 |
| 82.25 | 442.04 |
| 67.95 | 471.36 |
| 100.09 | 460.7 |
| 47.28 | 431.33 |
| 72.41 | 432.6 |
| 77.67 | 447.61 |
| 63.7 | 443.87 |
| 79.77 | 446.87 |
| 93.84 | 465.74 |
| 84.95 | 447.86 |
| 70.16 | 447.65 |
| 84.24 | 437.87 |
| 73.32 | 483.51 |
| 86.17 | 479.65 |
| 65.43 | 455.16 |
| 94.59 | 431.91 |
| 86.8 | 470.68 |
| 58.18 | 429.28 |
| 89.66 | 450.81 |
| 87.39 | 437.73 |
| 36.35 | 460.21 |
| 79.62 | 442.86 |
| 50.52 | 482.99 |
| 51.96 | 440.0 |
| 74.73 | 478.48 |
| 78.33 | 455.28 |
| 85.19 | 436.94 |
| 83.13 | 461.06 |
| 53.49 | 438.28 |
| 88.86 | 472.61 |
| 60.89 | 426.85 |
| 61.14 | 470.18 |
| 68.29 | 455.38 |
| 57.62 | 428.32 |
| 83.63 | 480.35 |
| 78.1 | 455.56 |
| 66.34 | 447.66 |
| 79.02 | 443.06 |
| 68.96 | 452.43 |
| 71.13 | 477.81 |
| 87.01 | 431.66 |
| 74.3 | 431.8 |
| 77.62 | 446.67 |
| 59.56 | 445.26 |
| 41.66 | 425.72 |
| 73.27 | 430.58 |
| 77.16 | 439.86 |
| 67.02 | 441.11 |
| 52.8 | 434.72 |
| 39.04 | 434.01 |
| 65.47 | 475.64 |
| 74.32 | 460.44 |
| 69.22 | 436.4 |
| 93.88 | 461.03 |
| 69.83 | 479.08 |
| 84.11 | 435.76 |
| 78.65 | 460.14 |
| 69.31 | 442.2 |
| 70.3 | 447.69 |
| 68.23 | 431.15 |
| 71.76 | 445.0 |
| 85.88 | 431.59 |
| 71.09 | 467.22 |
| 52.67 | 445.33 |
| 89.68 | 470.57 |
| 73.66 | 473.77 |
| 58.94 | 447.67 |
| 87.05 | 474.29 |
| 67.0 | 437.14 |
| 43.18 | 432.56 |
| 80.62 | 459.14 |
| 59.72 | 446.19 |
| 72.1 | 428.1 |
| 69.15 | 468.46 |
| 55.66 | 435.02 |
| 61.19 | 445.52 |
| 74.62 | 462.69 |
| 73.35 | 455.75 |
| 68.85 | 463.74 |
| 39.89 | 439.79 |
| 53.16 | 443.26 |
| 52.97 | 432.04 |
| 79.87 | 465.86 |
| 84.09 | 465.6 |
| 100.15 | 469.43 |
| 79.77 | 440.75 |
| 88.99 | 481.32 |
| 76.14 | 479.87 |
| 69.13 | 458.59 |
| 93.03 | 438.62 |
| 77.92 | 445.59 |
| 74.89 | 481.87 |
| 88.7 | 475.01 |
| 62.94 | 436.54 |
| 89.62 | 456.63 |
| 81.04 | 451.69 |
| 94.53 | 463.04 |
| 64.02 | 446.1 |
| 70.57 | 438.67 |
| 70.32 | 466.88 |
| 84.86 | 444.6 |
| 81.41 | 440.26 |
| 89.45 | 483.92 |
| 82.71 | 475.19 |
| 93.93 | 479.24 |
| 70.6 | 434.92 |
| 87.68 | 454.16 |
| 87.58 | 447.58 |
| 74.4 | 467.9 |
| 67.35 | 426.29 |
| 63.61 | 447.02 |
| 76.89 | 455.85 |
| 78.08 | 476.46 |
| 69.17 | 437.48 |
| 53.31 | 452.77 |
| 93.32 | 491.54 |
| 42.47 | 438.41 |
| 82.58 | 476.1 |
| 94.59 | 464.58 |
| 86.31 | 467.74 |
| 72.57 | 442.12 |
| 80.76 | 453.34 |
| 71.93 | 425.29 |
| 47.54 | 449.63 |
| 95.72 | 462.88 |
| 77.03 | 464.67 |
| 80.49 | 489.96 |
| 77.67 | 482.38 |
| 78.72 | 437.95 |
| 58.77 | 429.2 |
| 74.8 | 453.34 |
| 51.34 | 442.47 |
| 90.41 | 462.6 |
| 91.1 | 478.79 |
| 62.57 | 456.11 |
| 84.27 | 450.33 |
| 42.93 | 434.83 |
| 40.96 | 433.43 |
| 76.53 | 456.02 |
| 69.74 | 485.23 |
| 74.99 | 473.57 |
| 70.45 | 469.94 |
| 91.49 | 452.07 |
| 88.97 | 475.32 |
| 89.13 | 480.69 |
| 46.52 | 444.01 |
| 60.55 | 465.17 |
| 88.71 | 480.61 |
| 89.15 | 476.04 |
| 83.02 | 441.76 |
| 75.19 | 428.24 |
| 87.35 | 444.77 |
| 85.66 | 463.1 |
| 91.66 | 470.5 |
| 63.47 | 431.0 |
| 72.25 | 430.68 |
| 70.58 | 436.42 |
| 60.1 | 452.33 |
| 89.29 | 440.16 |
| 67.43 | 435.75 |
| 67.58 | 449.74 |
| 70.8 | 430.73 |
| 63.62 | 432.75 |
| 66.68 | 446.79 |
| 90.76 | 486.35 |
| 75.34 | 453.18 |
| 59.77 | 458.31 |
| 80.79 | 480.26 |
| 74.1 | 448.65 |
| 41.34 | 458.41 |
| 58.78 | 435.39 |
| 97.78 | 450.21 |
| 74.85 | 459.59 |
| 69.84 | 445.84 |
| 75.36 | 441.08 |
| 85.8 | 467.33 |
| 90.11 | 444.19 |
| 61.63 | 432.96 |
| 44.76 | 438.09 |
| 89.7 | 467.9 |
| 72.51 | 475.72 |
| 74.98 | 477.51 |
| 79.59 | 435.13 |
| 78.42 | 477.9 |
| 61.23 | 457.26 |
| 47.56 | 467.53 |
| 93.06 | 465.15 |
| 89.65 | 474.28 |
| 50.5 | 444.49 |
| 44.84 | 452.84 |
| 85.32 | 435.38 |
| 82.94 | 433.57 |
| 67.26 | 435.27 |
| 79.05 | 468.49 |
| 62.03 | 433.07 |
| 94.36 | 430.63 |
| 60.02 | 440.74 |
| 95.46 | 474.49 |
| 84.92 | 449.74 |
| 62.8 | 436.73 |
| 90.81 | 434.58 |
| 80.9 | 473.93 |
| 55.84 | 435.99 |
| 75.3 | 466.83 |
| 37.34 | 427.22 |
| 79.5 | 444.07 |
| 87.29 | 469.57 |
| 81.5 | 459.89 |
| 76.42 | 479.59 |
| 75.75 | 440.92 |
| 84.95 | 480.87 |
| 62.37 | 441.9 |
| 49.25 | 430.2 |
| 74.16 | 465.16 |
| 90.55 | 471.32 |
| 94.28 | 485.43 |
| 63.31 | 495.35 |
| 78.9 | 449.12 |
| 90.97 | 480.53 |
| 87.34 | 457.07 |
| 80.8 | 443.67 |
| 90.95 | 477.52 |
| 69.97 | 472.95 |
| 89.45 | 472.54 |
| 76.08 | 469.17 |
| 83.35 | 435.21 |
| 92.16 | 477.78 |
| 58.42 | 475.89 |
| 85.06 | 483.9 |
| 88.91 | 476.2 |
| 83.33 | 462.16 |
| 88.49 | 471.05 |
| 96.88 | 484.71 |
| 73.86 | 446.34 |
| 65.17 | 469.02 |
| 69.41 | 432.12 |
| 81.23 | 467.28 |
| 54.07 | 429.66 |
| 89.17 | 469.49 |
| 78.85 | 485.87 |
| 84.44 | 481.95 |
| 83.02 | 479.03 |
| 90.66 | 434.5 |
| 75.29 | 464.9 |
| 82.6 | 452.71 |
| 45.4 | 429.74 |
| 66.33 | 457.09 |
| 45.33 | 446.77 |
| 67.12 | 460.76 |
| 84.14 | 471.95 |
| 80.81 | 453.29 |
| 49.13 | 441.61 |
| 87.29 | 464.73 |
| 52.95 | 464.68 |
| 68.92 | 430.59 |
| 85.21 | 438.01 |
| 88.56 | 479.08 |
| 55.09 | 436.39 |
| 48.64 | 447.07 |
| 87.85 | 479.91 |
| 87.42 | 489.05 |
| 62.75 | 463.17 |
| 94.86 | 471.26 |
| 63.54 | 480.49 |
| 69.46 | 473.78 |
| 74.66 | 455.5 |
| 80.57 | 446.27 |
| 84.7 | 482.2 |
| 72.6 | 452.48 |
| 52.63 | 464.48 |
| 82.01 | 438.1 |
| 75.22 | 445.6 |
| 51.05 | 442.43 |
| 80.1 | 436.67 |
| 81.58 | 466.56 |
| 65.94 | 457.29 |
| 86.74 | 487.03 |
| 62.57 | 464.93 |
| 57.37 | 466.0 |
| 88.91 | 469.52 |
| 72.26 | 428.88 |
| 74.98 | 474.3 |
| 71.19 | 461.06 |
| 62.82 | 465.57 |
| 55.31 | 467.67 |
| 71.57 | 466.99 |
| 59.16 | 463.72 |
| 40.8 | 443.78 |
| 67.63 | 445.23 |
| 97.2 | 464.43 |
| 91.16 | 484.36 |
| 64.81 | 442.16 |
| 85.61 | 464.11 |
| 93.42 | 462.48 |
| 77.36 | 477.49 |
| 67.03 | 437.04 |
| 89.45 | 457.09 |
| 54.84 | 450.6 |
| 53.48 | 465.78 |
| 54.47 | 427.1 |
| 43.36 | 459.81 |
| 48.92 | 447.36 |
| 81.22 | 488.92 |
| 60.35 | 433.36 |
| 75.98 | 483.35 |
| 74.24 | 469.53 |
| 85.21 | 476.96 |
| 68.46 | 440.75 |
| 78.31 | 462.55 |
| 89.25 | 448.04 |
| 68.57 | 455.24 |
| 67.38 | 494.75 |
| 53.6 | 444.58 |
| 91.69 | 484.82 |
| 78.21 | 442.9 |
| 94.19 | 485.46 |
| 37.19 | 457.81 |
| 83.53 | 481.92 |
| 79.45 | 443.23 |
| 99.9 | 474.29 |
| 50.39 | 430.46 |
| 74.33 | 455.71 |
| 83.66 | 438.34 |
| 89.48 | 485.83 |
| 64.59 | 452.82 |
| 75.68 | 435.04 |
| 64.18 | 451.21 |
| 70.09 | 465.81 |
| 70.58 | 458.42 |
| 99.28 | 470.22 |
| 81.89 | 449.24 |
| 87.99 | 471.43 |
| 88.21 | 473.26 |
| 91.29 | 452.82 |
| 52.72 | 432.69 |
| 67.5 | 444.13 |
| 92.05 | 467.21 |
| 63.23 | 445.98 |
| 90.88 | 436.91 |
| 74.91 | 455.01 |
| 85.39 | 437.11 |
| 96.98 | 477.06 |
| 56.28 | 441.71 |
| 65.62 | 495.76 |
| 55.32 | 445.63 |
| 88.88 | 464.72 |
| 39.49 | 438.03 |
| 54.59 | 434.78 |
| 57.19 | 444.67 |
| 45.06 | 452.24 |
| 40.62 | 450.92 |
| 90.21 | 436.53 |
| 90.91 | 435.53 |
| 74.96 | 440.01 |
| 54.21 | 443.1 |
| 63.62 | 427.49 |
| 50.04 | 436.25 |
| 51.23 | 440.74 |
| 82.71 | 443.54 |
| 92.04 | 459.42 |
| 90.67 | 439.66 |
| 91.14 | 464.15 |
| 70.43 | 459.1 |
| 66.63 | 455.68 |
| 86.95 | 469.08 |
| 96.69 | 478.02 |
| 70.88 | 456.8 |
| 47.14 | 441.13 |
| 63.36 | 463.88 |
| 60.58 | 430.45 |
| 54.3 | 449.18 |
| 65.09 | 447.89 |
| 48.16 | 431.59 |
| 81.51 | 447.5 |
| 68.57 | 475.58 |
| 73.16 | 453.24 |
| 80.88 | 446.4 |
| 85.4 | 476.81 |
| 75.77 | 474.1 |
| 75.76 | 450.71 |
| 70.21 | 433.62 |
| 55.53 | 465.14 |
| 80.48 | 445.18 |
| 72.41 | 474.12 |
| 83.25 | 483.91 |
| 82.12 | 486.68 |
| 94.75 | 464.98 |
| 95.79 | 481.4 |
| 86.02 | 479.2 |
| 78.29 | 463.86 |
| 82.23 | 472.3 |
| 52.86 | 446.51 |
| 60.66 | 437.71 |
| 62.77 | 458.94 |
| 65.54 | 437.91 |
| 95.4 | 490.76 |
| 51.78 | 439.66 |
| 63.62 | 463.27 |
| 83.09 | 473.99 |
| 61.81 | 433.38 |
| 68.24 | 459.01 |
| 72.41 | 471.44 |
| 79.64 | 471.91 |
| 66.95 | 465.15 |
| 91.98 | 446.66 |
| 64.96 | 438.15 |
| 88.93 | 447.14 |
| 85.62 | 472.32 |
| 84.0 | 441.68 |
| 89.41 | 440.04 |
| 67.4 | 444.82 |
| 76.75 | 457.26 |
| 89.06 | 428.83 |
| 64.02 | 449.07 |
| 64.12 | 435.21 |
| 81.22 | 471.03 |
| 100.13 | 465.56 |
| 58.07 | 442.83 |
| 63.42 | 460.3 |
| 83.32 | 474.25 |
| 74.28 | 477.97 |
| 71.91 | 472.16 |
| 85.8 | 456.08 |
| 55.58 | 452.41 |
| 69.7 | 463.71 |
| 63.79 | 433.72 |
| 86.59 | 456.4 |
| 48.28 | 448.43 |
| 80.42 | 481.6 |
| 98.58 | 457.07 |
| 72.83 | 451.0 |
| 56.07 | 440.28 |
| 67.13 | 437.47 |
| 84.49 | 443.57 |
| 68.37 | 426.6 |
| 86.07 | 470.87 |
| 83.82 | 478.37 |
| 74.86 | 453.92 |
| 53.52 | 470.22 |
| 80.61 | 434.54 |
| 43.56 | 442.89 |
| 89.35 | 479.03 |
| 97.28 | 476.06 |
| 80.49 | 473.88 |
| 88.9 | 451.75 |
| 49.7 | 439.2 |
| 68.64 | 439.7 |
| 98.58 | 463.6 |
| 82.12 | 447.47 |
| 78.32 | 447.92 |
| 86.04 | 471.08 |
| 91.36 | 437.55 |
| 81.02 | 448.27 |
| 76.05 | 431.69 |
| 71.81 | 449.09 |
| 82.13 | 448.79 |
| 74.27 | 460.21 |
| 71.32 | 479.28 |
| 74.59 | 483.11 |
| 84.44 | 450.75 |
| 43.39 | 437.97 |
| 87.2 | 459.76 |
| 77.11 | 457.75 |
| 82.81 | 469.33 |
| 85.67 | 433.28 |
| 95.58 | 444.64 |
| 74.46 | 463.1 |
| 90.02 | 460.91 |
| 81.93 | 479.35 |
| 86.29 | 449.23 |
| 85.43 | 474.51 |
| 71.66 | 435.02 |
| 71.33 | 435.45 |
| 67.72 | 452.38 |
| 84.23 | 480.41 |
| 74.44 | 478.96 |
| 71.48 | 468.87 |
| 56.3 | 434.01 |
| 68.13 | 466.36 |
| 68.35 | 435.28 |
| 85.23 | 486.46 |
| 79.78 | 468.19 |
| 98.98 | 468.37 |
| 72.87 | 474.19 |
| 90.93 | 440.32 |
| 72.94 | 485.32 |
| 72.3 | 464.27 |
| 77.74 | 479.25 |
| 78.29 | 430.4 |
| 69.1 | 447.49 |
| 55.79 | 438.23 |
| 75.09 | 492.09 |
| 88.98 | 475.36 |
| 64.26 | 452.56 |
| 25.89 | 427.84 |
| 59.18 | 433.95 |
| 67.48 | 435.27 |
| 89.85 | 454.62 |
| 50.62 | 472.17 |
| 83.97 | 452.42 |
| 96.51 | 472.17 |
| 80.09 | 481.83 |
| 84.02 | 458.78 |
| 61.97 | 447.5 |
| 88.51 | 463.4 |
| 81.83 | 473.57 |
| 90.63 | 433.72 |
| 73.37 | 431.85 |
| 77.5 | 433.47 |
| 57.46 | 432.84 |
| 51.91 | 436.6 |
| 79.14 | 490.23 |
| 69.24 | 477.16 |
| 41.85 | 441.06 |
| 95.1 | 440.86 |
| 74.53 | 477.94 |
| 64.85 | 474.47 |
| 57.07 | 470.67 |
| 62.9 | 447.31 |
| 72.21 | 466.8 |
| 65.99 | 430.91 |
| 73.67 | 434.75 |
| 88.59 | 469.52 |
| 61.19 | 438.9 |
| 49.37 | 429.56 |
| 48.65 | 432.92 |
| 56.18 | 442.87 |
| 71.56 | 466.59 |
| 87.46 | 479.61 |
| 70.02 | 471.08 |
| 61.2 | 433.37 |
| 60.54 | 443.92 |
| 71.82 | 443.5 |
| 44.8 | 439.89 |
| 67.32 | 434.66 |
| 78.77 | 487.57 |
| 96.02 | 464.64 |
| 90.56 | 470.92 |
| 83.19 | 444.39 |
| 68.45 | 442.48 |
| 99.19 | 449.61 |
| 88.11 | 435.02 |
| 79.29 | 458.67 |
| 87.76 | 461.74 |
| 82.56 | 438.31 |
| 79.24 | 462.38 |
| 67.24 | 460.56 |
| 58.98 | 439.22 |
| 84.22 | 444.64 |
| 89.12 | 430.34 |
| 50.07 | 430.46 |
| 98.86 | 456.79 |
| 95.79 | 468.82 |
| 70.06 | 448.51 |
| 41.71 | 470.77 |
| 86.55 | 465.74 |
| 71.97 | 430.21 |
| 96.4 | 449.23 |
| 91.73 | 461.89 |
| 91.62 | 445.72 |
| 59.14 | 466.13 |
| 92.56 | 448.71 |
| 83.71 | 469.25 |
| 55.43 | 450.56 |
| 74.91 | 464.46 |
| 89.41 | 471.13 |
| 69.81 | 461.52 |
| 86.01 | 451.09 |
| 86.05 | 431.51 |
| 80.98 | 469.8 |
| 63.62 | 442.28 |
| 64.16 | 458.67 |
| 75.63 | 462.4 |
| 80.21 | 453.54 |
| 59.7 | 444.38 |
| 47.6 | 440.52 |
| 63.78 | 433.62 |
| 89.37 | 481.96 |
| 70.55 | 452.75 |
| 84.42 | 481.28 |
| 80.62 | 439.03 |
| 52.56 | 435.75 |
| 83.26 | 436.03 |
| 70.64 | 445.6 |
| 89.95 | 462.65 |
| 51.53 | 438.66 |
| 84.83 | 447.32 |
| 59.68 | 484.55 |
| 97.88 | 476.8 |
| 85.03 | 480.34 |
| 47.38 | 440.63 |
| 48.08 | 459.48 |
| 79.65 | 490.78 |
| 83.39 | 483.56 |
| 82.18 | 429.38 |
| 51.71 | 440.27 |
| 77.33 | 445.34 |
| 72.81 | 447.43 |
| 84.26 | 439.91 |
| 73.23 | 459.27 |
| 79.73 | 478.89 |
| 62.32 | 466.7 |
| 78.58 | 463.5 |
| 79.88 | 436.21 |
| 65.87 | 443.94 |
| 80.28 | 439.63 |
| 71.33 | 460.95 |
| 70.33 | 448.69 |
| 57.73 | 444.63 |
| 99.46 | 473.51 |
| 68.61 | 462.56 |
| 95.91 | 451.76 |
| 80.42 | 491.81 |
| 51.01 | 429.52 |
| 74.08 | 437.9 |
| 80.73 | 467.54 |
| 54.71 | 449.97 |
| 73.75 | 436.62 |
| 88.43 | 477.68 |
| 74.66 | 447.26 |
| 70.04 | 439.76 |
| 74.64 | 437.49 |
| 85.11 | 455.14 |
| 82.97 | 485.5 |
| 55.59 | 444.1 |
| 49.9 | 432.33 |
| 89.99 | 471.23 |
| 91.61 | 463.89 |
| 82.95 | 445.54 |
| 92.62 | 446.09 |
| 59.64 | 445.12 |
| 63.0 | 443.31 |
| 85.38 | 484.16 |
| 85.23 | 477.76 |
| 52.08 | 430.28 |
| 38.05 | 446.48 |
| 71.98 | 481.03 |
| 90.66 | 466.07 |
| 83.14 | 447.47 |
| 65.22 | 455.93 |
| 91.67 | 479.62 |
| 66.04 | 455.06 |
| 78.19 | 475.06 |
| 54.47 | 438.89 |
| 67.26 | 432.7 |
| 72.56 | 452.6 |
| 82.15 | 451.75 |
| 86.32 | 430.66 |
| 88.17 | 491.9 |
| 72.78 | 439.82 |
| 69.58 | 460.73 |
| 69.86 | 449.7 |
| 65.37 | 439.42 |
| 52.37 | 439.84 |
| 79.63 | 485.86 |
| 83.14 | 458.1 |
| 88.25 | 479.92 |
| 55.85 | 458.29 |
| 52.55 | 489.45 |
| 62.74 | 434.0 |
| 90.08 | 431.24 |
| 54.5 | 439.5 |
| 49.88 | 467.46 |
| 48.96 | 429.27 |
| 86.77 | 452.1 |
| 96.66 | 472.41 |
| 70.84 | 442.14 |
| 83.83 | 441.0 |
| 68.22 | 463.07 |
| 82.22 | 445.71 |
| 87.9 | 483.16 |
| 66.83 | 440.45 |
| 85.36 | 481.83 |
| 60.9 | 467.6 |
| 83.51 | 450.88 |
| 52.96 | 425.5 |
| 73.02 | 451.87 |
| 43.11 | 428.94 |
| 61.41 | 439.86 |
| 65.32 | 433.44 |
| 93.68 | 438.23 |
| 42.39 | 436.95 |
| 68.18 | 470.19 |
| 89.18 | 484.66 |
| 64.79 | 430.81 |
| 43.48 | 433.37 |
| 80.4 | 453.02 |
| 72.43 | 453.5 |
| 80.0 | 463.09 |
| 45.65 | 464.56 |
| 63.02 | 452.12 |
| 74.39 | 470.9 |
| 57.77 | 450.89 |
| 76.8 | 445.04 |
| 67.39 | 444.72 |
| 73.96 | 460.38 |
| 72.75 | 446.8 |
| 71.69 | 465.05 |
| 84.98 | 484.13 |
| 87.68 | 488.27 |
| 50.36 | 447.09 |
| 68.11 | 452.02 |
| 66.27 | 455.55 |
| 89.72 | 480.99 |
| 61.07 | 467.68 |
| AT | V | AP | RH | PE |
|---|---|---|---|---|
| 14.96 | 41.76 | 1024.07 | 73.17 | 463.26 |
| 25.18 | 62.96 | 1020.04 | 59.08 | 444.37 |
| 5.11 | 39.4 | 1012.16 | 92.14 | 488.56 |
| 20.86 | 57.32 | 1010.24 | 76.64 | 446.48 |
| 10.82 | 37.5 | 1009.23 | 96.62 | 473.9 |
| 26.27 | 59.44 | 1012.23 | 58.77 | 443.67 |
| 15.89 | 43.96 | 1014.02 | 75.24 | 467.35 |
| 9.48 | 44.71 | 1019.12 | 66.43 | 478.42 |
| 14.64 | 45.0 | 1021.78 | 41.25 | 475.98 |
| 11.74 | 43.56 | 1015.14 | 70.72 | 477.5 |
| 17.99 | 43.72 | 1008.64 | 75.04 | 453.02 |
| 20.14 | 46.93 | 1014.66 | 64.22 | 453.99 |
| 24.34 | 73.5 | 1011.31 | 84.15 | 440.29 |
| 25.71 | 58.59 | 1012.77 | 61.83 | 451.28 |
| 26.19 | 69.34 | 1009.48 | 87.59 | 433.99 |
| 21.42 | 43.79 | 1015.76 | 43.08 | 462.19 |
| 18.21 | 45.0 | 1022.86 | 48.84 | 467.54 |
| 11.04 | 41.74 | 1022.6 | 77.51 | 477.2 |
| 14.45 | 52.75 | 1023.97 | 63.59 | 459.85 |
| 13.97 | 38.47 | 1015.15 | 55.28 | 464.3 |
| 17.76 | 42.42 | 1009.09 | 66.26 | 468.27 |
| 5.41 | 40.07 | 1019.16 | 64.77 | 495.24 |
| 7.76 | 42.28 | 1008.52 | 83.31 | 483.8 |
| 27.23 | 63.9 | 1014.3 | 47.19 | 443.61 |
| 27.36 | 48.6 | 1003.18 | 54.93 | 436.06 |
| 27.47 | 70.72 | 1009.97 | 74.62 | 443.25 |
| 14.6 | 39.31 | 1011.11 | 72.52 | 464.16 |
| 7.91 | 39.96 | 1023.57 | 88.44 | 475.52 |
| 5.81 | 35.79 | 1012.14 | 92.28 | 484.41 |
| 30.53 | 65.18 | 1012.69 | 41.85 | 437.89 |
| 23.87 | 63.94 | 1019.02 | 44.28 | 445.11 |
| 26.09 | 58.41 | 1013.64 | 64.58 | 438.86 |
| 29.27 | 66.85 | 1011.11 | 63.25 | 440.98 |
| 27.38 | 74.16 | 1010.08 | 78.61 | 436.65 |
| 24.81 | 63.94 | 1018.76 | 44.51 | 444.26 |
| 12.75 | 44.03 | 1007.29 | 89.46 | 465.86 |
| 24.66 | 63.73 | 1011.4 | 74.52 | 444.37 |
| 16.38 | 47.45 | 1010.08 | 88.86 | 450.69 |
| 13.91 | 39.35 | 1014.69 | 75.51 | 469.02 |
| 23.18 | 51.3 | 1012.04 | 78.64 | 448.86 |
| 22.47 | 47.45 | 1007.62 | 76.65 | 447.14 |
| 13.39 | 44.85 | 1017.24 | 80.44 | 469.18 |
| 9.28 | 41.54 | 1018.33 | 79.89 | 482.8 |
| 11.82 | 42.86 | 1014.12 | 88.28 | 476.7 |
| 10.27 | 40.64 | 1020.63 | 84.6 | 474.99 |
| 22.92 | 63.94 | 1019.28 | 42.69 | 444.22 |
| 16.0 | 37.87 | 1020.24 | 78.41 | 461.33 |
| 21.22 | 43.43 | 1010.96 | 61.07 | 448.06 |
| 13.46 | 44.71 | 1014.51 | 50.0 | 474.6 |
| 9.39 | 40.11 | 1029.14 | 77.29 | 473.05 |
| 31.07 | 73.5 | 1010.58 | 43.66 | 432.06 |
| 12.82 | 38.62 | 1018.71 | 83.8 | 467.41 |
| 32.57 | 78.92 | 1011.6 | 66.47 | 430.12 |
| 8.11 | 42.18 | 1014.82 | 93.09 | 473.62 |
| 13.92 | 39.39 | 1012.94 | 80.52 | 471.81 |
| 23.04 | 59.43 | 1010.23 | 68.99 | 442.99 |
| 27.31 | 64.44 | 1014.65 | 57.27 | 442.77 |
| 5.91 | 39.33 | 1010.18 | 95.53 | 491.49 |
| 25.26 | 61.08 | 1013.68 | 71.72 | 447.46 |
| 27.97 | 58.84 | 1002.25 | 57.88 | 446.11 |
| 26.08 | 52.3 | 1007.03 | 63.34 | 442.44 |
| 29.01 | 65.71 | 1013.61 | 48.07 | 446.22 |
| 12.18 | 40.1 | 1016.67 | 91.87 | 471.49 |
| 13.76 | 45.87 | 1008.89 | 87.27 | 463.5 |
| 25.5 | 58.79 | 1016.02 | 64.4 | 440.01 |
| 28.26 | 65.34 | 1014.56 | 43.4 | 441.03 |
| 21.39 | 62.96 | 1019.49 | 72.24 | 452.68 |
| 7.26 | 40.69 | 1020.43 | 90.22 | 474.91 |
| 10.54 | 34.03 | 1018.71 | 74.0 | 478.77 |
| 27.71 | 74.34 | 998.14 | 71.85 | 434.2 |
| 23.11 | 68.3 | 1017.83 | 86.62 | 437.91 |
| 7.51 | 41.01 | 1024.61 | 97.41 | 477.61 |
| 26.46 | 74.67 | 1016.65 | 84.44 | 431.65 |
| 29.34 | 74.34 | 998.58 | 81.55 | 430.57 |
| 10.32 | 42.28 | 1008.82 | 75.66 | 481.09 |
| 22.74 | 61.02 | 1009.56 | 79.41 | 445.56 |
| 13.48 | 39.85 | 1012.71 | 58.91 | 475.74 |
| 25.52 | 69.75 | 1010.36 | 90.06 | 435.12 |
| 21.58 | 67.25 | 1017.39 | 79.0 | 446.15 |
| 27.66 | 76.86 | 1001.31 | 69.47 | 436.64 |
| 26.96 | 69.45 | 1013.89 | 51.47 | 436.69 |
| 12.29 | 42.18 | 1016.53 | 83.13 | 468.75 |
| 15.86 | 43.02 | 1012.18 | 40.33 | 466.6 |
| 13.87 | 45.08 | 1024.42 | 81.69 | 465.48 |
| 24.09 | 73.68 | 1014.93 | 94.55 | 441.34 |
| 20.45 | 69.45 | 1012.53 | 91.81 | 441.83 |
| 15.07 | 39.3 | 1019.0 | 63.62 | 464.7 |
| 32.72 | 69.75 | 1009.6 | 49.35 | 437.99 |
| 18.23 | 58.96 | 1015.55 | 69.61 | 459.12 |
| 35.56 | 68.94 | 1006.56 | 38.75 | 429.69 |
| 18.36 | 51.43 | 1010.57 | 90.17 | 459.8 |
| 26.35 | 64.05 | 1009.81 | 81.24 | 433.63 |
| 25.92 | 60.95 | 1014.62 | 48.46 | 442.84 |
| 8.01 | 41.66 | 1014.49 | 76.72 | 485.13 |
| 19.63 | 52.72 | 1025.09 | 51.16 | 459.12 |
| 20.02 | 67.32 | 1012.05 | 76.34 | 445.31 |
| 10.08 | 40.72 | 1022.7 | 67.3 | 480.8 |
| 27.23 | 66.48 | 1005.23 | 52.38 | 432.55 |
| 23.37 | 63.77 | 1013.42 | 76.44 | 443.86 |
| 18.74 | 59.21 | 1018.3 | 91.55 | 449.77 |
| 14.81 | 43.69 | 1017.19 | 71.9 | 470.71 |
| 23.1 | 51.3 | 1011.93 | 80.05 | 452.17 |
| 10.72 | 41.38 | 1021.6 | 63.77 | 478.29 |
| 29.46 | 71.94 | 1006.96 | 62.26 | 428.54 |
| 8.1 | 40.64 | 1020.66 | 89.04 | 478.27 |
| 27.29 | 62.66 | 1007.63 | 58.02 | 439.58 |
| 17.1 | 49.69 | 1005.53 | 81.82 | 457.32 |
| 11.49 | 44.2 | 1018.79 | 91.14 | 475.51 |
| 23.69 | 65.59 | 1010.85 | 88.92 | 439.66 |
| 13.51 | 40.89 | 1011.03 | 84.83 | 471.99 |
| 9.64 | 39.35 | 1015.1 | 91.76 | 479.81 |
| 25.65 | 78.92 | 1010.83 | 86.56 | 434.78 |
| 21.59 | 61.87 | 1011.18 | 57.21 | 446.58 |
| 27.98 | 58.33 | 1013.92 | 54.25 | 437.76 |
| 18.8 | 39.72 | 1001.24 | 63.8 | 459.36 |
| 18.28 | 44.71 | 1016.99 | 33.71 | 462.28 |
| 13.55 | 43.48 | 1016.08 | 67.25 | 464.33 |
| 22.99 | 46.21 | 1010.71 | 60.11 | 444.36 |
| 23.94 | 59.39 | 1014.32 | 74.55 | 438.64 |
| 13.74 | 34.03 | 1018.69 | 67.34 | 470.49 |
| 21.3 | 41.1 | 1001.86 | 42.75 | 455.13 |
| 27.54 | 66.93 | 1017.06 | 55.2 | 450.22 |
| 24.81 | 63.73 | 1009.34 | 83.61 | 440.43 |
| 4.97 | 42.85 | 1014.02 | 88.78 | 482.98 |
| 15.22 | 50.88 | 1014.19 | 100.12 | 460.44 |
| 23.88 | 54.2 | 1012.81 | 64.52 | 444.97 |
| 33.01 | 68.67 | 1005.2 | 51.41 | 433.94 |
| 25.98 | 73.18 | 1012.28 | 85.78 | 439.73 |
| 28.18 | 73.88 | 1005.89 | 75.41 | 434.48 |
| 21.67 | 60.84 | 1017.93 | 81.63 | 442.33 |
| 17.67 | 45.09 | 1014.26 | 51.92 | 457.67 |
| 21.37 | 57.76 | 1018.8 | 70.12 | 454.66 |
| 28.69 | 67.25 | 1017.71 | 53.83 | 432.21 |
| 16.61 | 43.77 | 1012.25 | 77.23 | 457.66 |
| 27.91 | 63.76 | 1010.27 | 65.67 | 435.21 |
| 20.97 | 47.43 | 1007.64 | 71.18 | 448.22 |
| 10.8 | 41.66 | 1013.79 | 81.96 | 475.51 |
| 20.61 | 62.91 | 1013.24 | 79.54 | 446.53 |
| 25.45 | 57.32 | 1011.7 | 47.09 | 441.3 |
| 30.16 | 69.34 | 1007.67 | 57.69 | 433.54 |
| 4.99 | 39.04 | 1020.45 | 78.89 | 472.52 |
| 10.51 | 44.78 | 1012.59 | 85.29 | 474.77 |
| 33.79 | 69.05 | 1001.62 | 40.13 | 435.1 |
| 21.34 | 59.8 | 1016.92 | 77.06 | 450.74 |
| 23.4 | 65.06 | 1014.32 | 67.38 | 442.7 |
| 32.21 | 68.14 | 1003.34 | 62.44 | 426.56 |
| 14.26 | 42.32 | 1016.0 | 77.43 | 463.71 |
| 27.71 | 66.93 | 1016.85 | 58.77 | 447.06 |
| 21.95 | 57.76 | 1018.02 | 67.72 | 452.27 |
| 25.76 | 63.94 | 1018.49 | 42.14 | 445.78 |
| 23.68 | 68.3 | 1017.93 | 84.16 | 438.65 |
| 8.28 | 40.77 | 1011.55 | 89.79 | 480.15 |
| 23.44 | 62.52 | 1016.46 | 67.21 | 447.19 |
| 25.32 | 48.41 | 1008.47 | 72.14 | 443.04 |
| 3.94 | 39.9 | 1008.06 | 97.49 | 488.81 |
| 17.3 | 57.76 | 1016.26 | 87.74 | 455.75 |
| 18.2 | 49.39 | 1018.83 | 96.3 | 455.86 |
| 21.43 | 46.97 | 1013.94 | 61.25 | 457.68 |
| 11.16 | 40.05 | 1014.95 | 88.38 | 479.11 |
| 30.38 | 74.16 | 1007.44 | 74.77 | 432.84 |
| 23.36 | 62.52 | 1016.18 | 68.18 | 448.37 |
| 21.69 | 47.45 | 1007.56 | 77.2 | 447.06 |
| 23.62 | 49.21 | 1014.1 | 49.54 | 443.53 |
| 21.87 | 61.45 | 1011.13 | 92.22 | 445.21 |
| 29.25 | 66.51 | 1015.53 | 33.65 | 441.7 |
| 20.03 | 66.86 | 1013.05 | 64.59 | 450.93 |
| 18.14 | 49.78 | 1002.95 | 100.09 | 451.44 |
| 24.23 | 56.89 | 1012.32 | 68.04 | 441.29 |
| 18.11 | 44.85 | 1014.48 | 48.94 | 458.85 |
| 6.57 | 43.65 | 1018.24 | 74.47 | 481.46 |
| 12.56 | 43.41 | 1016.93 | 81.02 | 467.19 |
| 13.4 | 41.58 | 1020.5 | 71.17 | 461.54 |
| 27.1 | 52.84 | 1006.28 | 53.85 | 439.08 |
| 14.28 | 42.74 | 1028.79 | 70.67 | 467.22 |
| 16.29 | 44.34 | 1019.49 | 59.36 | 468.8 |
| 31.24 | 71.98 | 1004.66 | 57.17 | 426.93 |
| 10.57 | 37.73 | 1024.36 | 70.29 | 474.65 |
| 13.8 | 44.21 | 1022.93 | 83.37 | 468.97 |
| 25.3 | 71.58 | 1010.18 | 87.36 | 433.97 |
| 18.06 | 50.16 | 1009.52 | 100.09 | 450.53 |
| 25.42 | 59.04 | 1011.98 | 68.78 | 444.51 |
| 15.07 | 40.69 | 1015.29 | 70.98 | 469.03 |
| 11.75 | 71.14 | 1019.36 | 75.68 | 466.56 |
| 20.23 | 52.05 | 1012.15 | 47.49 | 457.57 |
| 27.31 | 59.54 | 1006.24 | 71.99 | 440.13 |
| 28.57 | 69.84 | 1003.57 | 66.55 | 433.24 |
| 17.9 | 43.72 | 1008.64 | 74.73 | 452.55 |
| 23.83 | 71.37 | 1002.04 | 64.78 | 443.29 |
| 27.92 | 74.99 | 1005.47 | 75.13 | 431.76 |
| 17.34 | 44.78 | 1007.81 | 56.38 | 454.97 |
| 17.94 | 63.07 | 1012.42 | 94.35 | 456.7 |
| 6.4 | 39.9 | 1007.75 | 86.55 | 486.03 |
| 11.78 | 39.96 | 1011.37 | 82.95 | 472.79 |
| 20.28 | 57.25 | 1010.12 | 88.42 | 452.03 |
| 21.04 | 54.2 | 1012.26 | 85.61 | 443.41 |
| 25.11 | 67.32 | 1014.49 | 58.39 | 441.93 |
| 30.28 | 70.98 | 1007.51 | 74.28 | 432.64 |
| 8.14 | 36.24 | 1013.15 | 87.85 | 480.25 |
| 16.86 | 39.63 | 1004.47 | 83.5 | 466.68 |
| 6.25 | 40.07 | 1020.19 | 65.24 | 494.39 |
| 22.35 | 54.42 | 1012.46 | 75.01 | 454.72 |
| 17.98 | 56.85 | 1012.28 | 84.52 | 448.71 |
| 21.19 | 42.48 | 1013.43 | 80.52 | 469.76 |
| 20.94 | 44.89 | 1009.64 | 75.14 | 450.71 |
| 24.23 | 58.79 | 1009.8 | 75.75 | 444.01 |
| 19.18 | 58.2 | 1017.46 | 76.72 | 453.2 |
| 20.88 | 57.85 | 1012.39 | 85.47 | 450.87 |
| 23.67 | 63.86 | 1019.67 | 57.95 | 441.73 |
| 14.12 | 39.52 | 1018.41 | 78.32 | 465.09 |
| 25.23 | 64.63 | 1020.59 | 52.2 | 447.28 |
| 6.54 | 39.33 | 1011.54 | 93.69 | 491.16 |
| 20.08 | 62.52 | 1017.99 | 75.74 | 450.98 |
| 24.67 | 63.56 | 1013.75 | 67.56 | 446.3 |
| 27.82 | 79.74 | 1008.37 | 69.46 | 436.48 |
| 15.55 | 42.03 | 1017.41 | 74.58 | 460.84 |
| 24.26 | 69.51 | 1013.43 | 53.23 | 442.56 |
| 13.45 | 41.49 | 1020.19 | 88.72 | 467.3 |
| 11.06 | 40.64 | 1021.47 | 96.16 | 479.13 |
| 24.91 | 52.3 | 1008.72 | 68.26 | 441.15 |
| 22.39 | 59.04 | 1011.78 | 86.39 | 445.52 |
| 11.95 | 40.69 | 1015.62 | 85.34 | 475.4 |
| 14.85 | 40.69 | 1014.91 | 72.64 | 469.3 |
| 10.11 | 41.62 | 1017.17 | 97.82 | 463.57 |
| 23.67 | 68.67 | 1006.71 | 77.22 | 445.32 |
| 16.14 | 44.21 | 1020.36 | 80.59 | 461.03 |
| 15.11 | 43.13 | 1014.99 | 46.91 | 466.74 |
| 24.14 | 59.87 | 1018.47 | 57.76 | 444.04 |
| 30.08 | 67.25 | 1017.6 | 53.09 | 434.01 |
| 14.77 | 44.9 | 1020.5 | 84.31 | 465.23 |
| 27.6 | 69.34 | 1009.63 | 71.58 | 440.6 |
| 13.89 | 44.84 | 1023.66 | 92.97 | 466.74 |
| 26.85 | 75.6 | 1017.43 | 74.55 | 433.48 |
| 12.41 | 40.96 | 1023.36 | 78.96 | 473.59 |
| 13.08 | 41.74 | 1020.75 | 64.44 | 474.81 |
| 18.93 | 44.06 | 1017.58 | 68.23 | 454.75 |
| 20.5 | 49.69 | 1009.6 | 70.81 | 452.94 |
| 30.72 | 69.13 | 1009.94 | 61.66 | 435.83 |
| 7.55 | 39.22 | 1014.53 | 77.76 | 482.19 |
| 13.49 | 44.47 | 1030.46 | 69.49 | 466.66 |
| 15.62 | 40.12 | 1013.03 | 96.26 | 462.59 |
| 24.8 | 64.63 | 1020.69 | 55.74 | 447.82 |
| 10.03 | 41.62 | 1014.55 | 95.61 | 462.73 |
| 22.43 | 63.21 | 1012.06 | 84.75 | 447.98 |
| 14.95 | 39.31 | 1009.15 | 75.3 | 462.72 |
| 24.78 | 58.46 | 1016.82 | 67.5 | 442.42 |
| 23.2 | 48.41 | 1008.64 | 80.92 | 444.69 |
| 14.01 | 39.0 | 1016.73 | 79.23 | 466.7 |
| 19.4 | 64.63 | 1020.38 | 81.1 | 453.84 |
| 30.15 | 67.32 | 1013.83 | 32.8 | 436.92 |
| 6.91 | 36.08 | 1021.82 | 84.31 | 486.37 |
| 29.04 | 60.07 | 1015.42 | 46.15 | 440.43 |
| 26.02 | 63.07 | 1010.94 | 53.96 | 446.82 |
| 5.89 | 39.48 | 1005.11 | 59.83 | 484.91 |
| 26.52 | 71.64 | 1008.27 | 75.3 | 437.76 |
| 28.53 | 68.08 | 1013.27 | 42.53 | 438.91 |
| 16.59 | 39.54 | 1007.97 | 70.58 | 464.19 |
| 22.95 | 67.79 | 1009.89 | 91.69 | 442.19 |
| 23.96 | 47.43 | 1008.38 | 63.55 | 446.86 |
| 17.48 | 44.2 | 1018.89 | 61.51 | 457.15 |
| 6.69 | 43.65 | 1020.14 | 69.55 | 482.57 |
| 10.25 | 41.26 | 1007.44 | 98.08 | 476.03 |
| 28.87 | 72.58 | 1008.69 | 79.34 | 428.89 |
| 12.04 | 40.23 | 1018.07 | 81.28 | 472.7 |
| 22.58 | 52.3 | 1009.04 | 78.99 | 445.6 |
| 15.12 | 52.05 | 1014.63 | 80.38 | 464.78 |
| 25.48 | 58.95 | 1017.02 | 51.16 | 440.42 |
| 27.87 | 70.79 | 1003.96 | 72.17 | 428.41 |
| 23.72 | 70.47 | 1010.65 | 75.39 | 438.5 |
| 25.0 | 59.43 | 1007.84 | 68.91 | 438.28 |
| 8.42 | 40.64 | 1022.35 | 96.38 | 476.29 |
| 22.46 | 58.49 | 1011.5 | 70.54 | 448.46 |
| 29.92 | 57.19 | 1008.62 | 45.8 | 438.99 |
| 11.68 | 39.22 | 1017.9 | 57.95 | 471.8 |
| 14.04 | 42.44 | 1012.74 | 81.89 | 471.81 |
| 19.86 | 59.14 | 1016.12 | 69.32 | 449.82 |
| 25.99 | 68.08 | 1013.13 | 59.14 | 442.14 |
| 23.42 | 58.79 | 1009.74 | 81.54 | 441.46 |
| 10.6 | 40.22 | 1011.37 | 85.81 | 477.62 |
| 20.97 | 61.87 | 1011.45 | 65.41 | 446.76 |
| 14.14 | 39.82 | 1012.46 | 81.15 | 472.52 |
| 8.56 | 40.71 | 1021.27 | 95.87 | 471.58 |
| 24.86 | 72.39 | 1001.15 | 90.24 | 440.85 |
| 29.0 | 77.54 | 1011.33 | 75.13 | 431.37 |
| 27.59 | 71.97 | 1008.64 | 88.22 | 437.33 |
| 10.45 | 40.71 | 1015.68 | 81.48 | 469.22 |
| 8.51 | 40.78 | 1023.51 | 89.84 | 471.11 |
| 29.82 | 66.51 | 1010.98 | 43.57 | 439.17 |
| 22.56 | 62.26 | 1012.11 | 63.16 | 445.33 |
| 11.38 | 39.22 | 1018.62 | 57.14 | 473.71 |
| 20.25 | 57.76 | 1016.28 | 77.76 | 452.66 |
| 22.42 | 59.43 | 1007.12 | 90.56 | 440.99 |
| 14.85 | 38.91 | 1014.48 | 60.98 | 467.42 |
| 25.62 | 58.82 | 1010.02 | 70.31 | 444.14 |
| 19.85 | 56.53 | 1020.57 | 74.05 | 457.17 |
| 13.67 | 54.3 | 1015.92 | 75.42 | 467.87 |
| 24.39 | 70.72 | 1009.78 | 82.25 | 442.04 |
| 16.07 | 44.58 | 1019.52 | 67.95 | 471.36 |
| 11.6 | 39.1 | 1009.81 | 100.09 | 460.7 |
| 31.38 | 70.83 | 1010.35 | 47.28 | 431.33 |
| 29.91 | 76.86 | 998.59 | 72.41 | 432.6 |
| 19.67 | 59.39 | 1014.07 | 77.67 | 447.61 |
| 27.18 | 64.79 | 1016.27 | 63.7 | 443.87 |
| 21.39 | 52.3 | 1009.2 | 79.77 | 446.87 |
| 10.45 | 41.01 | 1020.57 | 93.84 | 465.74 |
| 19.46 | 56.89 | 1014.02 | 84.95 | 447.86 |
| 23.55 | 62.96 | 1020.16 | 70.16 | 447.65 |
| 23.35 | 63.47 | 1011.78 | 84.24 | 437.87 |
| 9.26 | 41.66 | 1016.87 | 73.32 | 483.51 |
| 10.3 | 41.46 | 1018.21 | 86.17 | 479.65 |
| 20.94 | 58.16 | 1016.88 | 65.43 | 455.16 |
| 23.13 | 71.25 | 1002.49 | 94.59 | 431.91 |
| 12.77 | 41.5 | 1014.13 | 86.8 | 470.68 |
| 28.29 | 69.13 | 1009.29 | 58.18 | 429.28 |
| 19.13 | 59.21 | 1018.32 | 89.66 | 450.81 |
| 24.44 | 73.5 | 1011.49 | 87.39 | 437.73 |
| 20.32 | 44.6 | 1015.16 | 36.35 | 460.21 |
| 20.54 | 69.05 | 1001.6 | 79.62 | 442.86 |
| 12.16 | 45.0 | 1021.51 | 50.52 | 482.99 |
| 28.09 | 65.27 | 1013.27 | 51.96 | 440.0 |
| 9.25 | 41.82 | 1033.25 | 74.73 | 478.48 |
| 21.75 | 49.82 | 1015.01 | 78.33 | 455.28 |
| 23.7 | 66.56 | 1002.07 | 85.19 | 436.94 |
| 16.22 | 37.87 | 1022.36 | 83.13 | 461.06 |
| 24.75 | 69.45 | 1013.97 | 53.49 | 438.28 |
| 10.48 | 39.58 | 1011.81 | 88.86 | 472.61 |
| 29.53 | 70.79 | 1003.7 | 60.89 | 426.85 |
| 12.59 | 39.72 | 1017.76 | 61.14 | 470.18 |
| 23.5 | 54.42 | 1012.31 | 68.29 | 455.38 |
| 29.01 | 66.56 | 1006.44 | 57.62 | 428.32 |
| 9.75 | 42.49 | 1010.57 | 83.63 | 480.35 |
| 19.55 | 56.53 | 1020.2 | 78.1 | 455.56 |
| 21.05 | 58.33 | 1013.14 | 66.34 | 447.66 |
| 24.72 | 68.67 | 1006.74 | 79.02 | 443.06 |
| 21.19 | 58.86 | 1014.19 | 68.96 | 452.43 |
| 10.77 | 41.54 | 1019.94 | 71.13 | 477.81 |
| 28.68 | 73.77 | 1004.72 | 87.01 | 431.66 |
| 29.87 | 73.91 | 1004.53 | 74.3 | 431.8 |
| 22.99 | 68.67 | 1006.65 | 77.62 | 446.67 |
| 24.66 | 60.29 | 1018.0 | 59.56 | 445.26 |
| 32.63 | 69.89 | 1013.85 | 41.66 | 425.72 |
| 31.38 | 72.29 | 1008.73 | 73.27 | 430.58 |
| 23.87 | 60.27 | 1018.94 | 77.16 | 439.86 |
| 25.6 | 59.15 | 1013.31 | 67.02 | 441.11 |
| 27.62 | 71.14 | 1011.6 | 52.8 | 434.72 |
| 30.1 | 67.45 | 1014.23 | 39.04 | 434.01 |
| 12.19 | 41.17 | 1019.43 | 65.47 | 475.64 |
| 13.11 | 41.58 | 1020.43 | 74.32 | 460.44 |
| 28.29 | 68.67 | 1005.46 | 69.22 | 436.4 |
| 13.45 | 40.73 | 1018.7 | 93.88 | 461.03 |
| 10.98 | 41.54 | 1019.94 | 69.83 | 479.08 |
| 26.48 | 69.14 | 1009.31 | 84.11 | 435.76 |
| 13.07 | 45.51 | 1015.22 | 78.65 | 460.14 |
| 25.56 | 75.6 | 1017.37 | 69.31 | 442.2 |
| 22.68 | 50.78 | 1008.83 | 70.3 | 447.69 |
| 28.86 | 73.67 | 1006.65 | 68.23 | 431.15 |
| 22.7 | 63.56 | 1014.32 | 71.76 | 445.0 |
| 27.89 | 73.21 | 1001.32 | 85.88 | 431.59 |
| 13.78 | 44.47 | 1027.94 | 71.09 | 467.22 |
| 28.14 | 51.43 | 1012.16 | 52.67 | 445.33 |
| 11.8 | 45.09 | 1013.21 | 89.68 | 470.57 |
| 10.71 | 39.61 | 1018.72 | 73.66 | 473.77 |
| 24.54 | 60.29 | 1017.42 | 58.94 | 447.67 |
| 11.54 | 40.05 | 1014.78 | 87.05 | 474.29 |
| 29.47 | 71.32 | 1008.07 | 67.0 | 437.14 |
| 29.24 | 69.05 | 1003.12 | 43.18 | 432.56 |
| 14.51 | 41.79 | 1009.72 | 80.62 | 459.14 |
| 22.91 | 60.07 | 1016.03 | 59.72 | 446.19 |
| 27.02 | 71.77 | 1006.38 | 72.1 | 428.1 |
| 13.49 | 44.47 | 1030.18 | 69.15 | 468.46 |
| 30.24 | 66.75 | 1017.95 | 55.66 | 435.02 |
| 23.19 | 48.6 | 1002.38 | 61.19 | 445.52 |
| 17.73 | 40.55 | 1003.36 | 74.62 | 462.69 |
| 18.62 | 61.27 | 1019.26 | 73.35 | 455.75 |
| 12.85 | 40.0 | 1015.89 | 68.85 | 463.74 |
| 32.33 | 69.68 | 1011.95 | 39.89 | 439.79 |
| 25.09 | 58.95 | 1016.99 | 53.16 | 443.26 |
| 29.45 | 69.13 | 1009.3 | 52.97 | 432.04 |
| 16.91 | 43.96 | 1013.32 | 79.87 | 465.86 |
| 14.09 | 45.87 | 1009.05 | 84.09 | 465.6 |
| 10.73 | 25.36 | 1009.35 | 100.15 | 469.43 |
| 23.2 | 49.3 | 1003.4 | 79.77 | 440.75 |
| 8.21 | 38.91 | 1015.82 | 88.99 | 481.32 |
| 9.3 | 40.56 | 1022.64 | 76.14 | 479.87 |
| 16.97 | 39.16 | 1005.7 | 69.13 | 458.59 |
| 23.69 | 71.97 | 1009.62 | 93.03 | 438.62 |
| 25.13 | 59.44 | 1012.38 | 77.92 | 445.59 |
| 9.86 | 43.56 | 1015.13 | 74.89 | 481.87 |
| 11.33 | 41.5 | 1013.58 | 88.7 | 475.01 |
| 26.95 | 48.41 | 1008.53 | 62.94 | 436.54 |
| 15.0 | 40.66 | 1016.28 | 89.62 | 456.63 |
| 20.76 | 62.52 | 1015.63 | 81.04 | 451.69 |
| 14.29 | 39.59 | 1010.93 | 94.53 | 463.04 |
| 19.74 | 67.71 | 1007.68 | 64.02 | 446.1 |
| 26.68 | 59.92 | 1009.94 | 70.57 | 438.67 |
| 14.24 | 41.4 | 1019.7 | 70.32 | 466.88 |
| 21.98 | 48.41 | 1008.42 | 84.86 | 444.6 |
| 22.75 | 59.39 | 1015.4 | 81.41 | 440.26 |
| 8.34 | 40.96 | 1023.28 | 89.45 | 483.92 |
| 11.8 | 41.2 | 1017.18 | 82.71 | 475.19 |
| 8.81 | 44.68 | 1023.06 | 93.93 | 479.24 |
| 30.05 | 73.68 | 1014.95 | 70.6 | 434.92 |
| 16.01 | 65.46 | 1014.0 | 87.68 | 454.16 |
| 21.75 | 58.79 | 1012.42 | 87.58 | 447.58 |
| 13.94 | 41.26 | 1021.67 | 74.4 | 467.9 |
| 29.25 | 69.13 | 1010.27 | 67.35 | 426.29 |
| 22.33 | 45.87 | 1007.8 | 63.61 | 447.02 |
| 16.43 | 41.79 | 1005.47 | 76.89 | 455.85 |
| 11.5 | 40.22 | 1010.31 | 78.08 | 476.46 |
| 23.53 | 68.94 | 1007.53 | 69.17 | 437.48 |
| 21.86 | 49.21 | 1014.61 | 53.31 | 452.77 |
| 6.17 | 39.33 | 1012.57 | 93.32 | 491.54 |
| 30.19 | 64.79 | 1017.22 | 42.47 | 438.41 |
| 11.67 | 41.93 | 1019.81 | 82.58 | 476.1 |
| 15.34 | 36.99 | 1007.87 | 94.59 | 464.58 |
| 11.5 | 40.78 | 1023.91 | 86.31 | 467.74 |
| 25.53 | 57.17 | 1010.0 | 72.57 | 442.12 |
| 21.27 | 57.5 | 1014.53 | 80.76 | 453.34 |
| 28.37 | 69.13 | 1010.44 | 71.93 | 425.29 |
| 28.39 | 51.43 | 1011.74 | 47.54 | 449.63 |
| 13.78 | 45.78 | 1025.27 | 95.72 | 462.88 |
| 14.6 | 42.32 | 1015.71 | 77.03 | 464.67 |
| 5.1 | 35.57 | 1027.17 | 80.49 | 489.96 |
| 7.0 | 38.08 | 1020.27 | 77.67 | 482.38 |
| 26.3 | 77.95 | 1009.45 | 78.72 | 437.95 |
| 30.56 | 71.98 | 1004.74 | 58.77 | 429.2 |
| 21.09 | 46.63 | 1013.03 | 74.8 | 453.34 |
| 28.21 | 70.02 | 1010.58 | 51.34 | 442.47 |
| 15.84 | 49.69 | 1015.14 | 90.41 | 462.6 |
| 10.03 | 40.96 | 1024.57 | 91.1 | 478.79 |
| 20.37 | 52.05 | 1012.34 | 62.57 | 456.11 |
| 21.19 | 50.16 | 1005.81 | 84.27 | 450.33 |
| 33.73 | 69.88 | 1007.21 | 42.93 | 434.83 |
| 29.87 | 73.68 | 1015.1 | 40.96 | 433.43 |
| 19.62 | 62.96 | 1020.76 | 76.53 | 456.02 |
| 9.93 | 40.67 | 1018.08 | 69.74 | 485.23 |
| 9.43 | 37.14 | 1013.03 | 74.99 | 473.57 |
| 14.24 | 39.58 | 1011.17 | 70.45 | 469.94 |
| 12.97 | 49.83 | 1008.69 | 91.49 | 452.07 |
| 7.6 | 41.04 | 1021.82 | 88.97 | 475.32 |
| 8.39 | 36.24 | 1013.39 | 89.13 | 480.69 |
| 25.41 | 48.06 | 1013.12 | 46.52 | 444.01 |
| 18.43 | 56.03 | 1020.41 | 60.55 | 465.17 |
| 10.31 | 39.82 | 1012.87 | 88.71 | 480.61 |
| 11.29 | 41.5 | 1013.39 | 89.15 | 476.04 |
| 22.61 | 49.3 | 1003.51 | 83.02 | 441.76 |
| 29.34 | 71.98 | 1005.19 | 75.19 | 428.24 |
| 18.87 | 67.71 | 1004.0 | 87.35 | 444.77 |
| 13.21 | 45.87 | 1008.58 | 85.66 | 463.1 |
| 11.3 | 44.6 | 1018.19 | 91.66 | 470.5 |
| 29.23 | 72.99 | 1007.04 | 63.47 | 431.0 |
| 27.76 | 69.4 | 1004.27 | 72.25 | 430.68 |
| 29.26 | 67.17 | 1006.6 | 70.58 | 436.42 |
| 25.72 | 49.82 | 1016.19 | 60.1 | 452.33 |
| 23.43 | 63.94 | 1010.64 | 89.29 | 440.16 |
| 25.6 | 63.76 | 1010.18 | 67.43 | 435.75 |
| 22.3 | 44.57 | 1008.48 | 67.58 | 449.74 |
| 27.91 | 72.24 | 1010.74 | 70.8 | 430.73 |
| 30.35 | 77.17 | 1009.55 | 63.62 | 432.75 |
| 21.78 | 47.43 | 1007.88 | 66.68 | 446.79 |
| 7.19 | 41.39 | 1018.12 | 90.76 | 486.35 |
| 20.88 | 59.8 | 1015.66 | 75.34 | 453.18 |
| 24.19 | 50.23 | 1015.73 | 59.77 | 458.31 |
| 9.98 | 41.54 | 1019.7 | 80.79 | 480.26 |
| 23.47 | 51.3 | 1011.89 | 74.1 | 448.65 |
| 26.35 | 49.5 | 1012.67 | 41.34 | 458.41 |
| 29.89 | 64.69 | 1006.37 | 58.78 | 435.39 |
| 19.29 | 50.16 | 1010.49 | 97.78 | 450.21 |
| 17.48 | 43.14 | 1018.68 | 74.85 | 459.59 |
| 25.21 | 75.6 | 1017.19 | 69.84 | 445.84 |
| 23.3 | 48.78 | 1018.17 | 75.36 | 441.08 |
| 15.42 | 37.85 | 1009.89 | 85.8 | 467.33 |
| 21.44 | 63.09 | 1016.56 | 90.11 | 444.19 |
| 29.45 | 68.27 | 1007.96 | 61.63 | 432.96 |
| 29.69 | 47.93 | 1002.85 | 44.76 | 438.09 |
| 15.52 | 36.99 | 1006.86 | 89.7 | 467.9 |
| 11.47 | 43.67 | 1012.68 | 72.51 | 475.72 |
| 9.77 | 34.69 | 1027.72 | 74.98 | 477.51 |
| 22.6 | 69.84 | 1006.37 | 79.59 | 435.13 |
| 8.24 | 39.61 | 1017.99 | 78.42 | 477.9 |
| 17.01 | 44.2 | 1019.18 | 61.23 | 457.26 |
| 19.64 | 44.6 | 1015.88 | 47.56 | 467.53 |
| 10.61 | 41.58 | 1021.08 | 93.06 | 465.15 |
| 12.04 | 40.1 | 1014.42 | 89.65 | 474.28 |
| 29.19 | 65.71 | 1013.85 | 50.5 | 444.49 |
| 21.75 | 45.09 | 1014.15 | 44.84 | 452.84 |
| 23.66 | 77.54 | 1008.5 | 85.32 | 435.38 |
| 27.05 | 75.33 | 1003.88 | 82.94 | 433.57 |
| 29.63 | 69.71 | 1009.04 | 67.26 | 435.27 |
| 18.2 | 39.63 | 1005.35 | 79.05 | 468.49 |
| 32.22 | 70.8 | 1009.9 | 62.03 | 433.07 |
| 26.88 | 73.56 | 1004.85 | 94.36 | 430.63 |
| 29.05 | 65.74 | 1013.29 | 60.02 | 440.74 |
| 8.9 | 39.96 | 1026.31 | 95.46 | 474.49 |
| 18.93 | 48.6 | 1005.72 | 84.92 | 449.74 |
| 27.49 | 63.76 | 1010.09 | 62.8 | 436.73 |
| 23.1 | 70.79 | 1006.53 | 90.81 | 434.58 |
| 11.22 | 43.13 | 1017.24 | 80.9 | 473.93 |
| 31.97 | 79.74 | 1007.03 | 55.84 | 435.99 |
| 13.32 | 43.22 | 1009.45 | 75.3 | 466.83 |
| 31.68 | 68.24 | 1005.29 | 37.34 | 427.22 |
| 23.69 | 63.77 | 1013.39 | 79.5 | 444.07 |
| 13.83 | 41.49 | 1020.11 | 87.29 | 469.57 |
| 18.32 | 66.51 | 1015.18 | 81.5 | 459.89 |
| 11.05 | 40.71 | 1024.91 | 76.42 | 479.59 |
| 22.03 | 64.69 | 1007.21 | 75.75 | 440.92 |
| 10.23 | 41.46 | 1020.45 | 84.95 | 480.87 |
| 23.92 | 66.54 | 1009.93 | 62.37 | 441.9 |
| 29.38 | 69.68 | 1011.35 | 49.25 | 430.2 |
| 17.35 | 42.86 | 1014.62 | 74.16 | 465.16 |
| 9.81 | 44.45 | 1021.19 | 90.55 | 471.32 |
| 4.97 | 40.64 | 1020.91 | 94.28 | 485.43 |
| 5.15 | 40.07 | 1012.27 | 63.31 | 495.35 |
| 21.54 | 58.49 | 1010.85 | 78.9 | 449.12 |
| 7.94 | 42.02 | 1006.22 | 90.97 | 480.53 |
| 18.77 | 50.66 | 1014.89 | 87.34 | 457.07 |
| 21.69 | 69.94 | 1010.7 | 80.8 | 443.67 |
| 10.07 | 44.68 | 1023.44 | 90.95 | 477.52 |
| 13.83 | 39.64 | 1012.52 | 69.97 | 472.95 |
| 10.45 | 39.69 | 1003.92 | 89.45 | 472.54 |
| 11.56 | 40.71 | 1015.85 | 76.08 | 469.17 |
| 23.64 | 70.04 | 1011.09 | 83.35 | 435.21 |
| 10.48 | 40.22 | 1004.81 | 92.16 | 477.78 |
| 13.09 | 39.85 | 1012.86 | 58.42 | 475.89 |
| 10.67 | 40.23 | 1017.75 | 85.06 | 483.9 |
| 12.57 | 39.16 | 1016.53 | 88.91 | 476.2 |
| 14.45 | 43.34 | 1015.47 | 83.33 | 462.16 |
| 14.22 | 37.85 | 1011.24 | 88.49 | 471.05 |
| 6.97 | 41.26 | 1010.6 | 96.88 | 484.71 |
| 20.61 | 63.86 | 1015.43 | 73.86 | 446.34 |
| 14.67 | 42.28 | 1007.21 | 65.17 | 469.02 |
| 29.06 | 72.86 | 1004.23 | 69.41 | 432.12 |
| 14.38 | 40.1 | 1015.51 | 81.23 | 467.28 |
| 32.51 | 69.98 | 1013.29 | 54.07 | 429.66 |
| 11.79 | 45.09 | 1013.16 | 89.17 | 469.49 |
| 8.65 | 40.56 | 1023.23 | 78.85 | 485.87 |
| 9.75 | 40.81 | 1026.0 | 84.44 | 481.95 |
| 9.11 | 40.02 | 1031.1 | 83.02 | 479.03 |
| 23.39 | 69.13 | 1010.99 | 90.66 | 434.5 |
| 14.3 | 54.3 | 1015.16 | 75.29 | 464.9 |
| 17.49 | 63.94 | 1020.02 | 82.6 | 452.71 |
| 31.1 | 69.51 | 1010.84 | 45.4 | 429.74 |
| 19.77 | 56.65 | 1020.67 | 66.33 | 457.09 |
| 28.61 | 72.29 | 1011.61 | 45.33 | 446.77 |
| 13.52 | 41.48 | 1014.46 | 67.12 | 460.76 |
| 13.52 | 40.83 | 1008.31 | 84.14 | 471.95 |
| 17.57 | 46.21 | 1014.09 | 80.81 | 453.29 |
| 28.18 | 60.07 | 1016.34 | 49.13 | 441.61 |
| 14.29 | 46.18 | 1017.01 | 87.29 | 464.73 |
| 18.12 | 43.69 | 1016.91 | 52.95 | 464.68 |
| 31.27 | 73.91 | 1003.72 | 68.92 | 430.59 |
| 26.24 | 77.95 | 1014.19 | 85.21 | 438.01 |
| 7.44 | 41.04 | 1021.84 | 88.56 | 479.08 |
| 29.78 | 74.78 | 1009.28 | 55.09 | 436.39 |
| 23.37 | 65.46 | 1016.25 | 48.64 | 447.07 |
| 10.62 | 39.58 | 1011.9 | 87.85 | 479.91 |
| 5.84 | 43.02 | 1013.88 | 87.42 | 489.05 |
| 14.51 | 53.82 | 1016.46 | 62.75 | 463.17 |
| 11.31 | 42.02 | 1001.18 | 94.86 | 471.26 |
| 11.25 | 40.67 | 1011.64 | 63.54 | 480.49 |
| 9.18 | 39.42 | 1025.41 | 69.46 | 473.78 |
| 19.82 | 58.16 | 1016.76 | 74.66 | 455.5 |
| 24.77 | 58.41 | 1013.78 | 80.57 | 446.27 |
| 9.66 | 41.06 | 1021.21 | 84.7 | 482.2 |
| 21.96 | 59.8 | 1016.72 | 72.6 | 452.48 |
| 18.59 | 43.14 | 1011.92 | 52.63 | 464.48 |
| 24.75 | 69.89 | 1015.29 | 82.01 | 438.1 |
| 24.37 | 63.47 | 1012.77 | 75.22 | 445.6 |
| 29.6 | 67.79 | 1010.37 | 51.05 | 442.43 |
| 25.32 | 61.25 | 1011.56 | 80.1 | 436.67 |
| 16.15 | 41.85 | 1016.54 | 81.58 | 466.56 |
| 15.74 | 71.14 | 1019.65 | 65.94 | 457.29 |
| 5.97 | 36.25 | 1029.65 | 86.74 | 487.03 |
| 15.84 | 52.72 | 1026.45 | 62.57 | 464.93 |
| 14.84 | 44.63 | 1019.28 | 57.37 | 466.0 |
| 12.25 | 48.79 | 1017.44 | 88.91 | 469.52 |
| 27.38 | 70.04 | 1011.18 | 72.26 | 428.88 |
| 8.76 | 41.48 | 1018.49 | 74.98 | 474.3 |
| 15.54 | 39.31 | 1009.69 | 71.19 | 461.06 |
| 18.71 | 39.39 | 1014.09 | 62.82 | 465.57 |
| 13.06 | 41.78 | 1012.3 | 55.31 | 467.67 |
| 12.72 | 40.71 | 1016.02 | 71.57 | 466.99 |
| 19.83 | 39.39 | 1013.73 | 59.16 | 463.72 |
| 27.23 | 49.16 | 1004.03 | 40.8 | 443.78 |
| 24.27 | 68.28 | 1005.43 | 67.63 | 445.23 |
| 11.8 | 40.66 | 1017.13 | 97.2 | 464.43 |
| 6.76 | 36.25 | 1028.31 | 91.16 | 484.36 |
| 25.99 | 63.07 | 1012.5 | 64.81 | 442.16 |
| 16.3 | 39.63 | 1004.64 | 85.61 | 464.11 |
| 16.5 | 49.39 | 1018.35 | 93.42 | 462.48 |
| 10.59 | 42.49 | 1009.59 | 77.36 | 477.49 |
| 26.05 | 65.59 | 1012.78 | 67.03 | 437.04 |
| 19.5 | 40.79 | 1003.8 | 89.45 | 457.09 |
| 22.21 | 45.01 | 1012.22 | 54.84 | 450.6 |
| 17.86 | 45.0 | 1023.25 | 53.48 | 465.78 |
| 29.96 | 70.04 | 1010.15 | 54.47 | 427.1 |
| 19.08 | 44.63 | 1020.14 | 43.36 | 459.81 |
| 23.59 | 47.43 | 1006.64 | 48.92 | 447.36 |
| 3.38 | 39.64 | 1011.0 | 81.22 | 488.92 |
| 26.39 | 66.49 | 1012.96 | 60.35 | 433.36 |
| 8.99 | 39.04 | 1021.99 | 75.98 | 483.35 |
| 10.91 | 41.04 | 1026.57 | 74.24 | 469.53 |
| 13.08 | 39.82 | 1012.27 | 85.21 | 476.96 |
| 23.95 | 58.46 | 1017.5 | 68.46 | 440.75 |
| 15.64 | 43.71 | 1024.51 | 78.31 | 462.55 |
| 18.78 | 54.2 | 1012.05 | 89.25 | 448.04 |
| 20.65 | 50.59 | 1016.22 | 68.57 | 455.24 |
| 4.96 | 40.07 | 1011.8 | 67.38 | 494.75 |
| 23.51 | 57.32 | 1012.55 | 53.6 | 444.58 |
| 5.99 | 35.79 | 1011.56 | 91.69 | 484.82 |
| 23.65 | 66.05 | 1019.6 | 78.21 | 442.9 |
| 5.17 | 39.33 | 1009.68 | 94.19 | 485.46 |
| 26.38 | 49.5 | 1012.82 | 37.19 | 457.81 |
| 6.02 | 43.65 | 1013.85 | 83.53 | 481.92 |
| 23.2 | 61.02 | 1009.63 | 79.45 | 443.23 |
| 8.57 | 39.69 | 1000.91 | 99.9 | 474.29 |
| 30.72 | 71.58 | 1009.98 | 50.39 | 430.46 |
| 21.52 | 50.66 | 1013.56 | 74.33 | 455.71 |
| 22.93 | 62.26 | 1011.25 | 83.66 | 438.34 |
| 5.71 | 41.31 | 1003.24 | 89.48 | 485.83 |
| 18.62 | 44.06 | 1017.76 | 64.59 | 452.82 |
| 27.88 | 68.94 | 1007.68 | 75.68 | 435.04 |
| 22.32 | 59.8 | 1016.82 | 64.18 | 451.21 |
| 14.55 | 42.74 | 1028.41 | 70.09 | 465.81 |
| 17.83 | 44.92 | 1025.04 | 70.58 | 458.42 |
| 9.68 | 39.96 | 1026.09 | 99.28 | 470.22 |
| 19.41 | 49.39 | 1020.84 | 81.89 | 449.24 |
| 13.22 | 44.92 | 1023.84 | 87.99 | 471.43 |
| 12.24 | 44.92 | 1023.74 | 88.21 | 473.26 |
| 19.21 | 58.49 | 1011.7 | 91.29 | 452.82 |
| 29.74 | 70.32 | 1008.1 | 52.72 | 432.69 |
| 23.28 | 60.84 | 1017.91 | 67.5 | 444.13 |
| 8.02 | 41.92 | 1029.8 | 92.05 | 467.21 |
| 22.47 | 48.6 | 1002.33 | 63.23 | 445.98 |
| 27.51 | 73.77 | 1002.42 | 90.88 | 436.91 |
| 17.51 | 44.9 | 1009.05 | 74.91 | 455.01 |
| 23.22 | 66.56 | 1002.47 | 85.39 | 437.11 |
| 11.73 | 40.64 | 1020.68 | 96.98 | 477.06 |
| 21.19 | 67.71 | 1006.65 | 56.28 | 441.71 |
| 5.48 | 40.07 | 1019.63 | 65.62 | 495.76 |
| 24.26 | 66.44 | 1011.33 | 55.32 | 445.63 |
| 12.32 | 41.62 | 1012.88 | 88.88 | 464.72 |
| 31.26 | 68.94 | 1005.94 | 39.49 | 438.03 |
| 32.09 | 72.86 | 1003.47 | 54.59 | 434.78 |
| 24.98 | 60.32 | 1015.63 | 57.19 | 444.67 |
| 27.48 | 61.41 | 1012.2 | 45.06 | 452.24 |
| 21.04 | 45.09 | 1014.19 | 40.62 | 450.92 |
| 27.75 | 70.4 | 1006.65 | 90.21 | 436.53 |
| 22.79 | 71.77 | 1005.75 | 90.91 | 435.53 |
| 24.22 | 68.51 | 1013.23 | 74.96 | 440.01 |
| 27.06 | 64.45 | 1008.72 | 54.21 | 443.1 |
| 29.25 | 71.94 | 1007.18 | 63.62 | 427.49 |
| 26.86 | 68.08 | 1012.99 | 50.04 | 436.25 |
| 29.64 | 67.79 | 1009.99 | 51.23 | 440.74 |
| 19.92 | 63.31 | 1015.02 | 82.71 | 443.54 |
| 18.5 | 51.43 | 1010.82 | 92.04 | 459.42 |
| 23.71 | 60.23 | 1009.76 | 90.67 | 439.66 |
| 14.39 | 44.84 | 1023.55 | 91.14 | 464.15 |
| 19.3 | 56.65 | 1020.55 | 70.43 | 459.1 |
| 24.65 | 52.36 | 1014.76 | 66.63 | 455.68 |
| 13.5 | 45.51 | 1015.33 | 86.95 | 469.08 |
| 9.82 | 41.26 | 1007.71 | 96.69 | 478.02 |
| 18.4 | 44.06 | 1017.36 | 70.88 | 456.8 |
| 28.12 | 44.89 | 1009.18 | 47.14 | 441.13 |
| 17.15 | 43.69 | 1017.05 | 63.36 | 463.88 |
| 30.69 | 73.67 | 1006.14 | 60.58 | 430.45 |
| 28.82 | 65.71 | 1014.24 | 54.3 | 449.18 |
| 21.3 | 48.92 | 1010.92 | 65.09 | 447.89 |
| 30.58 | 70.04 | 1010.4 | 48.16 | 431.59 |
| 21.17 | 52.3 | 1009.36 | 81.51 | 447.5 |
| 9.87 | 41.82 | 1033.04 | 68.57 | 475.58 |
| 22.18 | 59.8 | 1016.77 | 73.16 | 453.24 |
| 24.39 | 63.21 | 1012.59 | 80.88 | 446.4 |
| 10.73 | 44.92 | 1025.1 | 85.4 | 476.81 |
| 9.38 | 40.46 | 1019.29 | 75.77 | 474.1 |
| 20.27 | 57.76 | 1016.66 | 75.76 | 450.71 |
| 24.82 | 66.48 | 1006.4 | 70.21 | 433.62 |
| 16.55 | 41.66 | 1011.45 | 55.53 | 465.14 |
| 20.73 | 59.87 | 1019.08 | 80.48 | 445.18 |
| 9.51 | 39.22 | 1015.3 | 72.41 | 474.12 |
| 8.63 | 43.79 | 1016.08 | 83.25 | 483.91 |
| 6.48 | 40.27 | 1010.55 | 82.12 | 486.68 |
| 14.95 | 43.52 | 1022.43 | 94.75 | 464.98 |
| 5.76 | 45.87 | 1010.83 | 95.79 | 481.4 |
| 10.94 | 39.04 | 1021.81 | 86.02 | 479.2 |
| 15.87 | 41.16 | 1005.85 | 78.29 | 463.86 |
| 12.42 | 38.25 | 1012.76 | 82.23 | 472.3 |
| 29.12 | 58.84 | 1001.31 | 52.86 | 446.51 |
| 29.12 | 51.43 | 1005.93 | 60.66 | 437.71 |
| 19.08 | 41.1 | 1001.96 | 62.77 | 458.94 |
| 31.06 | 67.17 | 1007.62 | 65.54 | 437.91 |
| 5.72 | 39.33 | 1009.96 | 95.4 | 490.76 |
| 26.52 | 65.06 | 1013.4 | 51.78 | 439.66 |
| 13.84 | 44.9 | 1007.58 | 63.62 | 463.27 |
| 13.03 | 39.52 | 1016.68 | 83.09 | 473.99 |
| 25.94 | 66.49 | 1012.83 | 61.81 | 433.38 |
| 16.64 | 53.82 | 1015.13 | 68.24 | 459.01 |
| 14.13 | 40.75 | 1016.05 | 72.41 | 471.44 |
| 13.65 | 39.28 | 1012.97 | 79.64 | 471.91 |
| 14.5 | 44.47 | 1028.2 | 66.95 | 465.15 |
| 19.8 | 51.19 | 1008.25 | 91.98 | 446.66 |
| 25.2 | 63.76 | 1009.78 | 64.96 | 438.15 |
| 20.66 | 51.19 | 1008.81 | 88.93 | 447.14 |
| 12.07 | 43.71 | 1025.53 | 85.62 | 472.32 |
| 25.64 | 70.72 | 1010.16 | 84.0 | 441.68 |
| 23.33 | 72.99 | 1009.33 | 89.41 | 440.04 |
| 29.41 | 64.05 | 1009.82 | 67.4 | 444.82 |
| 16.6 | 53.16 | 1014.5 | 76.75 | 457.26 |
| 27.53 | 72.58 | 1009.13 | 89.06 | 428.83 |
| 20.62 | 43.43 | 1009.93 | 64.02 | 449.07 |
| 26.02 | 71.94 | 1009.38 | 64.12 | 435.21 |
| 12.75 | 44.2 | 1017.59 | 81.22 | 471.03 |
| 12.87 | 48.04 | 1012.47 | 100.13 | 465.56 |
| 25.77 | 62.96 | 1019.86 | 58.07 | 442.83 |
| 14.84 | 41.48 | 1017.26 | 63.42 | 460.3 |
| 7.41 | 40.71 | 1023.07 | 83.32 | 474.25 |
| 8.87 | 41.82 | 1033.3 | 74.28 | 477.97 |
| 9.69 | 40.46 | 1019.1 | 71.91 | 472.16 |
| 16.17 | 46.97 | 1014.22 | 85.8 | 456.08 |
| 26.24 | 49.82 | 1014.9 | 55.58 | 452.41 |
| 13.78 | 43.22 | 1011.31 | 69.7 | 463.71 |
| 26.3 | 67.07 | 1006.26 | 63.79 | 433.72 |
| 17.37 | 57.76 | 1016.0 | 86.59 | 456.4 |
| 23.6 | 48.98 | 1015.41 | 48.28 | 448.43 |
| 8.3 | 36.08 | 1020.63 | 80.42 | 481.6 |
| 18.86 | 42.18 | 1001.16 | 98.58 | 457.07 |
| 22.12 | 49.39 | 1019.8 | 72.83 | 451.0 |
| 28.41 | 75.6 | 1018.48 | 56.07 | 440.28 |
| 29.42 | 71.32 | 1002.26 | 67.13 | 437.47 |
| 18.61 | 67.71 | 1004.07 | 84.49 | 443.57 |
| 27.57 | 69.84 | 1004.91 | 68.37 | 426.6 |
| 12.83 | 41.5 | 1013.12 | 86.07 | 470.87 |
| 9.64 | 39.85 | 1012.9 | 83.82 | 478.37 |
| 19.13 | 58.66 | 1013.32 | 74.86 | 453.92 |
| 15.92 | 40.56 | 1020.79 | 53.52 | 470.22 |
| 24.64 | 72.24 | 1011.37 | 80.61 | 434.54 |
| 27.62 | 63.9 | 1013.11 | 43.56 | 442.89 |
| 8.9 | 36.24 | 1013.29 | 89.35 | 479.03 |
| 9.55 | 43.99 | 1020.5 | 97.28 | 476.06 |
| 10.57 | 36.71 | 1022.62 | 80.49 | 473.88 |
| 19.8 | 57.25 | 1010.84 | 88.9 | 451.75 |
| 25.63 | 56.85 | 1012.68 | 49.7 | 439.2 |
| 24.7 | 58.46 | 1015.58 | 68.64 | 439.7 |
| 15.26 | 46.18 | 1013.68 | 98.58 | 463.6 |
| 20.06 | 52.84 | 1004.21 | 82.12 | 447.47 |
| 19.84 | 56.89 | 1013.23 | 78.32 | 447.92 |
| 11.49 | 44.63 | 1020.44 | 86.04 | 471.08 |
| 23.74 | 72.43 | 1007.99 | 91.36 | 437.55 |
| 22.62 | 51.3 | 1012.36 | 81.02 | 448.27 |
| 29.53 | 72.39 | 998.47 | 76.05 | 431.69 |
| 21.32 | 48.14 | 1016.57 | 71.81 | 449.09 |
| 20.3 | 58.46 | 1015.93 | 82.13 | 448.79 |
| 16.97 | 44.92 | 1025.21 | 74.27 | 460.21 |
| 12.07 | 41.17 | 1013.54 | 71.32 | 479.28 |
| 7.46 | 41.82 | 1032.67 | 74.59 | 483.11 |
| 19.2 | 54.2 | 1011.46 | 84.44 | 450.75 |
| 28.64 | 66.54 | 1010.43 | 43.39 | 437.97 |
| 13.56 | 41.48 | 1008.53 | 87.2 | 459.76 |
| 17.4 | 44.9 | 1020.5 | 77.11 | 457.75 |
| 14.08 | 40.1 | 1015.48 | 82.81 | 469.33 |
| 27.11 | 69.75 | 1009.74 | 85.67 | 433.28 |
| 20.92 | 70.02 | 1010.23 | 95.58 | 444.64 |
| 16.18 | 44.9 | 1021.3 | 74.46 | 463.1 |
| 15.57 | 44.68 | 1022.01 | 90.02 | 460.91 |
| 10.37 | 39.04 | 1023.95 | 81.93 | 479.35 |
| 19.6 | 59.21 | 1017.65 | 86.29 | 449.23 |
| 9.22 | 40.92 | 1021.83 | 85.43 | 474.51 |
| 27.76 | 72.99 | 1007.81 | 71.66 | 435.02 |
| 28.68 | 70.72 | 1009.43 | 71.33 | 435.45 |
| 20.95 | 48.14 | 1013.3 | 67.72 | 452.38 |
| 9.06 | 39.3 | 1019.73 | 84.23 | 480.41 |
| 9.21 | 39.72 | 1019.54 | 74.44 | 478.96 |
| 13.65 | 42.74 | 1026.58 | 71.48 | 468.87 |
| 31.79 | 76.2 | 1007.89 | 56.3 | 434.01 |
| 14.32 | 44.6 | 1013.85 | 68.13 | 466.36 |
| 26.28 | 75.23 | 1011.44 | 68.35 | 435.28 |
| 7.69 | 43.02 | 1014.51 | 85.23 | 486.46 |
| 14.44 | 40.1 | 1015.51 | 79.78 | 468.19 |
| 9.19 | 41.01 | 1022.14 | 98.98 | 468.37 |
| 13.35 | 41.39 | 1019.17 | 72.87 | 474.19 |
| 23.04 | 74.22 | 1009.52 | 90.93 | 440.32 |
| 4.83 | 38.44 | 1015.35 | 72.94 | 485.32 |
| 17.29 | 42.86 | 1014.38 | 72.3 | 464.27 |
| 8.73 | 36.18 | 1013.66 | 77.74 | 479.25 |
| 26.21 | 70.32 | 1007.0 | 78.29 | 430.4 |
| 23.72 | 58.62 | 1016.65 | 69.1 | 447.49 |
| 29.27 | 64.69 | 1006.85 | 55.79 | 438.23 |
| 10.4 | 40.43 | 1025.46 | 75.09 | 492.09 |
| 12.19 | 40.75 | 1015.13 | 88.98 | 475.36 |
| 20.4 | 54.9 | 1016.68 | 64.26 | 452.56 |
| 34.3 | 74.67 | 1015.98 | 25.89 | 427.84 |
| 27.56 | 68.08 | 1010.8 | 59.18 | 433.95 |
| 30.9 | 70.8 | 1008.48 | 67.48 | 435.27 |
| 14.85 | 58.59 | 1014.04 | 89.85 | 454.62 |
| 16.42 | 40.56 | 1020.36 | 50.62 | 472.17 |
| 16.45 | 63.31 | 1015.96 | 83.97 | 452.42 |
| 10.14 | 42.02 | 1003.19 | 96.51 | 472.17 |
| 9.53 | 41.44 | 1018.01 | 80.09 | 481.83 |
| 17.01 | 49.15 | 1021.83 | 84.02 | 458.78 |
| 23.94 | 62.08 | 1022.47 | 61.97 | 447.5 |
| 15.95 | 49.25 | 1019.04 | 88.51 | 463.4 |
| 11.15 | 41.26 | 1022.67 | 81.83 | 473.57 |
| 25.56 | 70.32 | 1009.07 | 90.63 | 433.72 |
| 27.16 | 66.44 | 1011.2 | 73.37 | 431.85 |
| 26.71 | 77.95 | 1012.13 | 77.5 | 433.47 |
| 29.56 | 74.22 | 1007.45 | 57.46 | 432.84 |
| 31.19 | 70.94 | 1007.29 | 51.91 | 436.6 |
| 6.86 | 41.17 | 1020.12 | 79.14 | 490.23 |
| 12.36 | 41.74 | 1020.58 | 69.24 | 477.16 |
| 32.82 | 68.31 | 1010.44 | 41.85 | 441.06 |
| 25.3 | 70.98 | 1007.22 | 95.1 | 440.86 |
| 8.71 | 41.82 | 1033.08 | 74.53 | 477.94 |
| 13.34 | 40.8 | 1026.56 | 64.85 | 474.47 |
| 14.2 | 43.02 | 1012.18 | 57.07 | 470.67 |
| 23.74 | 65.34 | 1013.7 | 62.9 | 447.31 |
| 16.9 | 44.88 | 1018.14 | 72.21 | 466.8 |
| 28.54 | 71.94 | 1007.4 | 65.99 | 430.91 |
| 30.15 | 69.88 | 1007.2 | 73.67 | 434.75 |
| 14.33 | 42.86 | 1010.82 | 88.59 | 469.52 |
| 25.57 | 59.43 | 1008.88 | 61.19 | 438.9 |
| 30.55 | 70.04 | 1010.51 | 49.37 | 429.56 |
| 28.04 | 74.33 | 1013.53 | 48.65 | 432.92 |
| 26.39 | 49.16 | 1005.68 | 56.18 | 442.87 |
| 15.3 | 41.76 | 1022.57 | 71.56 | 466.59 |
| 6.03 | 41.14 | 1028.04 | 87.46 | 479.61 |
| 13.49 | 44.63 | 1019.12 | 70.02 | 471.08 |
| 27.67 | 59.14 | 1016.51 | 61.2 | 433.37 |
| 24.19 | 65.48 | 1018.8 | 60.54 | 443.92 |
| 24.44 | 59.14 | 1016.74 | 71.82 | 443.5 |
| 29.86 | 64.79 | 1017.37 | 44.8 | 439.89 |
| 30.2 | 69.59 | 1008.9 | 67.32 | 434.66 |
| 7.99 | 41.38 | 1021.95 | 78.77 | 487.57 |
| 9.93 | 41.62 | 1013.76 | 96.02 | 464.64 |
| 11.03 | 42.32 | 1017.26 | 90.56 | 470.92 |
| 22.34 | 63.73 | 1014.37 | 83.19 | 444.39 |
| 25.33 | 48.6 | 1002.54 | 68.45 | 442.48 |
| 18.87 | 52.08 | 1005.25 | 99.19 | 449.61 |
| 25.97 | 69.34 | 1009.43 | 88.11 | 435.02 |
| 16.58 | 43.99 | 1021.81 | 79.29 | 458.67 |
| 14.35 | 46.18 | 1016.63 | 87.76 | 461.74 |
| 25.06 | 62.39 | 1008.09 | 82.56 | 438.31 |
| 13.85 | 48.92 | 1011.68 | 79.24 | 462.38 |
| 16.09 | 44.2 | 1019.39 | 67.24 | 460.56 |
| 26.34 | 59.21 | 1013.37 | 58.98 | 439.22 |
| 23.01 | 58.79 | 1009.71 | 84.22 | 444.64 |
| 26.39 | 71.25 | 999.8 | 89.12 | 430.34 |
| 31.32 | 71.29 | 1008.37 | 50.07 | 430.46 |
| 16.64 | 45.87 | 1009.02 | 98.86 | 456.79 |
| 13.42 | 41.23 | 994.17 | 95.79 | 468.82 |
| 20.06 | 44.9 | 1008.79 | 70.06 | 448.51 |
| 14.8 | 44.71 | 1014.67 | 41.71 | 470.77 |
| 12.59 | 41.14 | 1025.79 | 86.55 | 465.74 |
| 26.7 | 66.56 | 1005.31 | 71.97 | 430.21 |
| 19.78 | 50.32 | 1008.62 | 96.4 | 449.23 |
| 15.17 | 49.15 | 1021.91 | 91.73 | 461.89 |
| 21.71 | 61.45 | 1010.97 | 91.62 | 445.72 |
| 19.09 | 39.39 | 1013.36 | 59.14 | 466.13 |
| 19.76 | 51.19 | 1008.38 | 92.56 | 448.71 |
| 14.68 | 41.23 | 998.43 | 83.71 | 469.25 |
| 21.3 | 66.86 | 1013.04 | 55.43 | 450.56 |
| 16.73 | 39.64 | 1008.94 | 74.91 | 464.46 |
| 12.26 | 41.5 | 1014.87 | 89.41 | 471.13 |
| 14.77 | 48.06 | 1010.92 | 69.81 | 461.52 |
| 18.26 | 59.15 | 1012.04 | 86.01 | 451.09 |
| 27.1 | 79.74 | 1005.43 | 86.05 | 431.51 |
| 14.72 | 40.83 | 1009.65 | 80.98 | 469.8 |
| 26.3 | 51.43 | 1012.05 | 63.62 | 442.28 |
| 16.48 | 48.92 | 1011.84 | 64.16 | 458.67 |
| 17.99 | 43.79 | 1016.13 | 75.63 | 462.4 |
| 20.34 | 59.8 | 1015.18 | 80.21 | 453.54 |
| 25.53 | 62.96 | 1019.81 | 59.7 | 444.38 |
| 31.59 | 58.9 | 1003.39 | 47.6 | 440.52 |
| 30.8 | 69.14 | 1007.68 | 63.78 | 433.62 |
| 10.75 | 45.0 | 1023.68 | 89.37 | 481.96 |
| 19.3 | 44.9 | 1008.89 | 70.55 | 452.75 |
| 4.71 | 39.42 | 1026.4 | 84.42 | 481.28 |
| 23.1 | 66.05 | 1020.28 | 80.62 | 439.03 |
| 32.63 | 73.88 | 1005.64 | 52.56 | 435.75 |
| 26.63 | 74.16 | 1009.72 | 83.26 | 436.03 |
| 24.35 | 58.49 | 1011.03 | 70.64 | 445.6 |
| 15.11 | 56.03 | 1020.27 | 89.95 | 462.65 |
| 29.1 | 50.05 | 1005.87 | 51.53 | 438.66 |
| 21.24 | 50.32 | 1008.54 | 84.83 | 447.32 |
| 6.16 | 39.48 | 1004.85 | 59.68 | 484.55 |
| 7.36 | 41.01 | 1024.9 | 97.88 | 476.8 |
| 10.44 | 39.04 | 1023.99 | 85.03 | 480.34 |
| 26.76 | 48.41 | 1010.53 | 47.38 | 440.63 |
| 16.79 | 44.6 | 1014.27 | 48.08 | 459.48 |
| 10.76 | 40.43 | 1025.98 | 79.65 | 490.78 |
| 6.07 | 38.91 | 1019.25 | 83.39 | 483.56 |
| 27.33 | 73.18 | 1012.26 | 82.18 | 429.38 |
| 27.15 | 59.21 | 1013.49 | 51.71 | 440.27 |
| 22.35 | 51.43 | 1011.34 | 77.33 | 445.34 |
| 21.82 | 65.27 | 1013.86 | 72.81 | 447.43 |
| 21.11 | 69.94 | 1004.37 | 84.26 | 439.91 |
| 19.95 | 50.59 | 1016.11 | 73.23 | 459.27 |
| 7.45 | 39.61 | 1017.88 | 79.73 | 478.89 |
| 15.36 | 41.66 | 1012.41 | 62.32 | 466.7 |
| 15.65 | 43.5 | 1021.39 | 78.58 | 463.5 |
| 25.31 | 74.33 | 1015.04 | 79.88 | 436.21 |
| 25.88 | 63.47 | 1011.95 | 65.87 | 443.94 |
| 24.6 | 63.94 | 1012.87 | 80.28 | 439.63 |
| 22.58 | 41.54 | 1013.21 | 71.33 | 460.95 |
| 19.69 | 59.14 | 1015.99 | 70.33 | 448.69 |
| 25.85 | 75.08 | 1006.24 | 57.73 | 444.63 |
| 10.06 | 37.83 | 1005.49 | 99.46 | 473.51 |
| 18.59 | 39.54 | 1008.56 | 68.61 | 462.56 |
| 18.27 | 50.16 | 1011.07 | 95.91 | 451.76 |
| 8.85 | 40.43 | 1025.68 | 80.42 | 491.81 |
| 30.04 | 68.08 | 1011.04 | 51.01 | 429.52 |
| 26.06 | 49.02 | 1007.59 | 74.08 | 437.9 |
| 14.8 | 38.73 | 1003.18 | 80.73 | 467.54 |
| 23.93 | 64.45 | 1015.35 | 54.71 | 449.97 |
| 23.72 | 66.48 | 1003.61 | 73.75 | 436.62 |
| 11.44 | 40.55 | 1023.37 | 88.43 | 477.68 |
| 20.28 | 63.86 | 1016.04 | 74.66 | 447.26 |
| 27.9 | 63.13 | 1011.8 | 70.04 | 439.76 |
| 24.74 | 59.39 | 1015.23 | 74.64 | 437.49 |
| 14.8 | 58.2 | 1018.29 | 85.11 | 455.14 |
| 8.22 | 41.03 | 1021.76 | 82.97 | 485.5 |
| 27.56 | 66.93 | 1016.81 | 55.59 | 444.1 |
| 32.07 | 70.94 | 1006.91 | 49.9 | 432.33 |
| 9.53 | 44.03 | 1008.87 | 89.99 | 471.23 |
| 13.61 | 42.34 | 1017.93 | 91.61 | 463.89 |
| 22.2 | 51.19 | 1009.2 | 82.95 | 445.54 |
| 21.36 | 59.54 | 1007.99 | 92.62 | 446.09 |
| 23.25 | 63.86 | 1017.82 | 59.64 | 445.12 |
| 23.5 | 59.21 | 1018.29 | 63.0 | 443.31 |
| 8.46 | 39.66 | 1015.14 | 85.38 | 484.16 |
| 8.19 | 40.69 | 1019.86 | 85.23 | 477.76 |
| 30.67 | 71.29 | 1008.36 | 52.08 | 430.28 |
| 32.48 | 62.04 | 1010.39 | 38.05 | 446.48 |
| 8.99 | 36.66 | 1028.11 | 71.98 | 481.03 |
| 13.77 | 47.83 | 1007.41 | 90.66 | 466.07 |
| 19.05 | 67.32 | 1013.2 | 83.14 | 447.47 |
| 21.19 | 55.5 | 1019.83 | 65.22 | 455.93 |
| 10.12 | 40.0 | 1021.15 | 91.67 | 479.62 |
| 24.93 | 47.01 | 1014.28 | 66.04 | 455.06 |
| 8.47 | 40.46 | 1019.87 | 78.19 | 475.06 |
| 24.52 | 56.85 | 1012.59 | 54.47 | 438.89 |
| 28.55 | 69.84 | 1003.38 | 67.26 | 432.7 |
| 20.58 | 50.9 | 1011.89 | 72.56 | 452.6 |
| 18.31 | 46.21 | 1010.46 | 82.15 | 451.75 |
| 27.18 | 71.06 | 1008.16 | 86.32 | 430.66 |
| 4.43 | 38.91 | 1019.04 | 88.17 | 491.9 |
| 26.02 | 74.78 | 1010.04 | 72.78 | 439.82 |
| 15.75 | 39.0 | 1015.91 | 69.58 | 460.73 |
| 22.99 | 60.95 | 1015.14 | 69.86 | 449.7 |
| 25.52 | 59.15 | 1013.88 | 65.37 | 439.42 |
| 27.04 | 65.06 | 1013.33 | 52.37 | 439.84 |
| 6.42 | 35.57 | 1025.58 | 79.63 | 485.86 |
| 17.04 | 40.12 | 1011.81 | 83.14 | 458.1 |
| 10.79 | 39.82 | 1012.89 | 88.25 | 479.92 |
| 20.41 | 56.03 | 1019.94 | 55.85 | 458.29 |
| 7.36 | 40.07 | 1017.29 | 52.55 | 489.45 |
| 28.08 | 73.42 | 1012.17 | 62.74 | 434.0 |
| 24.74 | 69.13 | 1010.69 | 90.08 | 431.24 |
| 28.32 | 47.93 | 1003.26 | 54.5 | 439.5 |
| 16.71 | 40.56 | 1019.48 | 49.88 | 467.46 |
| 30.7 | 71.58 | 1010.0 | 48.96 | 429.27 |
| 18.42 | 58.95 | 1016.95 | 86.77 | 452.1 |
| 10.62 | 42.02 | 999.83 | 96.66 | 472.41 |
| 22.18 | 69.05 | 1002.75 | 70.84 | 442.14 |
| 22.38 | 49.3 | 1003.56 | 83.83 | 441.0 |
| 13.94 | 41.58 | 1020.76 | 68.22 | 463.07 |
| 21.24 | 60.84 | 1017.99 | 82.22 | 445.71 |
| 6.76 | 39.81 | 1017.11 | 87.9 | 483.16 |
| 26.73 | 68.84 | 1010.75 | 66.83 | 440.45 |
| 7.24 | 38.06 | 1020.6 | 85.36 | 481.83 |
| 10.84 | 40.62 | 1015.53 | 60.9 | 467.6 |
| 19.32 | 52.84 | 1004.29 | 83.51 | 450.88 |
| 29.0 | 69.13 | 1001.22 | 52.96 | 425.5 |
| 23.38 | 54.42 | 1013.95 | 73.02 | 451.87 |
| 31.17 | 69.51 | 1010.51 | 43.11 | 428.94 |
| 26.17 | 48.6 | 1002.59 | 61.41 | 439.86 |
| 30.9 | 73.42 | 1011.21 | 65.32 | 433.44 |
| 24.92 | 73.68 | 1015.12 | 93.68 | 438.23 |
| 32.77 | 71.32 | 1007.68 | 42.39 | 436.95 |
| 14.37 | 40.56 | 1021.67 | 68.18 | 470.19 |
| 8.36 | 40.22 | 1011.6 | 89.18 | 484.66 |
| 31.45 | 68.27 | 1007.56 | 64.79 | 430.81 |
| 31.6 | 73.17 | 1010.05 | 43.48 | 433.37 |
| 17.9 | 48.98 | 1014.17 | 80.4 | 453.02 |
| 20.35 | 50.9 | 1012.6 | 72.43 | 453.5 |
| 16.21 | 41.23 | 995.88 | 80.0 | 463.09 |
| 19.36 | 44.6 | 1016.25 | 45.65 | 464.56 |
| 21.04 | 65.46 | 1017.22 | 63.02 | 452.12 |
| 14.05 | 40.69 | 1015.66 | 74.39 | 470.9 |
| 23.48 | 64.15 | 1021.08 | 57.77 | 450.89 |
| 21.91 | 63.76 | 1009.85 | 76.8 | 445.04 |
| 24.42 | 63.07 | 1011.49 | 67.39 | 444.72 |
| 14.26 | 40.92 | 1022.07 | 73.96 | 460.38 |
| 21.38 | 58.33 | 1013.05 | 72.75 | 446.8 |
| 15.71 | 44.06 | 1018.34 | 71.69 | 465.05 |
| 5.78 | 40.62 | 1016.55 | 84.98 | 484.13 |
| 6.77 | 39.81 | 1017.01 | 87.68 | 488.27 |
| 23.84 | 49.21 | 1013.85 | 50.36 | 447.09 |
| 21.17 | 58.16 | 1017.16 | 68.11 | 452.02 |
| 19.94 | 58.96 | 1014.16 | 66.27 | 455.55 |
| 8.73 | 41.92 | 1029.41 | 89.72 | 480.99 |
| 16.39 | 41.67 | 1012.96 | 61.07 | 467.68 |
Linear Regression Model
- Linear Regression is one of the most useful work-horses of statistical learning
- See Chapter 7 of Kevin Murphy's Machine Learning froma Probabilistic Perspective for a good mathematical and algorithmic introduction.
- You should have already seen Ameet's treatment of the topic from earlier notebook.
Let's open http://spark.apache.org/docs/latest/mllib-linear-methods.html#regression for some details.
// First let's hold out 20% of our data for testing and leave 80% for training
var Array(split20, split80) = dataset.randomSplit(Array(0.20, 0.80), 1800009193L)
split20: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
split80: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
// Let's cache these datasets for performance
val testSet = split20.cache()
val trainingSet = split80.cache()
testSet: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
trainingSet: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [AT: double, V: double ... 3 more fields]
testSet.count() // action to actually cache
res87: Long = 1856
trainingSet.count() // action to actually cache
res88: Long = 7712
Let's take a few elements of the three DataFrames.
dataset.take(3)
res89: Array[org.apache.spark.sql.Row] = Array([14.96,41.76,1024.07,73.17,463.26], [25.18,62.96,1020.04,59.08,444.37], [5.11,39.4,1012.16,92.14,488.56])
testSet.take(3)
res90: Array[org.apache.spark.sql.Row] = Array([2.34,39.42,1028.47,69.68,490.34], [2.8,39.64,1011.01,82.96,482.66], [3.82,35.47,1016.62,84.34,489.04])
trainingSet.take(3)
res91: Array[org.apache.spark.sql.Row] = Array([1.81,39.42,1026.92,76.97,490.55], [2.58,39.42,1028.68,69.03,488.69], [2.64,39.64,1011.02,85.24,481.29])
// ***** LINEAR REGRESSION MODEL ****
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.regression.LinearRegressionModel
import org.apache.spark.ml.Pipeline
// Let's initialize our linear regression learner
val lr = new LinearRegression()
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.regression.LinearRegressionModel
import org.apache.spark.ml.Pipeline
lr: org.apache.spark.ml.regression.LinearRegression = linReg_3b8dffe4015f
// We use explain params to dump the parameters we can use
lr.explainParams()
res92: String =
aggregationDepth: suggested depth for treeAggregate (>= 2) (default: 2)
elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty (default: 0.0)
epsilon: The shape parameter to control the amount of robustness. Must be > 1.0. (default: 1.35)
featuresCol: features column name (default: features)
fitIntercept: whether to fit an intercept term (default: true)
labelCol: label column name (default: label)
loss: The loss function to be optimized. Supported options: squaredError, huber. (Default squaredError) (default: squaredError)
maxIter: maximum number of iterations (>= 0) (default: 100)
predictionCol: prediction column name (default: prediction)
regParam: regularization parameter (>= 0) (default: 0.0)
solver: The solver algorithm for optimization. Supported options: auto, normal, l-bfgs. (Default auto) (default: auto)
standardization: whether to standardize the training features before fitting the model (default: true)
tol: the convergence tolerance for iterative algorithms (>= 0) (default: 1.0E-6)
weightCol: weight column name. If this is not set or empty, we treat all instance weights as 1.0 (undefined)
The cell below is based on the Spark ML pipeline API. More information can be found in the Spark ML Programming Guide at https://spark.apache.org/docs/latest/ml-guide.html
// Now we set the parameters for the method
lr.setPredictionCol("Predicted_PE")
.setLabelCol("PE")
.setMaxIter(100)
.setRegParam(0.1)
// We will use the new spark.ml pipeline API. If you have worked with scikit-learn this will be very familiar.
val lrPipeline = new Pipeline()
lrPipeline.setStages(Array(vectorizer, lr))
// Let's first train on the entire dataset to see what we get
val lrModel = lrPipeline.fit(trainingSet)
lrPipeline: org.apache.spark.ml.Pipeline = pipeline_07136143d91d
lrModel: org.apache.spark.ml.PipelineModel = pipeline_07136143d91d
Since Linear Regression is simply a line of best fit over the data that minimizes the square of the error, given multiple input dimensions we can express each predictor as a line function of the form:
\[ y = b_0 + b_1 x_1 + b_2 x_2 + b_3 x_3 + \ldots + b_i x_i + \ldots + b_k x_k \]
where \(b_0\) is the intercept and \(b_i\)'s are coefficients.
To express the coefficients of that line we can retrieve the Estimator stage from the fitted, linear-regression pipeline model named lrModel and express the weights and the intercept for the function.
// The intercept is as follows:
val intercept = lrModel.stages(1).asInstanceOf[LinearRegressionModel].intercept
intercept: Double = 434.0183482461227
// The coefficents (i.e. weights) are as follows:
val weights = lrModel.stages(1).asInstanceOf[LinearRegressionModel].coefficients.toArray
weights: Array[Double] = Array(-1.9288465830313846, -0.24931659465920197, 0.08140785421485836, -0.1458797521995801)
The model has been fit and the intercept and coefficients displayed above.
Now, let us do some work to make a string of the model that is easy to understand for an applied data scientist or data analyst.
val featuresNoLabel = dataset.columns.filter(col => col != "PE")
featuresNoLabel: Array[String] = Array(AT, V, AP, RH)
val coefficentFeaturePairs = sc.parallelize(weights).zip(sc.parallelize(featuresNoLabel))
coefficentFeaturePairs: org.apache.spark.rdd.RDD[(Double, String)] = ZippedPartitionsRDD2[32671] at zip at command-2972105651606305:1
coefficentFeaturePairs.collect() // this just pairs each coefficient with the name of its corresponding feature
res93: Array[(Double, String)] = Array((-1.9288465830313846,AT), (-0.24931659465920197,V), (0.08140785421485836,AP), (-0.1458797521995801,RH))
// Now let's sort the coefficients from the largest to the smallest
var equation = s"y = $intercept "
//var variables = Array
coefficentFeaturePairs.sortByKey().collect().foreach({
case (weight, feature) =>
{
val symbol = if (weight > 0) "+" else "-"
val absWeight = Math.abs(weight)
equation += (s" $symbol (${absWeight} * ${feature})")
}
}
)
equation: String = y = 434.0183482461227 - (1.9288465830313846 * AT) - (0.24931659465920197 * V) - (0.1458797521995801 * RH) + (0.08140785421485836 * AP)
// Finally here is our equation
println("Linear Regression Equation: " + equation)
Linear Regression Equation: y = 434.0183482461227 - (1.9288465830313846 * AT) - (0.24931659465920197 * V) - (0.1458797521995801 * RH) + (0.08140785421485836 * AP)
Based on examining the fitted Linear Regression Equation above:
- There is a strong negative correlation between Atmospheric Temperature (AT) and Power Output due to the coefficient being greater than -1.91.
- But our other dimenensions seem to have little to no correlation with Power Output.
Do you remember Step 2: Explore Your Data? When we visualized each predictor against Power Output using a Scatter Plot, only the temperature variable seemed to have a linear correlation with Power Output so our final equation seems logical.
Now let's see what our predictions look like given this model.
val predictionsAndLabels = lrModel.transform(testSet)
display(predictionsAndLabels.select("AT", "V", "AP", "RH", "PE", "Predicted_PE"))
| AT | V | AP | RH | PE | Predicted_PE |
|---|---|---|---|---|---|
| 2.34 | 39.42 | 1028.47 | 69.68 | 490.34 | 493.23742177145215 |
| 2.8 | 39.64 | 1011.01 | 82.96 | 482.66 | 488.93663844863084 |
| 3.82 | 35.47 | 1016.62 | 84.34 | 489.04 | 488.2642491377776 |
| 3.98 | 35.47 | 1017.22 | 86.53 | 489.64 | 487.68500173970443 |
| 4.23 | 38.44 | 1016.46 | 76.64 | 489.0 | 487.8432005878593 |
| 4.32 | 35.47 | 1017.8 | 88.51 | 488.03 | 486.7875685475632 |
| 4.43 | 38.91 | 1019.04 | 88.17 | 491.9 | 485.8682911927764 |
| 4.65 | 35.19 | 1018.23 | 94.78 | 489.36 | 485.34119715268844 |
| 4.78 | 42.85 | 1013.39 | 93.36 | 481.47 | 482.9938172155284 |
| 4.87 | 42.85 | 1012.69 | 94.72 | 482.05 | 482.5648390621137 |
| 4.96 | 40.07 | 1011.8 | 67.38 | 494.75 | 487.00024243767876 |
| 4.97 | 42.85 | 1014.02 | 88.78 | 482.98 | 483.3467525779819 |
| 5.01 | 39.4 | 1003.69 | 91.9 | 475.34 | 482.8336530053327 |
| 5.06 | 40.64 | 1021.49 | 93.7 | 483.73 | 483.6145343498689 |
| 5.15 | 35.19 | 1018.63 | 93.94 | 488.42 | 484.53187599470635 |
| 5.15 | 40.07 | 1012.27 | 63.31 | 495.35 | 487.2657538698361 |
| 5.17 | 35.57 | 1026.68 | 79.86 | 491.32 | 487.10787889447494 |
| 5.21 | 41.31 | 1003.51 | 91.23 | 486.46 | 482.0547750131424 |
| 5.23 | 40.78 | 1025.13 | 92.74 | 483.12 | 483.688095258955 |
| 5.25 | 40.07 | 1019.48 | 67.7 | 495.23 | 487.0194077282659 |
| 5.28 | 45.87 | 1008.25 | 97.88 | 479.91 | 480.1986449575354 |
| 5.29 | 41.38 | 1020.62 | 83.83 | 492.12 | 484.35541367676683 |
| 5.42 | 41.38 | 1020.77 | 86.02 | 491.38 | 483.7973981417879 |
| 5.47 | 40.62 | 1018.66 | 83.61 | 481.56 | 484.07023605498495 |
| 5.51 | 41.03 | 1022.28 | 84.5 | 491.84 | 484.05572584065357 |
| 5.53 | 35.79 | 1011.19 | 94.01 | 484.64 | 483.0334383183464 |
| 5.54 | 41.38 | 1020.47 | 82.91 | 490.07 | 483.9952002249004 |
| 5.54 | 45.87 | 1008.69 | 95.91 | 478.02 | 480.02034741363497 |
| 5.56 | 45.87 | 1006.99 | 96.48 | 476.61 | 479.7602256710553 |
| 5.65 | 40.72 | 1022.46 | 85.17 | 487.09 | 483.7798894431585 |
| 5.7 | 40.62 | 1016.07 | 84.94 | 482.82 | 483.2217349280458 |
| 5.8 | 45.87 | 1009.14 | 92.06 | 481.6 | 480.1171178824119 |
| 5.82 | 40.78 | 1024.82 | 96.01 | 470.02 | 482.0478125504672 |
| 5.84 | 43.02 | 1013.88 | 87.42 | 489.05 | 481.81327159305386 |
| 5.97 | 36.25 | 1029.65 | 86.74 | 487.03 | 484.6333949755666 |
| 6.02 | 41.38 | 1021.2 | 88.71 | 490.57 | 482.2826790358646 |
| 6.03 | 41.17 | 1019.81 | 84.2 | 488.57 | 482.86050781997415 |
| 6.04 | 41.14 | 1027.8 | 86.4 | 480.39 | 483.17821215232124 |
| 6.04 | 41.14 | 1027.92 | 87.12 | 481.37 | 483.0829476732433 |
| 6.06 | 41.17 | 1019.67 | 84.7 | 489.62 | 482.71830544679335 |
| 6.13 | 40.64 | 1020.69 | 94.57 | 481.13 | 481.35862683823984 |
| 6.14 | 39.4 | 1011.21 | 90.87 | 485.94 | 481.4164995749685 |
| 6.15 | 40.77 | 1022.42 | 88.57 | 482.49 | 482.3037528502627 |
| 6.17 | 36.25 | 1028.68 | 90.59 | 483.77 | 483.6070229944035 |
| 6.22 | 39.33 | 1012.31 | 93.23 | 491.77 | 481.0249164343975 |
| 6.25 | 39.64 | 1010.98 | 83.45 | 483.43 | 482.2081944229683 |
| 6.3 | 41.14 | 1027.45 | 86.11 | 481.49 | 482.6905244198958 |
| 6.34 | 40.64 | 1020.62 | 94.39 | 478.78 | 480.9741288614041 |
| 6.38 | 40.07 | 1018.53 | 60.2 | 492.96 | 485.8565717694332 |
| 6.45 | 40.02 | 1032.08 | 79.7 | 481.36 | 483.99243959507345 |
| 6.48 | 40.27 | 1010.55 | 82.12 | 486.68 | 481.76650494734884 |
| 6.49 | 43.65 | 1020.41 | 72.78 | 484.94 | 483.069724719673 |
| 6.52 | 39.85 | 1012.55 | 86.36 | 483.01 | 481.33834961288795 |
| 6.59 | 39.37 | 1020.34 | 77.92 | 488.17 | 483.1883946104104 |
| 6.65 | 39.33 | 1011.29 | 92.85 | 490.41 | 480.1679106982307 |
| 6.71 | 40.72 | 1022.78 | 80.69 | 483.11 | 482.4149038683481 |
| 6.72 | 38.91 | 1016.89 | 90.47 | 485.89 | 480.94068220101354 |
| 6.76 | 39.22 | 1013.91 | 77.0 | 487.08 | 482.508645049916 |
| 6.8 | 41.16 | 1023.17 | 95.4 | 477.8 | 480.01746628251317 |
| 6.84 | 41.06 | 1021.04 | 89.59 | 489.96 | 480.63940670945976 |
| 6.86 | 38.08 | 1019.62 | 77.37 | 486.92 | 483.0108446487773 |
| 6.91 | 36.08 | 1021.82 | 84.31 | 486.37 | 482.57972730795177 |
| 6.91 | 39.37 | 1019.58 | 71.02 | 488.2 | 483.51586402481416 |
| 6.93 | 41.14 | 1027.18 | 84.67 | 479.06 | 481.6634377951154 |
| 6.99 | 35.19 | 1019.32 | 68.95 | 480.05 | 484.68450470880435 |
| 6.99 | 39.37 | 1020.19 | 75.06 | 487.18 | 482.8218608903564 |
| 7.05 | 43.65 | 1018.41 | 72.36 | 480.47 | 481.8880244206695 |
| 7.1 | 35.77 | 1015.39 | 92.1 | 480.36 | 480.6306788292839 |
| 7.11 | 41.74 | 1022.35 | 90.68 | 487.85 | 479.89671820679695 |
| 7.11 | 43.13 | 1018.96 | 87.82 | 486.11 | 479.6914116057231 |
| 7.24 | 38.06 | 1020.6 | 85.36 | 481.83 | 481.1970697561745 |
| 7.3 | 43.65 | 1019.33 | 67.62 | 482.96 | 482.17217802621536 |
| 7.34 | 40.72 | 1023.01 | 80.08 | 483.92 | 481.3074409763495 |
| 7.4 | 41.04 | 1024.44 | 90.9 | 477.69 | 479.64992318380445 |
| 7.41 | 40.71 | 1023.07 | 83.32 | 474.25 | 480.70714895561014 |
| 7.44 | 41.04 | 1021.84 | 88.56 | 479.08 | 479.7024675196716 |
| 7.45 | 39.61 | 1017.88 | 79.73 | 478.89 | 481.00544489343537 |
| 7.46 | 41.82 | 1032.67 | 74.59 | 483.11 | 482.38901084355183 |
| 7.48 | 38.5 | 1014.01 | 77.35 | 488.43 | 481.25646633043965 |
| 7.52 | 41.66 | 1015.2 | 78.41 | 483.28 | 480.33371483717946 |
| 7.54 | 38.56 | 1016.49 | 69.1 | 486.76 | 482.5311759738776 |
| 7.55 | 41.04 | 1027.03 | 83.32 | 476.58 | 480.67721106043905 |
| 7.55 | 43.65 | 1019.09 | 61.71 | 481.61 | 482.53257783094546 |
| 7.57 | 41.14 | 1028.23 | 87.97 | 477.8 | 480.0330510466423 |
| 7.6 | 41.04 | 1021.82 | 88.97 | 475.32 | 479.3324132109004 |
| 7.65 | 41.01 | 1024.31 | 97.17 | 475.89 | 478.2499419685471 |
| 7.67 | 41.66 | 1016.25 | 77.0 | 485.03 | 480.3355565472517 |
| 7.69 | 36.24 | 1013.08 | 88.37 | 482.46 | 479.7315598782737 |
| 7.7 | 40.35 | 1011.72 | 92.88 | 484.57 | 477.91894784424176 |
| 7.73 | 37.8 | 1020.71 | 63.93 | 483.94 | 483.45191519870116 |
| 7.73 | 39.04 | 1018.61 | 68.23 | 482.39 | 482.34452319301437 |
| 7.76 | 42.28 | 1008.52 | 83.31 | 483.8 | 478.45760011663003 |
| 7.81 | 39.64 | 1011.42 | 86.68 | 482.22 | 478.7638216096892 |
| 7.82 | 40.72 | 1022.17 | 88.13 | 481.52 | 479.1388800137473 |
| 7.84 | 43.52 | 1022.23 | 96.51 | 483.79 | 477.1846287648614 |
| 7.87 | 42.85 | 1012.18 | 94.21 | 480.54 | 476.81117998099177 |
| 7.89 | 40.46 | 1019.61 | 74.53 | 477.27 | 480.84424359067077 |
| 7.9 | 40.0 | 1018.74 | 79.55 | 474.5 | 480.1364995691749 |
| 7.91 | 39.96 | 1023.57 | 88.44 | 475.52 | 479.2235127059344 |
| 7.92 | 39.54 | 1011.51 | 84.41 | 481.44 | 478.91505388939413 |
| 7.95 | 41.26 | 1008.48 | 97.92 | 480.6 | 476.210862698602 |
| 7.97 | 38.5 | 1013.84 | 72.36 | 487.19 | 481.0254321330136 |
| 8.01 | 40.46 | 1019.42 | 76.15 | 475.42 | 480.36098930984286 |
| 8.01 | 40.62 | 1015.62 | 86.43 | 476.22 | 478.51210495606927 |
| 8.01 | 41.66 | 1014.49 | 76.72 | 485.13 | 479.57731721621883 |
| 8.03 | 40.07 | 1016.06 | 47.46 | 488.02 | 484.33140555054337 |
| 8.04 | 40.64 | 1020.64 | 89.26 | 477.78 | 478.4450809561189 |
| 8.05 | 40.62 | 1018.16 | 73.67 | 477.2 | 480.5031526805204 |
| 8.07 | 43.41 | 1016.02 | 76.26 | 467.56 | 479.2169410835439 |
| 8.07 | 43.69 | 1017.05 | 87.34 | 485.18 | 477.6146348725092 |
| 8.11 | 41.92 | 1029.61 | 91.92 | 483.52 | 478.33312476559934 |
| 8.16 | 39.72 | 1020.54 | 82.11 | 480.21 | 479.4778900760471 |
| 8.18 | 40.02 | 1031.45 | 73.66 | 478.81 | 481.48536176155926 |
| 8.23 | 43.79 | 1016.11 | 82.11 | 484.67 | 477.9675154808001 |
| 8.24 | 39.61 | 1017.99 | 78.42 | 477.9 | 479.6817134321857 |
| 8.25 | 41.26 | 1020.59 | 91.84 | 475.17 | 477.5050067316079 |
| 8.26 | 40.96 | 1025.23 | 89.22 | 485.73 | 478.32045063849523 |
| 8.27 | 39.64 | 1011.0 | 84.64 | 479.91 | 478.1399555772117 |
| 8.28 | 40.56 | 1023.29 | 79.44 | 486.4 | 479.65037308403333 |
| 8.28 | 40.77 | 1011.55 | 89.79 | 480.15 | 477.1324329554068 |
| 8.3 | 36.3 | 1015.97 | 60.62 | 480.58 | 482.82343628916425 |
| 8.3 | 43.13 | 1020.02 | 83.11 | 484.07 | 478.16947013024355 |
| 8.33 | 38.08 | 1018.94 | 73.78 | 481.89 | 480.6437911412516 |
| 8.34 | 40.72 | 1023.62 | 83.75 | 483.14 | 478.8928744938167 |
| 8.35 | 43.52 | 1022.78 | 97.34 | 479.31 | 476.1246111330079 |
| 8.35 | 43.79 | 1016.2 | 85.23 | 484.21 | 477.288235770853 |
| 8.37 | 40.92 | 1021.82 | 86.03 | 476.02 | 478.30600580479216 |
| 8.39 | 36.24 | 1013.39 | 89.13 | 480.69 | 478.2957350932866 |
| 8.42 | 42.28 | 1008.4 | 87.78 | 481.91 | 476.52270993699136 |
| 8.46 | 40.8 | 1023.57 | 81.27 | 485.06 | 478.99917896902446 |
| 8.48 | 38.5 | 1013.5 | 66.51 | 485.29 | 480.8674382556021 |
| 8.51 | 38.5 | 1013.33 | 64.28 | 482.39 | 481.1210453702997 |
| 8.61 | 43.8 | 1021.9 | 74.35 | 478.25 | 478.8354389662744 |
| 8.63 | 39.96 | 1024.39 | 99.47 | 475.79 | 476.29244393984663 |
| 8.65 | 40.56 | 1023.23 | 78.85 | 485.87 | 479.0178844308566 |
| 8.67 | 40.77 | 1011.81 | 89.4 | 479.23 | 476.4582419334783 |
| 8.68 | 41.82 | 1032.83 | 73.62 | 478.61 | 480.1903466285615 |
| 8.72 | 36.25 | 1029.31 | 85.73 | 479.94 | 479.4487267515188 |
| 8.73 | 36.18 | 1013.66 | 77.74 | 479.25 | 479.3384367489267 |
| 8.73 | 41.92 | 1029.41 | 89.72 | 480.99 | 477.44189376811596 |
| 8.74 | 40.03 | 1016.81 | 93.37 | 481.07 | 476.33561360755584 |
| 8.75 | 36.3 | 1015.61 | 57.53 | 480.4 | 482.3769169335795 |
| 8.75 | 40.22 | 1008.96 | 90.53 | 482.8 | 476.04420182940044 |
| 8.76 | 41.82 | 1033.29 | 76.5 | 480.08 | 479.65335282852305 |
| 8.77 | 40.46 | 1019.68 | 77.84 | 475.0 | 478.6696951676176 |
| 8.8 | 42.32 | 1017.91 | 86.8 | 483.88 | 476.696926422392 |
| 8.81 | 43.56 | 1014.99 | 81.5 | 482.52 | 476.90393713153463 |
| 8.83 | 36.25 | 1028.86 | 85.6 | 478.45 | 479.2188844607746 |
| 8.85 | 40.22 | 1008.61 | 90.6 | 482.3 | 475.81261283946816 |
| 8.87 | 41.16 | 1023.17 | 94.13 | 472.45 | 476.2100211409317 |
| 8.88 | 36.66 | 1026.61 | 76.16 | 480.2 | 480.2141595165933 |
| 8.91 | 43.52 | 1022.78 | 98.0 | 478.38 | 474.9481764100585 |
| 8.93 | 40.46 | 1019.03 | 71.0 | 472.77 | 479.30598211413803 |
| 8.94 | 41.74 | 1022.55 | 90.74 | 483.53 | 476.3744577455605 |
| 8.96 | 40.02 | 1031.21 | 82.32 | 475.47 | 478.6980048877349 |
| 8.96 | 40.05 | 1015.91 | 89.4 | 467.24 | 476.41215657483474 |
| 8.99 | 36.66 | 1028.11 | 71.98 | 481.03 | 480.7338755379764 |
| 9.01 | 38.56 | 1016.67 | 63.61 | 482.37 | 480.51130475015583 |
| 9.03 | 40.71 | 1025.98 | 81.94 | 484.97 | 478.0206284049 |
| 9.04 | 44.68 | 1023.14 | 90.73 | 479.53 | 475.4980717304681 |
| 9.06 | 43.79 | 1016.05 | 81.32 | 482.8 | 476.4769333498689 |
| 9.08 | 36.18 | 1020.24 | 68.37 | 477.26 | 480.5658974037096 |
| 9.08 | 36.71 | 1025.07 | 81.32 | 479.02 | 478.9378167534134 |
| 9.08 | 40.02 | 1031.2 | 75.34 | 476.69 | 479.483969889582 |
| 9.11 | 40.64 | 1020.68 | 86.91 | 476.62 | 476.72728884411293 |
| 9.12 | 41.49 | 1020.58 | 96.23 | 475.69 | 475.1283411969007 |
| 9.12 | 41.54 | 1018.61 | 79.26 | 482.95 | 477.43108128919135 |
| 9.13 | 39.04 | 1022.36 | 74.6 | 483.24 | 479.0201634085648 |
| 9.13 | 39.16 | 1014.14 | 86.17 | 484.86 | 476.63324412261045 |
| 9.14 | 37.36 | 1015.22 | 78.06 | 491.97 | 478.3337308000573 |
| 9.15 | 39.61 | 1018.11 | 75.29 | 474.88 | 478.39283560851754 |
| 9.15 | 39.61 | 1018.69 | 84.47 | 475.64 | 477.10087603877 |
| 9.16 | 41.03 | 1021.3 | 76.08 | 484.96 | 478.16396362897893 |
| 9.19 | 40.62 | 1017.78 | 68.91 | 475.42 | 478.96772021173297 |
| 9.2 | 40.03 | 1017.05 | 92.46 | 480.38 | 475.6006326388746 |
| 9.26 | 44.68 | 1023.22 | 91.44 | 478.82 | 474.9766634864767 |
| 9.29 | 39.04 | 1022.72 | 74.29 | 482.55 | 478.786077505979 |
| 9.3 | 38.18 | 1017.19 | 71.51 | 480.14 | 478.9365615888623 |
| 9.31 | 43.56 | 1015.09 | 79.96 | 482.55 | 476.1723094438278 |
| 9.32 | 37.73 | 1022.14 | 79.49 | 477.91 | 478.2490255806092 |
| 9.35 | 44.03 | 1011.02 | 88.11 | 476.25 | 474.4577268339357 |
| 9.37 | 40.11 | 1024.94 | 75.03 | 471.13 | 478.43777544278043 |
| 9.39 | 39.66 | 1019.22 | 75.33 | 482.16 | 478.00197412694763 |
| 9.41 | 34.69 | 1027.02 | 78.91 | 480.87 | 479.3152324207446 |
| 9.41 | 39.61 | 1016.14 | 78.38 | 477.34 | 477.2801935898294 |
| 9.45 | 39.04 | 1023.08 | 75.81 | 483.66 | 478.285031656868 |
| 9.46 | 42.28 | 1008.67 | 78.16 | 481.95 | 475.9420528274367 |
| 9.47 | 41.4 | 1026.49 | 87.9 | 479.68 | 476.17198214059135 |
| 9.48 | 44.68 | 1023.29 | 92.9 | 478.66 | 474.34503134979343 |
| 9.5 | 37.36 | 1015.13 | 63.41 | 478.8 | 479.7691576930105 |
| 9.51 | 43.79 | 1016.02 | 79.81 | 481.12 | 475.82678857769963 |
| 9.53 | 38.18 | 1018.43 | 75.33 | 476.54 | 478.0366119605891 |
| 9.53 | 38.38 | 1020.49 | 80.08 | 478.03 | 477.46151999839185 |
| 9.55 | 38.08 | 1019.34 | 68.38 | 479.23 | 479.110912113517 |
| 9.55 | 39.66 | 1018.8 | 74.88 | 480.74 | 477.72481326338215 |
| 9.59 | 34.69 | 1027.65 | 75.32 | 478.88 | 479.54303529435083 |
| 9.59 | 38.56 | 1017.52 | 61.89 | 481.05 | 479.71268358186353 |
| 9.61 | 44.03 | 1008.3 | 91.36 | 473.54 | 473.26068816423447 |
| 9.63 | 41.14 | 1027.99 | 87.89 | 469.73 | 476.05175958076205 |
| 9.68 | 38.16 | 1015.54 | 79.67 | 475.51 | 476.88388448180046 |
| 9.68 | 41.06 | 1022.75 | 87.44 | 476.67 | 475.61433131158714 |
| 9.69 | 40.46 | 1019.1 | 71.91 | 472.16 | 477.7130066863276 |
| 9.72 | 41.44 | 1015.17 | 84.41 | 481.85 | 475.2673812565115 |
| 9.75 | 36.66 | 1026.21 | 70.12 | 479.45 | 479.3846135509556 |
| 9.76 | 39.16 | 1014.19 | 85.4 | 482.38 | 475.5344685772051 |
| 9.78 | 52.75 | 1022.97 | 78.31 | 469.58 | 473.85672752722735 |
| 9.82 | 39.64 | 1010.79 | 69.22 | 477.93 | 477.3826135030455 |
| 9.83 | 41.17 | 1019.34 | 72.29 | 478.21 | 477.2300569616709 |
| 9.84 | 36.66 | 1026.7 | 70.02 | 477.62 | 479.265495182268 |
| 9.86 | 37.83 | 1005.05 | 100.15 | 472.46 | 472.7773808573311 |
| 9.86 | 41.01 | 1018.49 | 98.71 | 467.37 | 473.2887424901299 |
| 9.87 | 40.81 | 1017.17 | 84.25 | 473.2 | 475.32128019247375 |
| 9.88 | 39.66 | 1017.81 | 76.05 | 480.05 | 476.83702080523557 |
| 9.92 | 40.64 | 1020.39 | 95.41 | 469.65 | 473.90133694043874 |
| 9.93 | 40.67 | 1018.08 | 69.74 | 485.23 | 477.4312500724956 |
| 9.95 | 43.56 | 1014.85 | 82.62 | 477.88 | 474.53026960482526 |
| 9.96 | 41.26 | 1022.9 | 83.83 | 475.21 | 475.5632280329792 |
| 9.96 | 41.55 | 1002.59 | 65.86 | 475.91 | 476.45899184845075 |
| 9.96 | 42.03 | 1017.78 | 82.39 | 477.85 | 475.16451288467897 |
| 9.97 | 41.62 | 1013.99 | 96.02 | 464.86 | 472.95056743270436 |
| 9.98 | 41.01 | 1017.83 | 98.07 | 466.05 | 473.09691475779204 |
| 9.98 | 41.62 | 1013.56 | 95.77 | 463.16 | 472.93274352761154 |
| 9.99 | 41.82 | 1033.14 | 68.36 | 475.75 | 478.4561215361668 |
| 9.99 | 42.07 | 1018.78 | 85.68 | 471.6 | 474.6981382928799 |
| 10.01 | 40.78 | 1023.71 | 88.11 | 470.82 | 475.02803269176394 |
| 10.02 | 41.44 | 1017.37 | 77.65 | 479.63 | 475.85397168574394 |
| 10.06 | 34.69 | 1027.9 | 71.73 | 477.68 | 479.18053767427625 |
| 10.08 | 43.14 | 1010.67 | 85.9 | 478.73 | 473.5654621009553 |
| 10.09 | 37.14 | 1012.99 | 72.59 | 473.66 | 477.1725989266351 |
| 10.1 | 41.4 | 1024.29 | 85.94 | 474.28 | 475.0636358283201 |
| 10.1 | 41.58 | 1021.26 | 94.06 | 468.19 | 473.5875494551498 |
| 10.11 | 41.62 | 1017.17 | 97.82 | 463.57 | 472.67682233352394 |
| 10.12 | 41.55 | 1005.78 | 62.34 | 475.46 | 476.9235641778536 |
| 10.12 | 43.72 | 1011.33 | 97.62 | 473.05 | 471.68772310073444 |
| 10.13 | 38.16 | 1013.57 | 79.04 | 474.92 | 475.9474342905188 |
| 10.15 | 41.46 | 1019.78 | 83.56 | 481.31 | 474.932278891215 |
| 10.15 | 43.41 | 1018.4 | 82.07 | 473.43 | 474.55112952359036 |
| 10.16 | 41.55 | 1005.69 | 58.04 | 477.27 | 477.46636654211125 |
| 10.18 | 43.5 | 1022.84 | 88.7 | 476.91 | 473.8650937482109 |
| 10.2 | 41.01 | 1021.39 | 96.64 | 468.27 | 473.17098851617544 |
| 10.2 | 41.46 | 1019.12 | 83.26 | 480.11 | 474.8258713039414 |
| 10.22 | 37.83 | 1005.94 | 93.53 | 471.79 | 473.1211730372522 |
| 10.23 | 41.46 | 1020.45 | 84.95 | 480.87 | 474.62974157133897 |
| 10.24 | 39.28 | 1010.13 | 81.61 | 477.53 | 474.801072598715 |
| 10.25 | 41.26 | 1007.44 | 98.08 | 476.03 | 471.6665106288944 |
| 10.25 | 41.46 | 1018.67 | 84.41 | 479.28 | 474.5250337253637 |
| 10.27 | 52.75 | 1026.19 | 76.78 | 470.76 | 473.3969220129792 |
| 10.28 | 39.64 | 1010.45 | 74.15 | 477.65 | 475.74847822607404 |
| 10.31 | 37.5 | 1008.55 | 99.31 | 474.16 | 472.3991408528041 |
| 10.31 | 38.18 | 1018.02 | 70.1 | 476.31 | 477.2616855096003 |
| 10.32 | 38.91 | 1012.11 | 81.49 | 479.17 | 474.9177051337058 |
| 10.33 | 40.62 | 1017.41 | 64.22 | 473.16 | 477.4228902388337 |
| 10.34 | 41.46 | 1017.75 | 89.73 | 478.03 | 473.50046202531144 |
| 10.37 | 37.83 | 1006.5 | 90.99 | 470.66 | 473.24796901874475 |
| 10.39 | 40.22 | 1006.59 | 87.77 | 479.14 | 473.09058493481064 |
| 10.4 | 42.44 | 1014.24 | 93.48 | 480.04 | 472.3076103285209 |
| 10.42 | 41.26 | 1008.48 | 96.76 | 472.54 | 471.615832151066 |
| 10.42 | 41.46 | 1021.04 | 89.16 | 479.24 | 473.69713759778955 |
| 10.44 | 37.83 | 1006.31 | 97.2 | 474.42 | 472.19156900447234 |
| 10.45 | 39.61 | 1020.23 | 73.39 | 477.41 | 476.3350912306915 |
| 10.46 | 37.5 | 1013.12 | 76.74 | 472.16 | 475.77435376625584 |
| 10.47 | 43.14 | 1010.35 | 86.86 | 476.55 | 472.6471168581127 |
| 10.51 | 37.5 | 1010.7 | 96.29 | 474.24 | 472.62895527440253 |
| 10.58 | 42.34 | 1022.32 | 94.01 | 474.91 | 472.5658087964315 |
| 10.59 | 38.38 | 1021.58 | 68.23 | 474.94 | 477.2343522450378 |
| 10.6 | 41.17 | 1018.38 | 87.92 | 478.47 | 473.38659302581107 |
| 10.6 | 41.46 | 1021.23 | 89.02 | 481.3 | 473.3858358704527 |
| 10.63 | 44.2 | 1018.63 | 90.26 | 477.19 | 472.2522916899094 |
| 10.66 | 41.93 | 1016.12 | 94.16 | 479.61 | 471.9871102146372 |
| 10.68 | 37.92 | 1009.59 | 65.05 | 474.03 | 476.66325912606675 |
| 10.7 | 44.77 | 1017.8 | 82.37 | 484.94 | 473.0585846959978 |
| 10.71 | 41.93 | 1018.23 | 90.88 | 478.76 | 472.5409240450936 |
| 10.73 | 40.35 | 1012.27 | 89.65 | 477.23 | 472.5905086170794 |
| 10.74 | 40.05 | 1015.45 | 87.33 | 477.93 | 473.2433331311532 |
| 10.76 | 44.58 | 1016.41 | 79.24 | 483.54 | 473.33367076102724 |
| 10.77 | 44.78 | 1012.87 | 84.16 | 470.66 | 472.25860679152254 |
| 10.82 | 39.96 | 1025.53 | 95.89 | 468.57 | 472.68332438968736 |
| 10.82 | 42.02 | 996.03 | 99.34 | 475.46 | 469.26491536026253 |
| 10.84 | 40.62 | 1015.53 | 60.9 | 467.6 | 476.77045249286635 |
| 10.89 | 44.2 | 1018.3 | 86.32 | 479.16 | 472.2986932100967 |
| 10.91 | 37.92 | 1008.66 | 66.53 | 473.72 | 475.9280130742943 |
| 10.94 | 25.36 | 1009.47 | 100.1 | 470.9 | 474.1703211862971 |
| 10.94 | 39.04 | 1021.81 | 86.02 | 479.2 | 473.8182300033406 |
| 10.94 | 40.81 | 1026.03 | 80.79 | 476.32 | 474.4834318795844 |
| 10.94 | 43.67 | 1012.36 | 73.3 | 477.34 | 473.75018039571677 |
| 10.98 | 37.5 | 1011.12 | 97.51 | 472.34 | 471.57861538146454 |
| 10.99 | 44.63 | 1020.67 | 90.09 | 473.29 | 471.6415723647869 |
| 11.01 | 43.69 | 1016.7 | 81.48 | 477.3 | 472.7701885173113 |
| 11.02 | 38.28 | 1013.51 | 95.66 | 472.11 | 471.77143688745184 |
| 11.02 | 40.0 | 1015.75 | 74.83 | 468.09 | 474.5636411763966 |
| 11.02 | 41.17 | 1018.18 | 86.86 | 477.62 | 472.7148284274264 |
| 11.04 | 39.96 | 1025.75 | 94.44 | 468.84 | 472.4884135100371 |
| 11.04 | 41.74 | 1022.6 | 77.51 | 477.2 | 474.2579394355058 |
| 11.06 | 37.92 | 1008.76 | 67.32 | 473.16 | 475.5315818680234 |
| 11.06 | 41.16 | 1018.52 | 89.14 | 467.46 | 472.3352405654698 |
| 11.06 | 41.5 | 1013.09 | 89.8 | 476.24 | 471.7121476384473 |
| 11.1 | 40.92 | 1021.98 | 94.14 | 462.07 | 471.87019509945225 |
| 11.16 | 40.96 | 1023.49 | 83.7 | 478.3 | 473.39040211351204 |
| 11.17 | 39.72 | 1002.4 | 81.4 | 474.64 | 472.2988980097268 |
| 11.17 | 40.27 | 1009.54 | 74.56 | 476.18 | 473.7408434668035 |
| 11.18 | 37.5 | 1013.32 | 74.32 | 472.02 | 474.7548947976392 |
| 11.18 | 39.61 | 1018.56 | 68.0 | 468.75 | 475.5773739728955 |
| 11.2 | 41.38 | 1021.65 | 61.89 | 476.87 | 476.2403822241514 |
| 11.2 | 42.02 | 999.99 | 96.69 | 472.27 | 469.24091010473035 |
| 11.22 | 40.22 | 1011.01 | 81.67 | 476.7 | 472.7393314749417 |
| 11.22 | 43.13 | 1017.24 | 80.9 | 473.93 | 472.63331852543564 |
| 11.29 | 41.5 | 1013.39 | 89.15 | 476.04 | 471.38775711954423 |
| 11.31 | 39.61 | 1018.74 | 68.9 | 471.92 | 475.2099855538805 |
| 11.38 | 52.75 | 1026.19 | 72.71 | 469.9 | 471.84963289726664 |
| 11.41 | 39.61 | 1018.69 | 69.44 | 467.19 | 474.9342554366788 |
| 11.48 | 41.14 | 1027.67 | 90.5 | 464.07 | 472.0765967355643 |
| 11.49 | 35.76 | 1019.08 | 60.04 | 472.45 | 477.1428353332941 |
| 11.49 | 44.2 | 1018.79 | 91.14 | 475.51 | 470.47813470324115 |
| 11.51 | 41.06 | 1021.3 | 78.11 | 476.91 | 473.32755876405025 |
| 11.53 | 41.14 | 1025.63 | 88.54 | 469.04 | 472.10000669812564 |
| 11.53 | 41.39 | 1018.39 | 85.52 | 474.49 | 471.88884153658796 |
| 11.54 | 40.05 | 1014.78 | 87.05 | 474.29 | 471.68655893301997 |
| 11.54 | 40.77 | 1022.13 | 83.5 | 465.61 | 472.62327183365306 |
| 11.56 | 39.28 | 1011.27 | 82.05 | 477.71 | 472.2836129719507 |
| 11.56 | 41.62 | 1012.5 | 91.42 | 460.6 | 470.4334505230224 |
| 11.57 | 39.72 | 1002.26 | 78.69 | 474.72 | 471.91129640538503 |
| 11.57 | 41.54 | 1020.13 | 69.14 | 476.89 | 474.3054501914308 |
| 11.62 | 39.72 | 1018.4 | 68.06 | 471.56 | 474.6794786091428 |
| 11.67 | 37.73 | 1021.2 | 68.88 | 473.54 | 475.187496898361 |
| 11.67 | 44.6 | 1018.09 | 92.53 | 467.96 | 469.771457326924 |
| 11.68 | 40.55 | 1022.21 | 85.76 | 475.13 | 472.08490735121984 |
| 11.7 | 25.36 | 1008.65 | 92.11 | 470.88 | 473.8032225628117 |
| 11.71 | 41.44 | 1015.37 | 79.95 | 474.22 | 472.09588182193215 |
| 11.72 | 40.35 | 1012.08 | 83.98 | 476.41 | 471.4926212025492 |
| 11.76 | 41.58 | 1020.91 | 88.35 | 465.45 | 471.19014476340215 |
| 11.77 | 39.39 | 1012.9 | 85.8 | 478.51 | 471.43677609572336 |
| 11.8 | 40.66 | 1017.13 | 97.2 | 464.43 | 469.7436046712689 |
| 11.8 | 41.2 | 1017.18 | 82.71 | 475.19 | 471.72684171223557 |
| 11.8 | 41.54 | 1020.0 | 65.85 | 476.12 | 474.3311768410223 |
| 11.8 | 42.34 | 1020.25 | 93.54 | 466.52 | 470.11266519044227 |
| 11.8 | 43.99 | 1020.86 | 98.44 | 466.75 | 469.0361408145477 |
| 11.82 | 41.54 | 1019.96 | 67.65 | 476.97 | 474.02676004123384 |
| 11.82 | 41.67 | 1012.94 | 84.51 | 476.73 | 470.9633331252549 |
| 11.84 | 40.05 | 1014.54 | 86.78 | 473.82 | 471.12775460619287 |
| 11.86 | 39.85 | 1013.0 | 66.92 | 478.29 | 473.91084477665686 |
| 11.87 | 40.55 | 1019.06 | 94.11 | 473.79 | 470.2438958288006 |
| 11.9 | 39.16 | 1016.53 | 84.59 | 477.75 | 471.7153938676623 |
| 11.92 | 43.8 | 1022.96 | 60.49 | 470.33 | 474.55914246739445 |
| 11.93 | 38.78 | 1013.15 | 96.08 | 474.57 | 469.8009518761225 |
| 11.95 | 41.58 | 1015.83 | 89.35 | 464.57 | 470.2642322610151 |
| 11.96 | 43.41 | 1017.42 | 79.44 | 469.34 | 471.3638012594579 |
| 12.02 | 41.92 | 1030.1 | 84.45 | 465.82 | 471.92094622344274 |
| 12.02 | 43.69 | 1016.12 | 74.07 | 477.74 | 471.85580587680386 |
| 12.04 | 40.1 | 1014.42 | 89.65 | 474.28 | 470.30107562853505 |
| 12.05 | 48.04 | 1009.14 | 100.13 | 464.14 | 466.34356012780466 |
| 12.05 | 49.83 | 1008.54 | 95.11 | 454.18 | 466.58075506687766 |
| 12.06 | 52.72 | 1024.53 | 80.05 | 467.21 | 469.3396022995035 |
| 12.07 | 38.25 | 1012.67 | 81.66 | 470.36 | 471.72756140636227 |
| 12.08 | 40.75 | 1015.84 | 86.9 | 476.84 | 470.5786344502193 |
| 12.09 | 40.6 | 1013.85 | 85.72 | 474.35 | 470.6068799512958 |
| 12.1 | 40.27 | 1005.53 | 68.37 | 477.61 | 472.5235663152981 |
| 12.1 | 41.17 | 1013.72 | 75.61 | 478.1 | 471.9097423001995 |
| 12.12 | 40.0 | 1018.72 | 84.42 | 462.1 | 471.2847044384862 |
| 12.14 | 40.83 | 1010.56 | 78.31 | 474.82 | 471.2662319288046 |
| 12.17 | 41.58 | 1013.89 | 88.98 | 463.03 | 469.73593028388524 |
| 12.19 | 40.05 | 1014.65 | 85.1 | 472.68 | 470.7066911497908 |
| 12.19 | 40.23 | 1017.45 | 82.07 | 474.91 | 471.33177180371854 |
| 12.19 | 44.63 | 1018.96 | 80.91 | 468.91 | 470.52692515963395 |
| 12.23 | 41.58 | 1018.76 | 87.66 | 464.45 | 470.2092170118331 |
| 12.24 | 49.83 | 1007.9 | 94.28 | 466.83 | 466.2832533837298 |
| 12.25 | 44.58 | 1016.47 | 81.15 | 475.24 | 470.18594349686214 |
| 12.27 | 41.17 | 1019.39 | 52.18 | 473.84 | 475.4613835085186 |
| 12.27 | 41.17 | 1019.41 | 58.1 | 475.13 | 474.5994035325814 |
| 12.3 | 39.85 | 1012.59 | 73.38 | 476.42 | 472.0863918606857 |
| 12.3 | 40.69 | 1015.74 | 82.58 | 474.46 | 470.7913069417126 |
| 12.31 | 44.03 | 1007.78 | 94.42 | 468.13 | 467.56407826412726 |
| 12.32 | 41.26 | 1022.42 | 79.74 | 470.27 | 471.5687225134983 |
| 12.32 | 43.69 | 1016.26 | 83.18 | 471.6 | 469.9595844589464 |
| 12.33 | 38.91 | 1017.24 | 79.84 | 472.49 | 471.6990473850642 |
| 12.33 | 39.85 | 1012.53 | 66.04 | 479.32 | 473.0943993730868 |
| 12.35 | 44.2 | 1017.81 | 82.49 | 471.65 | 470.00140680122996 |
| 12.36 | 40.56 | 1022.11 | 72.99 | 474.71 | 472.62554215897904 |
| 12.36 | 45.09 | 1013.05 | 88.4 | 467.2 | 468.51057584459073 |
| 12.36 | 48.04 | 1012.8 | 93.59 | 468.37 | 466.99762401287654 |
| 12.37 | 46.97 | 1013.95 | 90.76 | 464.4 | 467.7515630344035 |
| 12.38 | 45.09 | 1013.26 | 90.55 | 467.47 | 468.17545309508614 |
| 12.39 | 49.83 | 1007.6 | 92.43 | 468.43 | 466.2393815815799 |
| 12.42 | 41.58 | 1019.49 | 86.31 | 466.05 | 470.09910156010346 |
| 12.42 | 43.14 | 1015.88 | 79.48 | 471.1 | 470.4126440262426 |
| 12.43 | 43.22 | 1008.93 | 77.42 | 468.01 | 470.1081379355774 |
| 12.45 | 40.56 | 1017.84 | 66.52 | 477.41 | 473.04817642574005 |
| 12.47 | 38.25 | 1012.74 | 82.89 | 469.56 | 470.7822892277393 |
| 12.47 | 45.01 | 1017.8 | 95.25 | 467.18 | 467.70575905298347 |
| 12.49 | 41.62 | 1011.37 | 82.68 | 461.35 | 469.8226213597647 |
| 12.5 | 41.38 | 1021.87 | 57.59 | 474.4 | 474.3780743285961 |
| 12.5 | 43.67 | 1013.99 | 90.91 | 473.26 | 468.30493209232344 |
| 12.54 | 43.69 | 1017.26 | 83.59 | 470.04 | 469.5568353664925 |
| 12.55 | 39.58 | 1010.68 | 76.9 | 472.31 | 471.0025099661929 |
| 12.56 | 43.41 | 1016.93 | 81.02 | 467.19 | 469.9361134525985 |
| 12.57 | 39.16 | 1016.53 | 88.91 | 476.2 | 469.7928661275291 |
| 12.57 | 41.79 | 1014.99 | 87.8 | 461.88 | 469.1737219130261 |
| 12.58 | 39.72 | 1017.85 | 57.74 | 471.24 | 474.2884906123142 |
| 12.58 | 43.67 | 1014.36 | 91.72 | 473.02 | 468.06258267245875 |
| 12.59 | 39.18 | 1024.18 | 67.57 | 471.32 | 473.485146860658 |
| 12.6 | 41.74 | 1022.13 | 67.89 | 474.23 | 472.61404029065585 |
| 12.61 | 43.22 | 1013.41 | 78.94 | 466.85 | 469.903915514171 |
| 12.64 | 41.26 | 1020.79 | 83.66 | 465.78 | 470.2469481759357 |
| 12.65 | 44.34 | 1014.74 | 92.81 | 473.46 | 467.632447347929 |
| 12.68 | 41.4 | 1021.59 | 78.57 | 466.64 | 470.9425442114299 |
| 12.71 | 43.8 | 1023.15 | 71.16 | 466.2 | 471.494284203131 |
| 12.72 | 39.13 | 1008.36 | 92.59 | 467.28 | 468.30907898088435 |
| 12.72 | 40.64 | 1021.11 | 93.24 | 475.73 | 468.87573922525866 |
| 12.73 | 37.73 | 1021.89 | 61.76 | 470.89 | 474.237754775417 |
| 12.74 | 49.83 | 1007.44 | 91.47 | 466.09 | 465.69130458295615 |
| 12.75 | 39.85 | 1012.51 | 62.37 | 475.61 | 472.8180343417018 |
| 12.79 | 44.68 | 1022.51 | 99.55 | 465.75 | 466.9269506815448 |
| 12.83 | 41.67 | 1012.84 | 84.29 | 474.81 | 469.0391508364556 |
| 12.83 | 44.88 | 1017.86 | 87.88 | 474.26 | 468.1238036853617 |
| 12.87 | 39.3 | 1019.26 | 71.55 | 471.48 | 471.9340237695587 |
| 12.87 | 45.51 | 1015.3 | 86.67 | 475.77 | 467.85769076077656 |
| 12.88 | 44.0 | 1022.71 | 88.58 | 470.31 | 468.53947222591256 |
| 12.88 | 44.71 | 1018.73 | 51.95 | 469.12 | 473.38202950699997 |
| 12.89 | 36.71 | 1013.36 | 87.29 | 475.13 | 469.76472317857633 |
| 12.9 | 44.63 | 1020.72 | 89.51 | 467.41 | 468.04615604018346 |
| 12.92 | 39.35 | 1014.56 | 88.29 | 469.83 | 469.00047164404333 |
| 12.95 | 41.38 | 1021.97 | 53.83 | 474.46 | 474.0667420199239 |
| 12.99 | 40.55 | 1007.52 | 94.15 | 472.18 | 467.138305828078 |
| 13.02 | 45.51 | 1015.24 | 80.05 | 468.46 | 468.5292032616302 |
| 13.03 | 42.86 | 1014.39 | 86.25 | 475.03 | 468.1969526319267 |
| 13.04 | 40.69 | 1015.96 | 82.37 | 473.49 | 469.4125049461586 |
| 13.06 | 44.2 | 1018.95 | 85.68 | 469.02 | 468.259374271566 |
| 13.07 | 38.47 | 1015.16 | 57.26 | 468.9 | 473.50603668317063 |
| 13.07 | 40.83 | 1010.0 | 84.84 | 471.19 | 468.47422142636185 |
| 13.08 | 39.28 | 1012.41 | 77.98 | 474.13 | 470.0383017110002 |
| 13.08 | 44.9 | 1020.47 | 86.46 | 472.01 | 468.0562294553348 |
| 13.09 | 39.85 | 1012.86 | 58.42 | 475.89 | 472.76694427363464 |
| 13.09 | 54.3 | 1017.61 | 82.38 | 471.0 | 466.0557279256278 |
| 13.1 | 40.55 | 1007.59 | 95.46 | 472.37 | 466.7407287783581 |
| 13.1 | 49.83 | 1007.29 | 92.79 | 466.08 | 464.79214736202914 |
| 13.11 | 41.44 | 1015.55 | 74.45 | 471.61 | 470.21248865654456 |
| 13.12 | 39.18 | 1023.11 | 64.33 | 471.44 | 472.8484021647681 |
| 13.15 | 40.78 | 1024.13 | 79.59 | 462.3 | 470.24854120855605 |
| 13.15 | 41.14 | 1026.72 | 80.31 | 461.49 | 470.2646001553115 |
| 13.18 | 43.72 | 1010.59 | 99.09 | 464.7 | 465.51076750880605 |
| 13.19 | 44.88 | 1017.61 | 94.95 | 467.68 | 466.3776971038656 |
| 13.2 | 40.83 | 1007.75 | 94.98 | 472.41 | 466.5610830112806 |
| 13.2 | 41.78 | 1010.49 | 64.96 | 468.58 | 470.92659992793443 |
| 13.25 | 43.7 | 1013.61 | 76.05 | 472.22 | 468.98765579029424 |
| 13.25 | 44.92 | 1024.11 | 89.34 | 469.18 | 467.5995301073336 |
| 13.27 | 40.27 | 1006.1 | 40.04 | 473.88 | 474.44599166986796 |
| 13.34 | 41.79 | 1011.48 | 86.66 | 461.12 | 467.5690713933053 |
| 13.41 | 40.89 | 1010.85 | 89.61 | 472.4 | 467.1768048505422 |
| 13.42 | 40.92 | 1022.84 | 75.89 | 458.49 | 470.12758725908657 |
| 13.43 | 43.69 | 1016.21 | 73.01 | 475.36 | 469.2980914389405 |
| 13.44 | 40.69 | 1014.54 | 77.97 | 473.0 | 469.1672380696391 |
| 13.44 | 41.14 | 1026.41 | 77.26 | 461.44 | 470.1249314556345 |
| 13.48 | 41.92 | 1030.2 | 65.96 | 463.9 | 471.81028761580865 |
| 13.49 | 43.41 | 1015.75 | 57.86 | 461.06 | 471.424799923348 |
| 13.51 | 39.31 | 1012.18 | 75.19 | 466.46 | 469.58969888462434 |
| 13.53 | 38.73 | 1004.86 | 85.38 | 472.46 | 467.6133054100996 |
| 13.53 | 42.86 | 1014.0 | 90.63 | 471.73 | 466.56182696263306 |
| 13.54 | 40.69 | 1015.85 | 77.55 | 471.05 | 469.14226719628124 |
| 13.55 | 40.71 | 1019.13 | 75.44 | 467.21 | 469.69281643752356 |
| 13.56 | 40.03 | 1017.73 | 83.76 | 472.59 | 468.5153727218602 |
| 13.56 | 49.83 | 1007.01 | 89.86 | 466.33 | 464.3095114085993 |
| 13.57 | 42.99 | 1007.51 | 88.95 | 472.04 | 466.169002951847 |
| 13.58 | 39.28 | 1016.17 | 79.17 | 472.17 | 469.2063750462149 |
| 13.61 | 38.47 | 1015.1 | 54.98 | 466.51 | 472.79218089209587 |
| 13.65 | 41.58 | 1020.56 | 72.17 | 460.8 | 469.8764663630868 |
| 13.67 | 41.48 | 1017.51 | 63.54 | 463.97 | 470.87346939701916 |
| 13.67 | 42.32 | 1015.41 | 79.04 | 464.56 | 468.2319508045607 |
| 13.69 | 34.03 | 1018.45 | 65.76 | 469.12 | 472.4449714286485 |
| 13.69 | 40.83 | 1008.53 | 84.81 | 471.26 | 467.1630433917525 |
| 13.71 | 43.41 | 1015.45 | 69.26 | 466.06 | 469.3130021437414 |
| 13.72 | 44.47 | 1027.2 | 68.89 | 470.66 | 470.0399558829108 |
| 13.72 | 49.83 | 1006.88 | 89.49 | 463.65 | 464.0442884425802 |
| 13.73 | 45.08 | 1023.55 | 81.64 | 470.35 | 467.7114787859095 |
| 13.74 | 42.74 | 1029.54 | 70.0 | 465.92 | 470.46126451393184 |
| 13.74 | 44.21 | 1023.26 | 84.2 | 466.67 | 467.51203531407947 |
| 13.77 | 42.86 | 1030.72 | 77.3 | 471.38 | 469.4046202019984 |
| 13.77 | 43.13 | 1016.63 | 62.13 | 468.45 | 470.40326389642064 |
| 13.78 | 44.47 | 1027.94 | 71.09 | 467.22 | 469.66353144520883 |
| 13.78 | 45.78 | 1025.27 | 95.72 | 462.88 | 465.52654943877593 |
| 13.8 | 39.82 | 1012.37 | 83.69 | 473.56 | 467.6786715108734 |
| 13.83 | 38.73 | 999.62 | 91.95 | 469.81 | 465.64964430715304 |
| 13.84 | 42.18 | 1015.74 | 79.76 | 468.66 | 467.8607823790049 |
| 13.85 | 45.08 | 1024.86 | 83.85 | 468.35 | 467.26426723260613 |
| 13.86 | 37.85 | 1011.4 | 89.7 | 469.94 | 467.0983914780623 |
| 13.86 | 40.66 | 1017.15 | 83.82 | 463.49 | 467.7236799517389 |
| 13.87 | 45.08 | 1024.42 | 81.69 | 465.48 | 467.5049711098421 |
| 13.88 | 48.79 | 1017.28 | 79.4 | 464.14 | 466.31353063126903 |
| 13.9 | 39.54 | 1007.01 | 81.33 | 471.16 | 467.46352561567426 |
| 13.91 | 44.58 | 1021.36 | 78.98 | 472.67 | 467.6987016384138 |
| 13.93 | 42.86 | 1032.37 | 71.11 | 468.88 | 470.13332337428324 |
| 13.95 | 40.2 | 1013.18 | 90.77 | 464.79 | 466.32771593378925 |
| 13.95 | 71.14 | 1019.76 | 75.51 | 461.18 | 461.3756491943329 |
| 13.96 | 39.54 | 1011.05 | 85.72 | 468.82 | 467.03627043956425 |
| 13.97 | 45.01 | 1017.44 | 89.15 | 461.96 | 465.6730488393365 |
| 13.98 | 44.84 | 1023.6 | 89.09 | 462.81 | 466.20636936169376 |
| 14.0 | 41.78 | 1010.96 | 48.37 | 465.62 | 471.8419294419814 |
| 14.01 | 45.08 | 1023.28 | 82.49 | 464.79 | 467.025423832653 |
| 14.02 | 40.66 | 1017.05 | 84.14 | 465.39 | 467.36024219232854 |
| 14.03 | 44.88 | 1019.6 | 57.63 | 465.51 | 470.36369995609516 |
| 14.04 | 40.2 | 1013.29 | 89.54 | 465.25 | 466.34250670048556 |
| 14.04 | 40.83 | 1008.98 | 82.04 | 469.75 | 466.9286675356811 |
| 14.04 | 44.21 | 1021.93 | 86.12 | 468.64 | 466.5450197688411 |
| 14.07 | 40.78 | 1024.67 | 72.66 | 456.71 | 469.52890927618625 |
| 14.07 | 42.99 | 1007.57 | 96.05 | 468.87 | 464.17371789096717 |
| 14.07 | 43.34 | 1015.79 | 86.0 | 463.77 | 466.22172115408836 |
| 14.08 | 39.31 | 1011.67 | 72.0 | 464.16 | 468.9140947361635 |
| 14.09 | 41.2 | 1016.45 | 67.27 | 472.42 | 469.50273867747836 |
| 14.09 | 44.84 | 1023.65 | 94.29 | 466.12 | 465.2396919188332 |
| 14.1 | 41.04 | 1026.11 | 74.25 | 465.89 | 469.291500068156 |
| 14.1 | 42.18 | 1015.05 | 78.25 | 463.3 | 467.5233892738298 |
| 14.12 | 39.52 | 1016.79 | 75.89 | 472.32 | 468.6339203654876 |
| 14.12 | 41.39 | 1018.73 | 76.51 | 472.88 | 468.23518412428797 |
| 14.14 | 39.82 | 1012.46 | 81.15 | 472.52 | 467.40072495010907 |
| 14.17 | 42.86 | 1030.94 | 66.47 | 466.2 | 470.2308690130346 |
| 14.18 | 39.3 | 1020.1 | 67.48 | 464.32 | 470.06934793478035 |
| 14.18 | 40.69 | 1014.73 | 74.88 | 471.52 | 468.2061275247934 |
| 14.22 | 37.85 | 1011.24 | 88.49 | 471.05 | 466.5674959516581 |
| 14.22 | 42.32 | 1015.54 | 77.23 | 465.0 | 467.4457105564226 |
| 14.24 | 39.52 | 1018.22 | 77.19 | 468.51 | 468.3292283291916 |
| 14.24 | 39.99 | 1009.3 | 83.75 | 466.2 | 466.52892029567596 |
| 14.24 | 44.84 | 1023.6 | 94.06 | 466.67 | 464.97984688167367 |
| 14.27 | 41.48 | 1014.83 | 62.7 | 458.19 | 469.62052738975217 |
| 14.28 | 42.77 | 1020.06 | 81.77 | 466.75 | 466.9234567199092 |
| 14.28 | 43.02 | 1012.97 | 49.44 | 467.83 | 471.0002382734735 |
| 14.31 | 40.78 | 1024.3 | 76.41 | 463.54 | 468.4888161194508 |
| 14.32 | 45.08 | 1023.24 | 84.53 | 467.21 | 466.1266303832576 |
| 14.33 | 45.51 | 1015.42 | 71.55 | 468.92 | 467.25704554531416 |
| 14.34 | 42.86 | 1031.75 | 66.81 | 466.17 | 469.9193063400854 |
| 14.35 | 40.71 | 1023.09 | 69.5 | 473.56 | 469.33864000185486 |
| 14.35 | 49.83 | 1006.39 | 91.23 | 462.54 | 462.5353944778779 |
| 14.36 | 40.0 | 1016.16 | 68.79 | 460.42 | 469.0357845125853 |
| 14.38 | 40.66 | 1016.34 | 92.37 | 463.1 | 465.4074674853422 |
| 14.39 | 43.56 | 1012.97 | 59.17 | 469.35 | 469.2340241993221 |
| 14.4 | 40.2 | 1013.04 | 90.5 | 464.5 | 465.48772540492894 |
| 14.41 | 40.71 | 1016.78 | 69.77 | 467.01 | 468.6698381136833 |
| 14.41 | 40.83 | 1009.82 | 80.43 | 470.13 | 466.5182432985413 |
| 14.42 | 41.16 | 1004.32 | 88.42 | 467.25 | 464.80335793821706 |
| 14.43 | 35.85 | 1021.99 | 78.25 | 464.6 | 469.0300144538734 |
| 14.48 | 39.0 | 1016.75 | 75.97 | 464.56 | 468.0542535304745 |
| 14.48 | 40.89 | 1011.39 | 82.18 | 470.44 | 466.2407858068176 |
| 14.5 | 41.76 | 1023.94 | 84.42 | 464.23 | 466.68020136327283 |
| 14.52 | 40.35 | 1011.11 | 69.84 | 470.8 | 468.07562484757494 |
| 14.54 | 41.17 | 1015.15 | 67.78 | 470.19 | 468.4620083288529 |
| 14.54 | 43.14 | 1010.26 | 82.98 | 465.45 | 465.35539799683 |
| 14.55 | 44.84 | 1023.83 | 87.6 | 465.14 | 465.34301144661265 |
| 14.57 | 41.79 | 1007.61 | 82.85 | 457.21 | 465.4373435562456 |
| 14.58 | 41.92 | 1030.42 | 61.96 | 462.69 | 470.2899851111997 |
| 14.58 | 42.07 | 1017.9 | 86.14 | 460.66 | 465.705988879045 |
| 14.64 | 44.92 | 1025.54 | 91.12 | 462.64 | 464.775180629532 |
| 14.64 | 45.0 | 1021.78 | 41.25 | 475.98 | 471.7241650123044 |
| 14.65 | 35.4 | 1016.16 | 60.26 | 469.61 | 470.86762962520095 |
| 14.65 | 41.92 | 1030.61 | 63.07 | 464.95 | 470.0085068177468 |
| 14.65 | 44.84 | 1023.39 | 87.76 | 467.18 | 465.090966572103 |
| 14.66 | 41.76 | 1022.12 | 78.13 | 463.6 | 467.14100725665213 |
| 14.66 | 42.07 | 1018.14 | 84.68 | 462.77 | 465.7842034756254 |
| 14.72 | 39.0 | 1016.42 | 76.42 | 464.93 | 467.49881987016624 |
| 14.74 | 43.71 | 1024.35 | 81.53 | 465.49 | 466.18608052784475 |
| 14.76 | 39.64 | 1010.37 | 81.99 | 465.82 | 465.9570356485115 |
| 14.77 | 48.06 | 1010.92 | 69.81 | 461.52 | 465.6600911572598 |
| 14.77 | 58.2 | 1018.78 | 83.83 | 460.54 | 461.72665249570616 |
| 14.78 | 38.58 | 1017.02 | 82.4 | 460.54 | 466.6642858393167 |
| 14.79 | 47.83 | 1007.27 | 92.04 | 463.22 | 462.1388114830899 |
| 14.81 | 39.58 | 1011.62 | 73.64 | 467.32 | 467.1954080636746 |
| 14.81 | 40.71 | 1018.54 | 73.0 | 467.66 | 467.57038570428426 |
| 14.81 | 43.69 | 1017.19 | 71.9 | 470.71 | 466.8779893764293 |
| 14.82 | 42.74 | 1028.04 | 69.81 | 466.34 | 468.28371557585353 |
| 14.83 | 53.82 | 1016.57 | 63.47 | 464.0 | 465.49312878230023 |
| 14.84 | 71.14 | 1019.61 | 66.78 | 454.16 | 460.9202947940051 |
| 14.85 | 45.01 | 1013.12 | 85.53 | 460.19 | 464.1520666190232 |
| 14.86 | 37.85 | 1010.58 | 86.8 | 467.71 | 465.5258417359535 |
| 14.87 | 41.23 | 997.39 | 82.06 | 465.01 | 464.28156360850716 |
| 14.87 | 54.3 | 1017.17 | 71.57 | 462.87 | 464.1635216732549 |
| 14.88 | 42.28 | 1007.26 | 71.3 | 466.73 | 466.3736543730528 |
| 14.91 | 39.28 | 1014.57 | 75.23 | 469.34 | 467.08552274770574 |
| 14.91 | 40.73 | 1017.44 | 86.91 | 458.99 | 465.25377872135545 |
| 14.92 | 41.16 | 1004.83 | 83.7 | 465.72 | 464.56900508273293 |
| 14.92 | 46.18 | 1014.21 | 98.82 | 465.63 | 461.87533959682145 |
| 14.93 | 42.44 | 1012.65 | 86.8 | 471.24 | 464.4149735638803 |
| 14.94 | 40.0 | 1017.69 | 65.43 | 456.41 | 468.5317634787664 |
| 14.94 | 42.77 | 1018.06 | 75.35 | 460.51 | 466.42415027580006 |
| 14.98 | 39.58 | 1011.78 | 75.07 | 467.77 | 466.6719213555882 |
| 15.0 | 40.66 | 1016.28 | 89.62 | 456.63 | 464.60786745115865 |
| 15.01 | 39.52 | 1017.53 | 72.0 | 468.02 | 467.544960954765 |
| 15.02 | 37.64 | 1016.49 | 83.28 | 463.93 | 466.26419991369926 |
| 15.03 | 43.71 | 1025.07 | 83.25 | 463.12 | 465.43441550001705 |
| 15.03 | 44.45 | 1020.94 | 65.57 | 460.06 | 467.4928608009505 |
| 15.08 | 42.77 | 1018.67 | 73.89 | 461.6 | 466.41675498345813 |
| 15.09 | 41.76 | 1022.4 | 76.22 | 463.27 | 466.61302775183003 |
| 15.11 | 43.67 | 1012.06 | 91.75 | 467.82 | 462.99098636012917 |
| 15.12 | 48.92 | 1011.8 | 72.93 | 462.59 | 464.3870766666383 |
| 15.14 | 39.72 | 1002.96 | 72.52 | 460.15 | 465.9823776729848 |
| 15.14 | 44.21 | 1019.97 | 83.86 | 463.1 | 464.5934173732165 |
| 15.15 | 53.82 | 1016.34 | 62.59 | 461.6 | 464.9855482511964 |
| 15.19 | 42.03 | 1017.38 | 71.66 | 462.64 | 466.6093718548404 |
| 15.21 | 50.88 | 1014.24 | 100.11 | 460.56 | 459.9584434481331 |
| 15.24 | 48.6 | 1007.08 | 86.49 | 459.85 | 461.87302187524506 |
| 15.27 | 38.73 | 1002.83 | 77.77 | 465.99 | 465.2019993258076 |
| 15.27 | 39.54 | 1010.64 | 81.91 | 464.49 | 465.03190605144545 |
| 15.29 | 38.73 | 1000.9 | 81.17 | 468.62 | 464.51031407803373 |
| 15.31 | 40.66 | 1016.46 | 84.64 | 458.26 | 464.7510595901315 |
| 15.31 | 41.35 | 1005.09 | 95.25 | 466.5 | 462.10563966655616 |
| 15.32 | 45.01 | 1013.3 | 83.72 | 459.31 | 463.52420449023833 |
| 15.34 | 43.5 | 1021.18 | 79.44 | 459.77 | 465.12795484714036 |
| 15.34 | 71.14 | 1019.79 | 77.56 | 457.1 | 458.39794118753656 |
| 15.4 | 38.73 | 1000.67 | 79.71 | 469.18 | 464.49240158564226 |
| 15.41 | 42.44 | 1012.6 | 86.74 | 472.28 | 463.4938095964465 |
| 15.46 | 42.07 | 1017.9 | 81.12 | 459.15 | 464.74092024201923 |
| 15.47 | 43.13 | 1015.11 | 50.5 | 466.63 | 468.69706628494185 |
| 15.48 | 53.82 | 1016.1 | 64.77 | 462.69 | 464.01147313398934 |
| 15.5 | 44.34 | 1019.21 | 65.21 | 468.53 | 466.52540885533836 |
| 15.5 | 49.25 | 1021.41 | 77.92 | 453.99 | 463.6262300043777 |
| 15.52 | 41.93 | 1022.97 | 52.92 | 471.97 | 469.18664060318713 |
| 15.52 | 58.59 | 1014.12 | 91.22 | 457.74 | 458.7253721171194 |
| 15.55 | 39.63 | 1004.98 | 89.82 | 466.83 | 462.85471321992253 |
| 15.55 | 43.02 | 1011.97 | 44.66 | 466.2 | 469.16650047432273 |
| 15.55 | 43.71 | 1024.34 | 79.61 | 465.14 | 464.90298984127037 |
| 15.55 | 45.09 | 1014.33 | 62.19 | 457.36 | 466.2852656032666 |
| 15.61 | 38.52 | 1018.4 | 80.99 | 439.21 | 465.39633546049805 |
| 15.62 | 40.12 | 1013.03 | 96.26 | 462.59 | 462.31339644999167 |
| 15.62 | 58.59 | 1013.91 | 97.6 | 457.3 | 457.58467899039783 |
| 15.66 | 41.35 | 1001.68 | 86.26 | 463.57 | 462.46440155189674 |
| 15.67 | 45.17 | 1018.73 | 94.74 | 462.09 | 461.6436673101792 |
| 15.69 | 37.87 | 1021.18 | 84.38 | 465.41 | 465.13586499514474 |
| 15.69 | 39.3 | 1019.03 | 60.57 | 464.17 | 468.0777122780922 |
| 15.7 | 42.99 | 1007.51 | 76.05 | 464.59 | 463.94240853336476 |
| 15.75 | 36.99 | 1007.67 | 93.8 | 464.38 | 462.7655254273002 |
| 15.75 | 39.99 | 1007.02 | 77.44 | 464.95 | 464.3512532840681 |
| 15.79 | 58.86 | 1015.85 | 91.91 | 455.15 | 458.1774466179169 |
| 15.81 | 58.59 | 1014.41 | 90.03 | 456.91 | 458.36321179088014 |
| 15.84 | 41.04 | 1025.64 | 63.43 | 456.21 | 467.4754642409999 |
| 15.85 | 42.28 | 1007.4 | 82.12 | 467.3 | 462.93565136830296 |
| 15.85 | 49.69 | 1015.48 | 88.65 | 464.72 | 460.79339608207107 |
| 15.86 | 38.62 | 1016.65 | 67.51 | 462.58 | 466.71318747004864 |
| 15.86 | 43.02 | 1012.18 | 40.33 | 466.6 | 469.2173130099923 |
| 15.86 | 43.5 | 1021.22 | 75.38 | 459.42 | 464.720482732063 |
| 15.92 | 37.64 | 1014.93 | 83.73 | 464.14 | 464.335595847906 |
| 15.92 | 39.16 | 1006.59 | 71.32 | 460.45 | 465.0880608446689 |
| 15.92 | 41.2 | 1016.04 | 73.37 | 468.91 | 465.0497057218854 |
| 15.96 | 41.66 | 1011.93 | 55.47 | 466.39 | 467.13452750857033 |
| 15.98 | 39.64 | 1009.31 | 81.21 | 467.22 | 463.63133669846115 |
| 15.98 | 44.68 | 1018.48 | 85.94 | 462.77 | 462.43127985662505 |
| 15.99 | 39.63 | 1006.09 | 89.92 | 464.95 | 462.0817954663473 |
| 16.0 | 40.66 | 1016.12 | 89.23 | 457.12 | 462.7228887148107 |
| 16.0 | 44.9 | 1020.5 | 80.89 | 461.5 | 463.2389898882613 |
| 16.0 | 45.09 | 1014.31 | 60.02 | 458.46 | 465.7322155460913 |
| 16.01 | 43.69 | 1017.12 | 62.43 | 465.89 | 465.9391561803266 |
| 16.02 | 39.54 | 1007.73 | 72.81 | 466.15 | 464.67588000342283 |
| 16.02 | 44.9 | 1009.3 | 82.62 | 455.48 | 462.03627301808893 |
| 16.09 | 44.71 | 1017.86 | 42.74 | 464.95 | 468.46315966006046 |
| 16.09 | 65.46 | 1013.84 | 85.52 | 454.88 | 456.7218449478403 |
| 16.12 | 45.87 | 1008.15 | 86.12 | 457.41 | 460.9973530979208 |
| 16.14 | 44.71 | 1014.83 | 39.41 | 468.88 | 468.60583110746245 |
| 16.16 | 25.88 | 1009.58 | 72.24 | 461.86 | 468.0452621538944 |
| 16.17 | 46.97 | 1014.22 | 85.8 | 456.08 | 461.16748971043216 |
| 16.19 | 36.99 | 1007.37 | 92.4 | 462.94 | 462.09664222758136 |
| 16.2 | 45.76 | 1014.73 | 89.84 | 460.87 | 460.8634611992421 |
| 16.22 | 37.87 | 1022.36 | 83.13 | 461.06 | 464.39198726436115 |
| 16.22 | 50.88 | 1014.33 | 100.09 | 454.94 | 458.02055270119473 |
| 16.23 | 43.69 | 1016.4 | 68.9 | 466.22 | 464.5123542802937 |
| 16.29 | 53.82 | 1014.97 | 73.15 | 459.95 | 461.1346442030387 |
| 16.3 | 41.16 | 1007.88 | 76.61 | 463.47 | 463.18977819659995 |
| 16.31 | 52.75 | 1024.4 | 55.69 | 456.58 | 464.6775725663142 |
| 16.32 | 43.56 | 1014.4 | 59.77 | 463.57 | 465.5402356742791 |
| 16.33 | 42.44 | 1013.98 | 84.9 | 462.44 | 462.1000323229214 |
| 16.34 | 47.45 | 1009.41 | 92.96 | 448.59 | 459.28384302135794 |
| 16.36 | 39.99 | 1008.91 | 72.48 | 462.5 | 464.0520812837949 |
| 16.37 | 36.99 | 1006.37 | 90.11 | 463.76 | 462.0021066209579 |
| 16.39 | 52.75 | 1024.42 | 54.61 | 459.48 | 464.6824431291315 |
| 16.39 | 58.59 | 1014.58 | 90.34 | 455.05 | 457.21309738475657 |
| 16.4 | 44.9 | 1009.22 | 82.31 | 456.11 | 461.3420214113817 |
| 16.42 | 40.56 | 1020.36 | 50.62 | 472.17 | 467.91529134370023 |
| 16.47 | 38.01 | 1022.3 | 72.29 | 461.54 | 465.4513233379416 |
| 16.47 | 44.89 | 1010.04 | 82.01 | 459.69 | 461.3200136826322 |
| 16.49 | 49.39 | 1018.1 | 93.13 | 461.54 | 459.19347653551756 |
| 16.5 | 49.39 | 1018.35 | 93.42 | 462.48 | 459.1522349051031 |
| 16.51 | 51.86 | 1022.37 | 81.18 | 442.48 | 460.6299621913311 |
| 16.55 | 41.66 | 1011.45 | 55.53 | 465.14 | 465.94867946942674 |
| 16.56 | 42.99 | 1007.48 | 74.45 | 464.62 | 462.5145658398506 |
| 16.57 | 43.7 | 1015.67 | 71.95 | 465.78 | 463.3496922983309 |
| 16.57 | 53.82 | 1015.17 | 63.69 | 462.52 | 461.99087118644087 |
| 16.57 | 63.31 | 1016.19 | 81.02 | 454.78 | 457.1797966088055 |
| 16.59 | 43.56 | 1012.88 | 59.61 | 465.03 | 464.9190479188059 |
| 16.62 | 39.99 | 1007.07 | 77.14 | 463.74 | 462.72099107520137 |
| 16.62 | 54.3 | 1017.99 | 63.76 | 459.59 | 461.9941154580848 |
| 16.65 | 46.18 | 1010.6 | 95.69 | 465.6 | 458.7011562788462 |
| 16.7 | 36.99 | 1006.19 | 89.33 | 464.7 | 461.4647200415145 |
| 16.73 | 54.3 | 1017.96 | 59.44 | 460.54 | 462.40970062782714 |
| 16.77 | 42.28 | 1007.53 | 73.19 | 465.52 | 462.47440172010425 |
| 16.82 | 41.66 | 1010.49 | 63.14 | 465.64 | 464.23959443772316 |
| 16.82 | 45.0 | 1022.05 | 37.28 | 468.22 | 468.12040219816635 |
| 16.85 | 39.64 | 1008.82 | 80.81 | 464.2 | 461.97170222353844 |
| 16.85 | 42.24 | 1017.43 | 66.01 | 472.63 | 464.18342103476823 |
| 16.87 | 52.05 | 1012.7 | 71.63 | 460.31 | 460.4941449517029 |
| 16.89 | 49.21 | 1015.19 | 70.39 | 458.25 | 461.5472235985969 |
| 16.94 | 49.64 | 1024.43 | 69.22 | 459.25 | 462.26646301676067 |
| 16.97 | 42.86 | 1013.92 | 74.8 | 463.62 | 462.2293585659873 |
| 17.01 | 44.2 | 1019.18 | 61.23 | 457.26 | 464.22591401634116 |
| 17.02 | 51.86 | 1021.53 | 81.28 | 460.0 | 459.5632798612247 |
| 17.03 | 43.99 | 1021.5 | 82.32 | 460.25 | 461.3519558174483 |
| 17.07 | 41.35 | 1005.88 | 83.43 | 464.6 | 460.4994805562497 |
| 17.08 | 38.58 | 1015.41 | 73.42 | 461.49 | 463.40687222781077 |
| 17.08 | 58.86 | 1016.04 | 87.46 | 449.98 | 456.35386691539543 |
| 17.19 | 43.14 | 1014.34 | 68.62 | 464.72 | 462.67093183857946 |
| 17.27 | 43.52 | 1021.37 | 76.73 | 460.08 | 461.81109623075827 |
| 17.27 | 44.9 | 1007.85 | 78.8 | 454.19 | 460.0644340540906 |
| 17.29 | 42.86 | 1014.38 | 72.3 | 464.27 | 462.014274652855 |
| 17.3 | 43.72 | 1009.64 | 77.86 | 456.55 | 460.5836092644097 |
| 17.32 | 43.7 | 1015.13 | 61.66 | 464.5 | 463.360199769915 |
| 17.32 | 44.34 | 1019.52 | 56.24 | 468.8 | 464.34868588625807 |
| 17.35 | 42.86 | 1014.62 | 74.16 | 465.16 | 461.6467454037935 |
| 17.35 | 52.72 | 1026.31 | 58.01 | 463.65 | 462.4960995942487 |
| 17.36 | 43.96 | 1013.02 | 79.59 | 466.36 | 460.43082906265056 |
| 17.37 | 48.92 | 1011.91 | 58.4 | 455.53 | 462.1757595182412 |
| 17.39 | 46.21 | 1013.94 | 82.73 | 454.06 | 459.4288341311474 |
| 17.4 | 63.09 | 1020.84 | 92.58 | 453.0 | 454.3258801823864 |
| 17.41 | 40.55 | 1003.91 | 76.87 | 461.47 | 460.83972369537236 |
| 17.44 | 44.89 | 1009.9 | 80.54 | 457.67 | 459.652078633235 |
| 17.45 | 50.9 | 1012.16 | 83.8 | 458.67 | 457.84281119185783 |
| 17.46 | 39.99 | 1008.52 | 69.73 | 461.01 | 462.29977029786545 |
| 17.48 | 52.9 | 1020.03 | 76.47 | 458.34 | 458.99629100134234 |
| 17.61 | 56.53 | 1019.5 | 82.3 | 457.01 | 456.94689658887796 |
| 17.64 | 57.76 | 1016.28 | 85.04 | 455.75 | 455.92052796835753 |
| 17.66 | 60.08 | 1017.22 | 86.96 | 456.62 | 455.0999707958263 |
| 17.67 | 45.09 | 1014.26 | 51.92 | 457.67 | 463.6885973525347 |
| 17.67 | 50.88 | 1015.64 | 90.55 | 456.16 | 456.72206228080466 |
| 17.7 | 49.21 | 1015.16 | 67.91 | 455.97 | 460.34419741616995 |
| 17.74 | 49.69 | 1006.09 | 80.7 | 457.05 | 457.5432003190509 |
| 17.74 | 50.88 | 1015.56 | 89.78 | 457.71 | 456.69285780084897 |
| 17.75 | 55.5 | 1020.15 | 81.26 | 459.43 | 457.13828420727975 |
| 17.77 | 52.9 | 1020.11 | 81.51 | 457.98 | 457.7082041695146 |
| 17.79 | 40.12 | 1012.74 | 79.03 | 459.13 | 460.61769921749 |
| 17.82 | 49.15 | 1020.73 | 70.25 | 457.35 | 460.2397779497155 |
| 17.83 | 66.86 | 1011.65 | 77.31 | 456.56 | 454.03599822567077 |
| 17.84 | 61.27 | 1020.1 | 70.68 | 454.57 | 457.06546864918414 |
| 17.85 | 48.14 | 1017.16 | 86.68 | 451.9 | 457.7462919446442 |
| 17.86 | 50.88 | 1015.59 | 88.28 | 457.33 | 456.68265807481106 |
| 17.89 | 42.42 | 1008.92 | 65.08 | 467.59 | 461.5754309315541 |
| 17.89 | 44.6 | 1014.48 | 42.51 | 463.99 | 464.77705443177615 |
| 17.9 | 43.72 | 1008.64 | 74.73 | 452.55 | 459.8014970847607 |
| 17.9 | 58.33 | 1013.6 | 85.81 | 452.28 | 454.94641693932414 |
| 17.94 | 62.1 | 1019.81 | 82.65 | 453.55 | 454.8958623057626 |
| 17.97 | 65.94 | 1012.92 | 88.22 | 448.88 | 452.5071708494883 |
| 17.98 | 56.85 | 1012.28 | 84.52 | 448.71 | 455.24182428555105 |
| 18.0 | 44.06 | 1016.8 | 78.88 | 454.59 | 459.58273190303845 |
| 18.01 | 62.26 | 1011.89 | 89.29 | 451.14 | 453.10756062981807 |
| 18.02 | 53.16 | 1013.41 | 82.84 | 458.01 | 456.42171751548034 |
| 18.03 | 53.16 | 1013.02 | 81.95 | 456.55 | 456.5005129659639 |
| 18.03 | 53.16 | 1013.06 | 82.03 | 458.04 | 456.4920988999565 |
| 18.04 | 44.85 | 1015.13 | 48.4 | 463.31 | 463.61908166044077 |
| 18.06 | 65.48 | 1018.79 | 77.95 | 454.34 | 454.4243094498896 |
| 18.11 | 58.95 | 1016.61 | 89.17 | 454.29 | 454.14166454199494 |
| 18.13 | 60.1 | 1009.67 | 84.75 | 455.82 | 453.8961915229473 |
| 18.14 | 49.78 | 1002.95 | 100.09 | 451.44 | 453.6649941349345 |
| 18.14 | 67.71 | 1003.82 | 95.69 | 442.45 | 449.9074433355401 |
| 18.16 | 43.72 | 1008.64 | 75.22 | 454.98 | 459.22851589459475 |
| 18.16 | 43.96 | 1012.78 | 78.33 | 465.26 | 459.05202239898534 |
| 18.17 | 52.08 | 1001.91 | 100.09 | 451.06 | 452.94903640134396 |
| 18.17 | 66.86 | 1011.29 | 78.48 | 452.77 | 453.1802042498492 |
| 18.2 | 52.72 | 1025.87 | 54.26 | 463.47 | 461.3678096135659 |
| 18.21 | 39.54 | 1009.81 | 70.92 | 461.73 | 460.89674705500823 |
| 18.21 | 45.0 | 1022.86 | 48.84 | 467.54 | 463.8188758742396 |
| 18.22 | 45.09 | 1013.62 | 75.56 | 454.74 | 459.1270333631719 |
| 18.22 | 58.2 | 1017.09 | 81.92 | 451.04 | 455.213182837326 |
| 18.24 | 49.5 | 1014.37 | 79.36 | 464.13 | 457.4956830813669 |
| 18.25 | 60.1 | 1009.72 | 90.15 | 456.25 | 452.8810496638165 |
| 18.26 | 58.96 | 1014.04 | 69.7 | 457.43 | 456.48090497858726 |
| 18.27 | 58.2 | 1018.34 | 72.73 | 448.17 | 456.5591352486571 |
| 18.28 | 60.1 | 1009.72 | 85.79 | 452.93 | 453.45921998591575 |
| 18.31 | 62.1 | 1020.38 | 79.02 | 455.24 | 454.7581350474279 |
| 18.32 | 39.53 | 1008.15 | 64.85 | 454.44 | 461.43742015467615 |
| 18.32 | 42.28 | 1007.86 | 45.62 | 460.27 | 463.53345887643894 |
| 18.32 | 65.94 | 1012.74 | 86.77 | 450.5 | 452.02894677235804 |
| 18.34 | 44.63 | 1000.76 | 89.27 | 455.22 | 455.963340998892 |
| 18.34 | 50.59 | 1018.42 | 83.95 | 457.17 | 456.69115708185933 |
| 18.36 | 53.16 | 1013.31 | 83.18 | 458.47 | 455.70816977608035 |
| 18.36 | 56.65 | 1020.29 | 82.0 | 456.49 | 455.5784197907349 |
| 18.38 | 55.28 | 1020.22 | 68.33 | 451.29 | 457.8698842565306 |
| 18.4 | 50.16 | 1011.51 | 98.07 | 453.78 | 454.0602820488982 |
| 18.41 | 42.44 | 1012.66 | 65.93 | 463.2 | 460.7479119618785 |
| 18.42 | 58.95 | 1016.95 | 86.77 | 452.1 | 453.92151217696727 |
| 18.42 | 60.1 | 1009.77 | 86.75 | 451.93 | 453.0532072948905 |
| 18.42 | 63.94 | 1020.47 | 74.47 | 450.55 | 454.758298968509 |
| 18.48 | 46.48 | 1007.53 | 82.57 | 461.49 | 456.7605922899199 |
| 18.48 | 58.59 | 1015.61 | 85.14 | 452.52 | 454.0242328275001 |
| 18.5 | 52.08 | 1006.23 | 100.09 | 451.23 | 452.6641989591518 |
| 18.51 | 48.06 | 1015.14 | 79.83 | 455.44 | 457.32803096446935 |
| 18.53 | 63.91 | 1010.26 | 97.8 | 440.64 | 450.3190565318654 |
| 18.55 | 41.85 | 1015.24 | 62.47 | 467.51 | 461.3397464375879 |
| 18.55 | 46.48 | 1007.34 | 80.67 | 452.37 | 456.8872770659861 |
| 18.56 | 42.28 | 1007.75 | 60.89 | 462.05 | 460.8339970164602 |
| 18.59 | 43.14 | 1011.92 | 52.63 | 464.48 | 462.10615685280686 |
| 18.63 | 45.87 | 1007.98 | 79.9 | 453.79 | 457.0494808979769 |
| 18.65 | 52.08 | 1005.48 | 99.94 | 449.55 | 452.3356980438659 |
| 18.66 | 56.53 | 1020.13 | 80.04 | 459.45 | 455.30258286482143 |
| 18.68 | 43.69 | 1016.68 | 48.88 | 463.02 | 462.7299869900826 |
| 18.68 | 56.65 | 1020.38 | 80.26 | 455.79 | 455.2223463598715 |
| 18.68 | 62.1 | 1019.78 | 83.67 | 453.25 | 453.31727625144936 |
| 18.73 | 40.12 | 1013.19 | 74.12 | 454.71 | 459.55748654713716 |
| 18.73 | 46.48 | 1007.19 | 79.23 | 450.74 | 456.7379403460756 |
| 18.8 | 47.83 | 1005.86 | 76.77 | 453.9 | 456.5169354267787 |
| 18.81 | 52.08 | 1004.43 | 99.6 | 450.87 | 451.9912034594031 |
| 18.83 | 48.98 | 1016.33 | 74.23 | 453.28 | 457.39523074964615 |
| 18.84 | 61.27 | 1019.64 | 71.95 | 454.47 | 454.91390716792046 |
| 18.86 | 50.78 | 1008.46 | 91.67 | 446.7 | 453.70377279073705 |
| 18.87 | 43.43 | 1009.16 | 69.5 | 454.58 | 458.80810089986693 |
| 18.87 | 52.05 | 1012.02 | 53.46 | 458.64 | 459.2317295422404 |
| 18.89 | 66.86 | 1012.38 | 71.96 | 454.02 | 452.8313052555021 |
| 18.9 | 62.96 | 1020.69 | 80.57 | 455.88 | 453.2048261109298 |
| 18.91 | 43.14 | 1013.56 | 58.34 | 460.19 | 460.78946144208953 |
| 18.95 | 46.21 | 1013.47 | 81.22 | 457.58 | 456.60185019595883 |
| 18.97 | 50.59 | 1016.01 | 74.9 | 459.68 | 456.60000256329795 |
| 18.98 | 38.52 | 1018.85 | 63.16 | 454.6 | 461.5337919917975 |
| 18.99 | 56.65 | 1020.46 | 77.16 | 457.55 | 455.08314377928764 |
| 19.02 | 50.66 | 1013.04 | 85.25 | 455.42 | 454.7344713102365 |
| 19.05 | 53.16 | 1013.22 | 82.8 | 455.76 | 454.4253732327452 |
| 19.05 | 67.32 | 1013.2 | 83.14 | 447.47 | 450.84382297953874 |
| 19.06 | 56.65 | 1020.82 | 82.14 | 455.7 | 454.2509501800389 |
| 19.08 | 44.63 | 1020.05 | 41.07 | 455.95 | 463.1377560912732 |
| 19.08 | 58.59 | 1013.42 | 68.88 | 451.05 | 455.0606464477159 |
| 19.11 | 58.95 | 1017.07 | 85.49 | 449.89 | 452.78710305999687 |
| 19.12 | 50.16 | 1011.52 | 99.71 | 451.49 | 452.43308379405045 |
| 19.13 | 42.18 | 1001.45 | 98.77 | 456.04 | 453.7206916287245 |
| 19.14 | 56.65 | 1020.84 | 82.97 | 458.06 | 453.97719041615505 |
| 19.2 | 58.71 | 1009.8 | 84.62 | 448.17 | 452.20842313451385 |
| 19.22 | 62.1 | 1019.43 | 79.19 | 451.8 | 452.90074763749135 |
| 19.23 | 41.67 | 1012.53 | 48.86 | 465.66 | 461.8378158906793 |
| 19.24 | 58.33 | 1013.65 | 85.47 | 449.26 | 452.41543202652065 |
| 19.25 | 43.43 | 1012.01 | 73.26 | 451.08 | 457.75864371455697 |
| 19.26 | 44.34 | 1019.45 | 51.32 | 467.72 | 461.3187533462041 |
| 19.31 | 43.56 | 1013.65 | 41.54 | 463.35 | 462.37131638295244 |
| 19.31 | 60.07 | 1014.86 | 69.37 | 453.47 | 454.29376940501464 |
| 19.32 | 52.84 | 1004.29 | 83.51 | 450.88 | 453.15381920341724 |
| 19.43 | 52.9 | 1018.35 | 61.12 | 456.08 | 457.3375291656138 |
| 19.44 | 59.21 | 1018.5 | 88.35 | 448.04 | 451.78495851322157 |
| 19.47 | 58.79 | 1016.8 | 87.26 | 450.17 | 451.8524216632198 |
| 19.54 | 44.57 | 1007.19 | 78.93 | 450.54 | 455.69553323527913 |
| 19.54 | 50.66 | 1014.7 | 84.53 | 456.61 | 453.9716415466405 |
| 19.57 | 68.61 | 1011.13 | 96.4 | 448.73 | 447.41632457686086 |
| 19.6 | 48.14 | 1013.18 | 68.71 | 456.57 | 456.66826631159057 |
| 19.6 | 60.95 | 1015.4 | 84.26 | 456.06 | 451.3868160236597 |
| 19.61 | 56.65 | 1020.64 | 63.74 | 457.41 | 455.8596185860852 |
| 19.62 | 58.79 | 1017.59 | 87.39 | 446.29 | 451.60844251280884 |
| 19.62 | 68.63 | 1012.26 | 68.05 | 453.84 | 451.542577765937 |
| 19.64 | 56.65 | 1020.79 | 73.88 | 456.03 | 454.3347436794228 |
| 19.65 | 42.23 | 1013.04 | 75.9 | 461.16 | 456.98501253896984 |
| 19.65 | 57.85 | 1011.73 | 93.89 | 450.5 | 450.35966629930124 |
| 19.66 | 51.43 | 1010.17 | 84.19 | 459.12 | 453.2290277149437 |
| 19.67 | 60.77 | 1017.33 | 88.13 | 444.87 | 450.8892362675085 |
| 19.68 | 44.34 | 1019.49 | 49.5 | 468.27 | 460.77739524450277 |
| 19.68 | 51.19 | 1008.64 | 94.88 | 452.04 | 451.5662781980391 |
| 19.68 | 56.65 | 1020.75 | 67.25 | 456.89 | 455.22151625901614 |
| 19.69 | 39.72 | 1001.49 | 60.34 | 456.55 | 458.86327155628703 |
| 19.69 | 56.65 | 1020.84 | 72.14 | 455.07 | 454.4962025118092 |
| 19.7 | 51.3 | 1014.88 | 88.1 | 454.94 | 452.9973261711798 |
| 19.7 | 52.84 | 1004.86 | 89.72 | 444.64 | 451.56134671760844 |
| 19.72 | 44.78 | 1009.27 | 39.18 | 464.54 | 461.26403285215525 |
| 19.73 | 49.69 | 1007.78 | 73.02 | 454.28 | 454.96273138933435 |
| 19.73 | 68.63 | 1012.41 | 74.12 | 450.26 | 450.45712572408434 |
| 19.76 | 62.1 | 1020.07 | 73.55 | 450.44 | 452.7340333117575 |
| 19.78 | 50.32 | 1008.62 | 96.4 | 449.23 | 451.3669335966618 |
| 19.79 | 60.1 | 1010.47 | 84.04 | 452.41 | 450.86300710254875 |
| 19.8 | 57.25 | 1010.84 | 88.9 | 451.75 | 450.87541624186673 |
| 19.8 | 58.79 | 1017.04 | 87.71 | 449.66 | 451.1697942873412 |
| 19.8 | 67.71 | 1005.58 | 69.65 | 446.03 | 450.6475445784032 |
| 19.82 | 46.63 | 1013.17 | 87.1 | 456.36 | 453.93684539976664 |
| 19.83 | 46.33 | 1013.27 | 96.4 | 451.22 | 452.6438110022995 |
| 19.83 | 59.21 | 1012.67 | 96.42 | 440.03 | 449.38085095551605 |
| 19.86 | 41.67 | 1012.31 | 53.9 | 462.86 | 459.86949886435633 |
| 19.87 | 48.14 | 1016.94 | 81.56 | 451.14 | 454.5790164502554 |
| 19.87 | 49.69 | 1012.23 | 68.57 | 456.03 | 455.7041227162542 |
| 19.89 | 50.78 | 1008.85 | 92.97 | 446.35 | 451.5591661954991 |
| 19.89 | 51.43 | 1007.38 | 91.79 | 448.85 | 451.4495789708702 |
| 19.89 | 68.08 | 1012.65 | 80.25 | 448.71 | 449.41092940189 |
| 19.92 | 46.97 | 1014.32 | 69.17 | 459.39 | 456.368436088565 |
| 19.93 | 56.65 | 1020.7 | 62.82 | 456.53 | 455.3814815227917 |
| 19.94 | 44.63 | 1004.73 | 78.48 | 455.58 | 454.77441817350825 |
| 19.94 | 44.9 | 1008.52 | 74.69 | 459.47 | 455.56852272126105 |
| 19.94 | 56.53 | 1020.48 | 76.43 | 453.8 | 453.3887778929569 |
| 19.95 | 58.46 | 1017.45 | 89.46 | 447.1 | 450.7408294300028 |
| 19.97 | 50.78 | 1008.75 | 92.7 | 446.57 | 451.436105216529 |
| 19.99 | 40.79 | 1003.15 | 87.55 | 455.28 | 454.1835978057384 |
| 20.01 | 45.09 | 1014.21 | 38.19 | 453.96 | 461.1739549532308 |
| 20.01 | 68.63 | 1012.34 | 69.49 | 454.5 | 450.58677338372456 |
| 20.03 | 60.77 | 1017.23 | 87.82 | 449.31 | 450.2319334353776 |
| 20.04 | 49.39 | 1020.62 | 78.84 | 449.63 | 454.6358406173096 |
| 20.04 | 58.2 | 1017.56 | 74.31 | 448.92 | 452.85108866192866 |
| 20.08 | 54.42 | 1011.79 | 89.35 | 457.14 | 451.05259673451775 |
| 20.08 | 62.52 | 1017.99 | 75.74 | 450.98 | 451.5232844413466 |
| 20.1 | 57.17 | 1011.96 | 87.68 | 452.67 | 450.58585768893414 |
| 20.11 | 51.19 | 1007.82 | 92.06 | 449.03 | 451.0815006280822 |
| 20.12 | 58.12 | 1015.47 | 79.38 | 453.33 | 451.80697350389795 |
| 20.16 | 57.76 | 1019.34 | 72.1 | 455.13 | 453.19662660647845 |
| 20.19 | 44.57 | 1009.2 | 72.13 | 454.36 | 455.59739505823774 |
| 20.19 | 66.86 | 1012.97 | 64.7 | 454.84 | 451.430922332517 |
| 20.21 | 54.9 | 1016.82 | 66.56 | 454.23 | 454.4162557726164 |
| 20.21 | 69.94 | 1009.33 | 83.96 | 447.06 | 447.51848167260005 |
| 20.23 | 52.05 | 1012.15 | 47.49 | 457.57 | 457.4899833309971 |
| 20.24 | 56.65 | 1020.72 | 62.9 | 455.49 | 454.7734968589603 |
| 20.25 | 44.78 | 1007.93 | 40.16 | 462.44 | 459.9896954813451 |
| 20.28 | 48.78 | 1017.4 | 82.51 | 451.59 | 453.5274885789799 |
| 20.28 | 62.52 | 1017.89 | 75.67 | 452.45 | 451.13958592197287 |
| 20.3 | 58.46 | 1015.93 | 82.13 | 448.79 | 451.01129177115814 |
| 20.33 | 57.76 | 1016.47 | 75.35 | 450.25 | 452.16097295111786 |
| 20.37 | 52.05 | 1012.34 | 62.57 | 456.11 | 455.0355456385039 |
| 20.43 | 63.09 | 1016.46 | 91.78 | 445.58 | 448.2416124360999 |
| 20.45 | 59.8 | 1015.13 | 79.21 | 452.96 | 450.748723139911 |
| 20.5 | 49.69 | 1009.6 | 70.81 | 452.94 | 453.9480760674323 |
| 20.51 | 39.72 | 1002.25 | 47.97 | 452.39 | 459.1480198621134 |
| 20.51 | 68.08 | 1012.73 | 78.05 | 445.96 | 448.54249260358677 |
| 20.56 | 60.08 | 1017.79 | 78.08 | 452.8 | 450.84813038147 |
| 20.56 | 64.45 | 1012.24 | 53.09 | 458.97 | 452.9523382793844 |
| 20.58 | 39.53 | 1005.68 | 62.09 | 460.1 | 457.2797775931854 |
| 20.59 | 59.8 | 1015.27 | 77.94 | 453.83 | 450.67534900317014 |
| 20.6 | 59.15 | 1013.32 | 91.07 | 443.76 | 448.7439698617689 |
| 20.61 | 60.1 | 1010.84 | 80.57 | 450.46 | 449.81767655065505 |
| 20.61 | 62.91 | 1013.24 | 79.54 | 446.53 | 449.46273191454395 |
| 20.61 | 63.86 | 1015.43 | 73.86 | 446.34 | 450.23276134284185 |
| 20.61 | 65.61 | 1014.91 | 83.82 | 449.72 | 448.3011628860887 |
| 20.64 | 61.86 | 1012.81 | 99.97 | 447.14 | 446.65132022669536 |
| 20.65 | 41.67 | 1012.76 | 45.27 | 455.5 | 459.6412858596406 |
| 20.65 | 57.5 | 1016.04 | 87.45 | 448.22 | 449.8084139802319 |
| 20.68 | 63.86 | 1015.73 | 74.36 | 447.84 | 450.0492245621943 |
| 20.69 | 50.78 | 1008.71 | 91.95 | 447.58 | 450.1534891767275 |
| 20.71 | 58.18 | 1007.63 | 98.44 | 447.06 | 447.2352893702614 |
| 20.72 | 63.94 | 1017.17 | 59.83 | 447.69 | 452.1889854808296 |
| 20.76 | 44.58 | 1017.09 | 57.47 | 462.01 | 457.2763644769643 |
| 20.76 | 59.04 | 1012.51 | 85.39 | 448.92 | 449.22543586447597 |
| 20.76 | 69.05 | 1001.89 | 77.86 | 442.82 | 446.9636998742384 |
| 20.77 | 43.77 | 1010.76 | 63.12 | 453.46 | 456.1194901357003 |
| 20.78 | 58.86 | 1016.02 | 77.29 | 446.2 | 450.6991034809647 |
| 20.8 | 69.45 | 1013.7 | 82.48 | 443.77 | 447.0742816761689 |
| 20.82 | 63.77 | 1014.28 | 86.37 | 448.9 | 447.93156732156075 |
| 20.83 | 44.78 | 1008.51 | 35.9 | 460.6 | 459.53962876300176 |
| 20.85 | 59.21 | 1012.9 | 75.97 | 444.44 | 450.41539217977487 |
| 20.87 | 57.19 | 1006.5 | 77.0 | 445.95 | 450.2091683575852 |
| 20.88 | 59.8 | 1015.66 | 75.34 | 453.18 | 450.5270199129538 |
| 20.9 | 67.07 | 1005.43 | 82.85 | 443.46 | 446.7475520504839 |
| 20.92 | 70.02 | 1010.23 | 95.58 | 444.64 | 444.5071996193093 |
| 20.94 | 44.78 | 1008.14 | 35.7 | 465.57 | 459.3265106832487 |
| 20.94 | 68.12 | 1012.43 | 78.2 | 446.41 | 447.6568115900025 |
| 20.95 | 44.89 | 1010.48 | 67.97 | 450.39 | 454.7627521673882 |
| 20.95 | 48.14 | 1013.3 | 67.72 | 452.38 | 454.2185133216816 |
| 20.95 | 70.72 | 1009.96 | 87.73 | 445.66 | 445.39798853968557 |
| 20.96 | 69.48 | 1011.04 | 82.63 | 444.31 | 446.5197598700026 |
| 20.98 | 60.1 | 1011.07 | 79.44 | 450.95 | 449.2875712413884 |
| 20.99 | 67.07 | 1005.17 | 82.41 | 442.02 | 446.61697690688305 |
| 21.01 | 58.96 | 1014.33 | 61.8 | 453.88 | 452.35263519535 |
| 21.06 | 50.59 | 1016.42 | 66.12 | 454.15 | 453.8829146493028 |
| 21.06 | 62.91 | 1011.92 | 75.52 | 455.22 | 449.0737291884585 |
| 21.06 | 67.07 | 1004.9 | 84.09 | 446.44 | 446.2148995417375 |
| 21.08 | 44.05 | 1008.13 | 72.52 | 449.6 | 453.8663667211949 |
| 21.11 | 58.66 | 1011.7 | 68.71 | 457.89 | 451.0124137711604 |
| 21.11 | 59.39 | 1013.87 | 85.29 | 446.02 | 448.5883814092364 |
| 21.13 | 51.43 | 1007.43 | 88.72 | 445.4 | 449.5097304398748 |
| 21.14 | 58.05 | 1012.98 | 87.27 | 449.74 | 448.5033053489824 |
| 21.14 | 58.98 | 1009.05 | 94.36 | 448.31 | 446.9172206057899 |
| 21.16 | 45.38 | 1014.65 | 73.06 | 458.63 | 453.8324720669487 |
| 21.18 | 44.57 | 1007.27 | 73.67 | 449.93 | 453.30606496401464 |
| 21.18 | 60.1 | 1011.02 | 78.19 | 452.39 | 449.08008122232087 |
| 21.19 | 74.93 | 1015.75 | 80.84 | 443.29 | 445.36190546480196 |
| 21.26 | 50.32 | 1008.41 | 87.22 | 446.22 | 449.83432112958235 |
| 21.28 | 70.32 | 1011.06 | 90.12 | 439.0 | 444.60209183702824 |
| 21.32 | 45.01 | 1012.23 | 59.94 | 452.75 | 455.3330390953461 |
| 21.32 | 49.02 | 1008.81 | 85.81 | 445.65 | 450.28095549994475 |
| 21.33 | 58.46 | 1016.17 | 79.73 | 445.27 | 449.3942290809264 |
| 21.33 | 60.1 | 1010.97 | 78.36 | 451.95 | 448.76188428428145 |
| 21.33 | 63.86 | 1020.33 | 72.13 | 445.02 | 449.49526226001734 |
| 21.34 | 59.8 | 1016.92 | 77.06 | 450.74 | 449.4914112072868 |
| 21.36 | 58.95 | 1018.35 | 78.87 | 443.93 | 449.5171242611325 |
| 21.36 | 68.28 | 1007.6 | 72.37 | 445.91 | 447.2640843894497 |
| 21.38 | 44.05 | 1005.69 | 81.66 | 445.71 | 451.75573664689705 |
| 21.39 | 51.3 | 1013.39 | 89.05 | 452.7 | 449.47769197848703 |
| 21.39 | 63.9 | 1013.44 | 70.95 | 449.38 | 448.9807967933042 |
| 21.4 | 44.57 | 1005.7 | 73.09 | 445.09 | 452.83851864090616 |
| 21.4 | 68.28 | 1008.2 | 60.34 | 450.22 | 448.99070865761826 |
| 21.41 | 56.9 | 1007.03 | 79.41 | 441.41 | 448.9314689751323 |
| 21.42 | 43.79 | 1015.76 | 43.08 | 462.19 | 458.19122302999057 |
| 21.44 | 51.19 | 1009.1 | 84.94 | 446.17 | 449.6590005617065 |
| 21.44 | 63.09 | 1016.56 | 90.11 | 444.19 | 446.545237358833 |
| 21.45 | 45.09 | 1013.83 | 52.26 | 453.15 | 456.3129527756159 |
| 21.45 | 60.08 | 1017.92 | 72.7 | 451.49 | 449.9268730104538 |
| 21.45 | 66.05 | 1014.81 | 73.73 | 453.38 | 448.0350183689646 |
| 21.46 | 46.63 | 1012.97 | 71.29 | 452.1 | 453.06361431502756 |
| 21.47 | 58.79 | 1017.0 | 76.97 | 446.33 | 449.51211271813366 |
| 21.49 | 56.9 | 1007.47 | 66.66 | 452.46 | 450.672947544889 |
| 21.52 | 66.51 | 1015.32 | 72.85 | 444.87 | 447.9552056621943 |
| 21.53 | 52.84 | 1005.06 | 88.22 | 444.04 | 448.2666586698033 |
| 21.54 | 58.49 | 1010.85 | 78.9 | 449.12 | 448.66968221055265 |
| 21.54 | 69.48 | 1011.04 | 80.48 | 443.15 | 445.7146703190735 |
| 21.55 | 44.57 | 1006.03 | 71.54 | 446.84 | 452.8021698612517 |
| 21.55 | 60.27 | 1017.42 | 92.59 | 443.93 | 446.7443660008083 |
| 21.57 | 66.49 | 1014.76 | 68.19 | 455.18 | 448.49796091182566 |
| 21.58 | 63.87 | 1015.27 | 63.15 | 451.88 | 449.90863388073797 |
| 21.61 | 41.54 | 1014.5 | 75.62 | 458.47 | 453.53620348431275 |
| 21.61 | 49.39 | 1019.41 | 78.2 | 449.26 | 451.60241101975805 |
| 21.62 | 50.05 | 1007.2 | 92.9 | 444.16 | 448.28015134415546 |
| 21.65 | 58.18 | 1008.33 | 95.28 | 441.05 | 445.940139097113 |
| 21.69 | 44.57 | 1005.84 | 71.53 | 447.88 | 452.5181226448485 |
| 21.71 | 65.27 | 1013.24 | 63.58 | 444.91 | 449.080854354919 |
| 21.73 | 69.05 | 1001.31 | 86.64 | 439.01 | 443.764677908941 |
| 21.75 | 59.8 | 1016.65 | 72.94 | 453.17 | 449.2796285666682 |
Now that we have real predictions we can use an evaluation metric such as Root Mean Squared Error to validate our regression model. The lower the Root Mean Squared Error, the better our model.
//Now let's compute some evaluation metrics against our test dataset
import org.apache.spark.mllib.evaluation.RegressionMetrics
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").rdd.map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])))
import org.apache.spark.mllib.evaluation.RegressionMetrics
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@2cb97126
val rmse = metrics.rootMeanSquaredError
rmse: Double = 4.426730543741181
val explainedVariance = metrics.explainedVariance
explainedVariance: Double = 269.3177257387032
val r2 = metrics.r2
r2: Double = 0.9314313152952873
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")
println (f"R2: $r2")
Root Mean Squared Error: 4.426730543741181
Explained Variance: 269.3177257387032
R2: 0.9314313152952873
Generally a good model will have 68% of predictions within 1 RMSE and 95% within 2 RMSE of the actual value. Let's calculate and see if our RMSE meets this criteria.
display(predictionsAndLabels) // recall the DataFrame predictionsAndLabels
// First we calculate the residual error and divide it by the RMSE from predictionsAndLabels DataFrame and make another DataFrame that is registered as a temporary table Power_Plant_RMSE_Evaluation
predictionsAndLabels.selectExpr("PE", "Predicted_PE", "PE - Predicted_PE AS Residual_Error", s""" (PE - Predicted_PE) / $rmse AS Within_RSME""").createOrReplaceTempView("Power_Plant_RMSE_Evaluation")
SELECT * from Power_Plant_RMSE_Evaluation
| PE | Predicted_PE | Residual_Error | Within_RSME |
|---|---|---|---|
| 490.34 | 493.23742177145215 | -2.897421771452173 | -0.6545286058914855 |
| 482.66 | 488.93663844863084 | -6.27663844863082 | -1.4178948518800556 |
| 489.04 | 488.2642491377776 | 0.7757508622224236 | 0.17524239493619823 |
| 489.64 | 487.68500173970443 | 1.9549982602955538 | 0.44163480044197995 |
| 489.0 | 487.8432005878593 | 1.1567994121406855 | 0.26132139752130357 |
| 488.03 | 486.7875685475632 | 1.24243145243679 | 0.28066570579802425 |
| 491.9 | 485.8682911927764 | 6.031708807223595 | 1.3625651590092023 |
| 489.36 | 485.34119715268844 | 4.018802847311576 | 0.9078489886839033 |
| 481.47 | 482.9938172155284 | -1.5238172155283678 | -0.3442308494884213 |
| 482.05 | 482.5648390621137 | -0.5148390621137082 | -0.11630232674577932 |
| 494.75 | 487.00024243767876 | 7.749757562321236 | 1.7506729821805804 |
| 482.98 | 483.3467525779819 | -0.3667525779818561 | -8.284953745386567e-2 |
| 475.34 | 482.8336530053327 | -7.493653005332703 | -1.692818871916148 |
| 483.73 | 483.6145343498689 | 0.11546565013111376 | 2.608373132048146e-2 |
| 488.42 | 484.53187599470635 | 3.888124005293662 | 0.8783285919200484 |
| 495.35 | 487.2657538698361 | 8.084246130163933 | 1.8262340682999108 |
| 491.32 | 487.10787889447494 | 4.212121105525057 | 0.9515196517846441 |
| 486.46 | 482.0547750131424 | 4.405224986857604 | 0.9951418870719423 |
| 483.12 | 483.688095258955 | -0.5680952589549975 | -0.12833292050229034 |
| 495.23 | 487.0194077282659 | 8.210592271734129 | 1.8547757064958097 |
| 479.91 | 480.1986449575354 | -0.2886449575353822 | -6.520499829010114e-2 |
| 492.12 | 484.35541367676683 | 7.764586323233175 | 1.7540228045303743 |
| 491.38 | 483.7973981417879 | 7.582601858212115 | 1.712912449331917 |
| 481.56 | 484.07023605498495 | -2.5102360549849436 | -0.567063215206105 |
| 491.84 | 484.05572584065357 | 7.78427415934641 | 1.758470293691663 |
| 484.64 | 483.0334383183464 | 1.6065616816536021 | 0.36292285373571487 |
| 490.07 | 483.9952002249004 | 6.0747997750996205 | 1.3722994239368362 |
| 478.02 | 480.02034741363497 | -2.0003474136349837 | -0.4518791902667791 |
| 476.61 | 479.7602256710553 | -3.1502256710552956 | -0.711637096481805 |
| 487.09 | 483.7798894431585 | 3.3101105568414937 | 0.7477551488923485 |
| 482.82 | 483.2217349280458 | -0.401734928045812 | -9.075206274161249e-2 |
| 481.6 | 480.1171178824119 | 1.482882117588133 | 0.33498359634397323 |
| 470.02 | 482.0478125504672 | -12.027812550467218 | -2.717087121436152 |
| 489.05 | 481.81327159305386 | 7.236728406946156 | 1.6347795140090344 |
| 487.03 | 484.6333949755666 | 2.39660502443337 | 0.5413939250993844 |
| 490.57 | 482.2826790358646 | 8.287320964135404 | 1.8721087453250556 |
| 488.57 | 482.86050781997415 | 5.709492180025848 | 1.2897763086344445 |
| 480.39 | 483.17821215232124 | -2.7882121523212504 | -0.6298581141929721 |
| 481.37 | 483.0829476732433 | -1.7129476732433204 | -0.3869554869711247 |
| 489.62 | 482.71830544679335 | 6.901694553206653 | 1.5590952476122018 |
| 481.13 | 481.35862683823984 | -0.22862683823984753 | -5.164688385271067e-2 |
| 485.94 | 481.4164995749685 | 4.5235004250315 | 1.021860350507925 |
| 482.49 | 482.3037528502627 | 0.18624714973731216 | 4.2073297187840204e-2 |
| 483.77 | 483.6070229944035 | 0.1629770055964741 | 3.6816563372465104e-2 |
| 491.77 | 481.0249164343975 | 10.7450835656025 | 2.4273181887690556 |
| 483.43 | 482.2081944229683 | 1.2218055770317164 | 0.2760063132279849 |
| 481.49 | 482.6905244198958 | -1.2005244198957712 | -0.2711988922825122 |
| 478.78 | 480.9741288614041 | -2.194128861404124 | -0.49565448805243767 |
| 492.96 | 485.8565717694332 | 7.103428230566806 | 1.6046669568831393 |
| 481.36 | 483.99243959507345 | -2.6324395950734356 | -0.5946690382578993 |
| 486.68 | 481.76650494734884 | 4.913495052651172 | 1.1099602752189677 |
| 484.94 | 483.069724719673 | 1.8702752803270073 | 0.42249584921569994 |
| 483.01 | 481.33834961288795 | 1.671650387112038 | 0.3776264153858503 |
| 488.17 | 483.1883946104104 | 4.9816053895896175 | 1.1253464244922151 |
| 490.41 | 480.1679106982307 | 10.24208930176934 | 2.313691606156222 |
| 483.11 | 482.4149038683481 | 0.6950961316518942 | 0.15702246269194528 |
| 485.89 | 480.94068220101354 | 4.949317798986442 | 1.1180526463224945 |
| 487.08 | 482.508645049916 | 4.571354950083958 | 1.0326707046913566 |
| 477.8 | 480.01746628251317 | -2.2174662825131577 | -0.5009264197587914 |
| 489.96 | 480.63940670945976 | 9.320593290540216 | 2.1055253303633577 |
| 486.92 | 483.0108446487773 | 3.9091553512226938 | 0.883079580425271 |
| 486.37 | 482.57972730795177 | 3.7902726920482337 | 0.8562239455498787 |
| 488.2 | 483.51586402481416 | 4.684135975185825 | 1.0581479782654901 |
| 479.06 | 481.6634377951154 | -2.603437795115383 | -0.5881175213603873 |
| 480.05 | 484.68450470880435 | -4.634504708804343 | -1.0469362575856187 |
| 487.18 | 482.8218608903564 | 4.358139109643616 | 0.9845051707078616 |
| 480.47 | 481.8880244206695 | -1.4180244206694965 | -0.32033221960491765 |
| 480.36 | 480.6306788292839 | -0.2706788292838951 | -6.11464435454739e-2 |
| 487.85 | 479.89671820679695 | 7.953281793203075 | 1.7966491781271794 |
| 486.11 | 479.6914116057231 | 6.418588394276924 | 1.449961394951398 |
| 481.83 | 481.1970697561745 | 0.6329302438254558 | 0.1429791665816065 |
| 482.96 | 482.17217802621536 | 0.7878219737846166 | 0.17796926331974147 |
| 483.92 | 481.3074409763495 | 2.612559023650533 | 0.590178010121793 |
| 477.69 | 479.64992318380445 | -1.9599231838044489 | -0.4427473424095181 |
| 474.25 | 480.70714895561014 | -6.457148955610137 | -1.4586722394340677 |
| 479.08 | 479.7024675196716 | -0.6224675196716021 | -0.14061563348410486 |
| 478.89 | 481.00544489343537 | -2.115444893435381 | -0.4778797517789611 |
| 483.11 | 482.38901084355183 | 0.7209891564481836 | 0.16287170617772706 |
| 488.43 | 481.25646633043965 | 7.173533669560356 | 1.6205038004183012 |
| 483.28 | 480.33371483717946 | 2.946285162820516 | 0.6655668633335223 |
| 486.76 | 482.5311759738776 | 4.228824026122368 | 0.9552928474721311 |
| 476.58 | 480.67721106043905 | -4.097211060439065 | -0.9255614318409298 |
| 481.61 | 482.53257783094546 | -0.9225778309454427 | -0.20841065925050425 |
| 477.8 | 480.0330510466423 | -2.233051046642288 | -0.5044470235035043 |
| 475.32 | 479.3324132109004 | -4.012413210900434 | -0.9064055675522111 |
| 475.89 | 478.2499419685471 | -2.359941968547105 | -0.5331117277702286 |
| 485.03 | 480.3355565472517 | 4.69444345274826 | 1.0604764411029242 |
| 482.46 | 479.7315598782737 | 2.728440121726294 | 0.6163555912803304 |
| 484.57 | 477.91894784424176 | 6.6510521557582365 | 1.502475041125319 |
| 483.94 | 483.45191519870116 | 0.48808480129883947 | 0.11025852973791857 |
| 482.39 | 482.34452319301437 | 4.547680698561862e-2 | 1.027322682875218e-2 |
| 483.8 | 478.45760011663003 | 5.342399883369978 | 1.2068500286116204 |
| 482.22 | 478.7638216096892 | 3.4561783903108108 | 0.7807519242835766 |
| 481.52 | 479.1388800137473 | 2.3811199862526564 | 0.5378958494817917 |
| 483.79 | 477.1846287648614 | 6.605371235138648 | 1.4921557049542535 |
| 480.54 | 476.81117998099177 | 3.7288200190082534 | 0.8423417649127792 |
| 477.27 | 480.84424359067077 | -3.5742435906707897 | -0.8074228949228237 |
| 474.5 | 480.1364995691749 | -5.636499569174873 | -1.273287251952606 |
| 475.52 | 479.2235127059344 | -3.703512705934429 | -0.8366248339128552 |
| 481.44 | 478.91505388939413 | 2.5249461106058675 | 0.5703862219885535 |
| 480.6 | 476.210862698602 | 4.389137301398023 | 0.9915076732202934 |
| 487.19 | 481.0254321330136 | 6.164567866986374 | 1.392578067734949 |
| 475.42 | 480.36098930984286 | -4.940989309842848 | -1.1161712376708273 |
| 476.22 | 478.51210495606927 | -2.292104956069238 | -0.5177873225895746 |
| 485.13 | 479.57731721621883 | 5.552682783781165 | 1.2543530104022107 |
| 488.02 | 484.33140555054337 | 3.688594449456616 | 0.8332547944829863 |
| 477.78 | 478.4450809561189 | -0.6650809561189135 | -0.15024202389261102 |
| 477.2 | 480.5031526805204 | -3.303152680520384 | -0.7461833621634392 |
| 467.56 | 479.2169410835439 | -11.65694108354387 | -2.6333071255094267 |
| 485.18 | 477.6146348725092 | 7.565365127490793 | 1.7090186657480726 |
| 483.52 | 478.33312476559934 | 5.186875234400645 | 1.17171695524459 |
| 480.21 | 479.4778900760471 | 0.7321099239528621 | 0.16538389150158914 |
| 478.81 | 481.48536176155926 | -2.675361761559259 | -0.6043651708916123 |
| 484.67 | 477.9675154808001 | 6.702484519199913 | 1.5140936302699408 |
| 477.9 | 479.6817134321857 | -1.781713432185711 | -0.40248969630754267 |
| 475.17 | 477.5050067316079 | -2.335006731607905 | -0.5274788489010923 |
| 485.73 | 478.32045063849523 | 7.409549361504787 | 1.673819828943716 |
| 479.91 | 478.1399555772117 | 1.7700444227883168 | 0.3998536629456538 |
| 486.4 | 479.65037308403333 | 6.749626915966644 | 1.5247431144210337 |
| 480.15 | 477.1324329554068 | 3.017567044593193 | 0.681669465709775 |
| 480.58 | 482.82343628916425 | -2.243436289164265 | -0.5067930534728822 |
| 484.07 | 478.16947013024355 | 5.90052986975644 | 1.3329317905059794 |
| 481.89 | 480.6437911412516 | 1.246208858748389 | 0.2815190232236669 |
| 483.14 | 478.8928744938167 | 4.247125506183295 | 0.9594271583094607 |
| 479.31 | 476.1246111330079 | 3.1853888669921275 | 0.7195804749163808 |
| 484.21 | 477.288235770853 | 6.921764229146959 | 1.563628994525865 |
| 476.02 | 478.30600580479216 | -2.2860058047921825 | -0.5164095221527084 |
| 480.69 | 478.2957350932866 | 2.3942649067133743 | 0.5408652916763936 |
| 481.91 | 476.52270993699136 | 5.387290063008663 | 1.2169907361146226 |
| 485.06 | 478.99917896902446 | 6.060821030975546 | 1.3691416206809235 |
| 485.29 | 480.8674382556021 | 4.42256174439791 | 0.9990582667496749 |
| 482.39 | 481.1210453702997 | 1.2689546297002607 | 0.2866573009496584 |
| 478.25 | 478.8354389662744 | -0.5854389662744097 | -0.1322508701375881 |
| 475.79 | 476.29244393984663 | -0.5024439398466143 | -0.11350226422907182 |
| 485.87 | 479.0178844308566 | 6.852115569143393 | 1.5478953375265612 |
| 479.23 | 476.4582419334783 | 2.771758066521727 | 0.6261411303745675 |
| 478.61 | 480.1903466285615 | -1.5803466285614718 | -0.3570008639436786 |
| 479.94 | 479.4487267515188 | 0.4912732484812068 | 0.11097880108736301 |
| 479.25 | 479.3384367489267 | -8.84367489267106e-2 | -1.997789295120947e-2 |
| 480.99 | 477.44189376811596 | 3.5481062318840486 | 0.8015184563019332 |
| 481.07 | 476.33561360755584 | 4.734386392444151 | 1.0694995653480546 |
| 480.4 | 482.3769169335795 | -1.9769169335795027 | -0.44658623651141477 |
| 482.8 | 476.04420182940044 | 6.755798170599576 | 1.5261372030315674 |
| 480.08 | 479.65335282852305 | 0.42664717147692954 | 9.637974736911713e-2 |
| 475.0 | 478.6696951676176 | -3.66969516761759 | -0.8289854400119429 |
| 483.88 | 476.696926422392 | 7.183073577608013 | 1.6226588690300885 |
| 482.52 | 476.90393713153463 | 5.616062868465349 | 1.2686705940133918 |
| 478.45 | 479.2188844607746 | -0.7688844607745864 | -0.17369127241360752 |
| 482.3 | 475.81261283946816 | 6.487387160531853 | 1.465503060651426 |
| 472.45 | 476.2100211409317 | -3.7600211409317126 | -0.8493901094223798 |
| 480.2 | 480.2141595165933 | -1.415951659333814e-2 | -3.19863982083975e-3 |
| 478.38 | 474.9481764100585 | 3.431823589941473 | 0.775250166241454 |
| 472.77 | 479.30598211413803 | -6.535982114138051 | -1.476480677907779 |
| 483.53 | 476.3744577455605 | 7.155542254439467 | 1.6164395333609067 |
| 475.47 | 478.6980048877349 | -3.228004887734869 | -0.7292074491181413 |
| 467.24 | 476.41215657483474 | -9.17215657483473 | -2.0719934236347326 |
| 481.03 | 480.7338755379764 | 0.29612446202355613 | 6.689462100697262e-2 |
| 482.37 | 480.51130475015583 | 1.8586952498441747 | 0.41987991622217147 |
| 484.97 | 478.0206284049 | 6.949371595100047 | 1.5698655082870474 |
| 479.53 | 475.4980717304681 | 4.031928269531875 | 0.9108140262190783 |
| 482.8 | 476.4769333498689 | 6.323066650131125 | 1.4283829990671366 |
| 477.26 | 480.5658974037096 | -3.3058974037095936 | -0.7468033961054397 |
| 479.02 | 478.9378167534134 | 8.218324658656684e-2 | 1.8565224554443056e-2 |
| 476.69 | 479.483969889582 | -2.7939698895820015 | -0.631158789082455 |
| 476.62 | 476.72728884411293 | -0.10728884411292938 | -2.423658794064658e-2 |
| 475.69 | 475.1283411969007 | 0.5616588030993057 | 0.1268789228414677 |
| 482.95 | 477.43108128919135 | 5.518918710808634 | 1.2467256943415417 |
| 483.24 | 479.0201634085648 | 4.2198365914351825 | 0.9532625827884375 |
| 484.86 | 476.63324412261045 | 8.226755877389564 | 1.858427070746631 |
| 491.97 | 478.3337308000573 | 13.636269199942717 | 3.080438049074982 |
| 474.88 | 478.39283560851754 | -3.5128356085175483 | -0.7935508099728905 |
| 475.64 | 477.10087603877 | -1.4608760387700386 | -0.33001241533337206 |
| 484.96 | 478.16396362897893 | 6.796036371021046 | 1.5352270267793358 |
| 475.42 | 478.96772021173297 | -3.547720211732951 | -0.8014312542128782 |
| 480.38 | 475.6006326388746 | 4.7793673611254235 | 1.0796607821279804 |
| 478.82 | 474.9766634864767 | 3.8433365135232975 | 0.8682110816429235 |
| 482.55 | 478.786077505979 | 3.763922494021017 | 0.8502714264690703 |
| 480.14 | 478.9365615888623 | 1.2034384111377108 | 0.271857163937664 |
| 482.55 | 476.1723094438278 | 6.377690556172183 | 1.4407225588170043 |
| 477.91 | 478.2490255806092 | -0.33902558060918864 | -7.658599891256687e-2 |
| 476.25 | 474.4577268339357 | 1.7922731660643194 | 0.4048751439362759 |
| 471.13 | 478.43777544278043 | -7.307775442780439 | -1.6508290646045036 |
| 482.16 | 478.00197412694763 | 4.158025873052395 | 0.9392995195813987 |
| 480.87 | 479.3152324207446 | 1.5547675792553832 | 0.3512225476325008 |
| 477.34 | 477.2801935898294 | 5.980641017055177e-2 | 1.3510289270963245e-2 |
| 483.66 | 478.285031656868 | 5.374968343132025 | 1.214207255223955 |
| 481.95 | 475.9420528274367 | 6.007947172563263 | 1.357197397311141 |
| 479.68 | 476.17198214059135 | 3.5080178594086533 | 0.7924624787403274 |
| 478.66 | 474.34503134979343 | 4.314968650206595 | 0.9747529486084029 |
| 478.8 | 479.7691576930105 | -0.9691576930105157 | -0.2189330666129607 |
| 481.12 | 475.82678857769963 | 5.293211422300374 | 1.1957383378087658 |
| 476.54 | 478.0366119605891 | -1.496611960589064 | -0.33808517274788225 |
| 478.03 | 477.46151999839185 | 0.5684800016081226 | 0.12841983400410018 |
| 479.23 | 479.110912113517 | 0.1190878864830438 | 2.6901995797195863e-2 |
| 480.74 | 477.72481326338215 | 3.0151867366178635 | 0.6811317532938489 |
| 478.88 | 479.54303529435083 | -0.6630352943508342 | -0.14977990817360218 |
| 481.05 | 479.71268358186353 | 1.3373164181364814 | 0.3021002532054435 |
| 473.54 | 473.26068816423447 | 0.2793118357655544 | 6.309664277182285e-2 |
| 469.73 | 476.05175958076205 | -6.321759580762034 | -1.4280877316330394 |
| 475.51 | 476.88388448180046 | -1.3738844818004736 | -0.31036099175789383 |
| 476.67 | 475.61433131158714 | 1.0556686884128794 | 0.23847593115995214 |
| 472.16 | 477.7130066863276 | -5.553006686327592 | -1.2544261801023373 |
| 481.85 | 475.2673812565115 | 6.582618743488524 | 1.487015909020143 |
| 479.45 | 479.3846135509556 | 6.538644904441071e-2 | 1.4770822031817277e-2 |
| 482.38 | 475.5344685772051 | 6.84553142279492 | 1.5464079765310332 |
| 469.58 | 473.85672752722735 | -4.276727527227365 | -0.9661142653632034 |
| 477.93 | 477.3826135030455 | 0.5473864969545161 | 0.12365480382094843 |
| 478.21 | 477.2300569616709 | 0.9799430383290542 | 0.2213694799459989 |
| 477.62 | 479.265495182268 | -1.6454951822680073 | -0.371717945334288 |
| 472.46 | 472.7773808573311 | -0.3173808573311021 | -7.16964482466269e-2 |
| 467.37 | 473.2887424901299 | -5.918742490129887 | -1.3370460279083884 |
| 473.2 | 475.32128019247375 | -2.121280192473762 | -0.4791979479015218 |
| 480.05 | 476.83702080523557 | 3.2129791947644435 | 0.7258131397464832 |
| 469.65 | 473.90133694043874 | -4.251336940438762 | -0.9603785228015735 |
| 485.23 | 477.4312500724956 | 7.798749927504446 | 1.761740374853143 |
| 477.88 | 474.53026960482526 | 3.3497303951747313 | 0.756705284425051 |
| 475.21 | 475.5632280329792 | -0.35322803297924565 | -7.97943379405968e-2 |
| 475.91 | 476.45899184845075 | -0.5489918484507257 | -0.12401745329336308 |
| 477.85 | 475.16451288467897 | 2.6854871153210524 | 0.6066524918978817 |
| 464.86 | 472.95056743270436 | -8.090567432704347 | -1.827662052785967 |
| 466.05 | 473.09691475779204 | -7.046914757792024 | -1.591900543337891 |
| 463.16 | 472.93274352761154 | -9.77274352761151 | -2.2076662292962226 |
| 475.75 | 478.4561215361668 | -2.7061215361667905 | -0.6113138148859982 |
| 471.6 | 474.6981382928799 | -3.0981382928798666 | -0.6998705392764937 |
| 470.82 | 475.02803269176394 | -4.208032691763947 | -0.950596077665842 |
| 479.63 | 475.85397168574394 | 3.7760283142560525 | 0.8530061355541199 |
| 477.68 | 479.18053767427625 | -1.500537674276245 | -0.3389719928622738 |
| 478.73 | 473.5654621009553 | 5.164537899044717 | 1.166670943264595 |
| 473.66 | 477.1725989266351 | -3.5125989266350643 | -0.793497343451686 |
| 474.28 | 475.0636358283201 | -0.7836358283201434 | -0.17702361157448407 |
| 468.19 | 473.5875494551498 | -5.397549455149829 | -1.2193083364383357 |
| 463.57 | 472.67682233352394 | -9.10682233352395 | -2.0572343953484604 |
| 475.46 | 476.9235641778536 | -1.463564177853641 | -0.3306196669058454 |
| 473.05 | 471.68772310073444 | 1.3622768992655665 | 0.30773883474602903 |
| 474.92 | 475.9474342905188 | -1.0274342905187837 | -0.23209777066088685 |
| 481.31 | 474.932278891215 | 6.3777211087850105 | 1.4407294606630792 |
| 473.43 | 474.55112952359036 | -1.121129523590355 | -0.25326355704561365 |
| 477.27 | 477.46636654211125 | -0.1963665421112637 | -4.4359271514481574e-2 |
| 476.91 | 473.8650937482109 | 3.0449062517891434 | 0.6878454023126037 |
| 468.27 | 473.17098851617544 | -4.90098851617546 | -1.1071350441930146 |
| 480.11 | 474.8258713039414 | 5.284128696058588 | 1.1936865467290878 |
| 471.79 | 473.1211730372522 | -1.3311730372521993 | -0.3007124612846165 |
| 480.87 | 474.62974157133897 | 6.240258428661036 | 1.4096765924648265 |
| 477.53 | 474.801072598715 | 2.7289274012849773 | 0.6164656679054759 |
| 476.03 | 471.6665106288944 | 4.3634893711055724 | 0.9857137966698644 |
| 479.28 | 474.5250337253637 | 4.754966274636274 | 1.0741485680349747 |
| 470.76 | 473.3969220129792 | -2.6369220129791984 | -0.5956816180527323 |
| 477.65 | 475.74847822607404 | 1.9015217739259356 | 0.42955444320288233 |
| 474.16 | 472.3991408528041 | 1.7608591471959016 | 0.3977787059312943 |
| 476.31 | 477.2616855096003 | -0.9516855096002814 | -0.21498609418317555 |
| 479.17 | 474.9177051337058 | 4.252294866294221 | 0.9605949185920998 |
| 473.16 | 477.4228902388337 | -4.262890238833677 | -0.9629884170069595 |
| 478.03 | 473.50046202531144 | 4.529537974688537 | 1.0232242351169787 |
| 470.66 | 473.24796901874475 | -2.5879690187447295 | -0.5846231192914554 |
| 479.14 | 473.09058493481064 | 6.049415065189351 | 1.3665650089641064 |
| 480.04 | 472.3076103285209 | 7.732389671479098 | 1.746749569478921 |
| 472.54 | 471.615832151066 | 0.9241678489340188 | 0.20876984487810568 |
| 479.24 | 473.69713759778955 | 5.54286240221046 | 1.2521345827220822 |
| 474.42 | 472.19156900447234 | 2.228430995527674 | 0.5034033523179731 |
| 477.41 | 476.3350912306915 | 1.0749087693085357 | 0.2428222722587704 |
| 472.16 | 475.77435376625584 | -3.6143537662558174 | -0.8164837978146291 |
| 476.55 | 472.6471168581127 | 3.902883141887287 | 0.8816626861116392 |
| 474.24 | 472.62895527440253 | 1.6110447255974805 | 0.3639355749527803 |
| 474.91 | 472.5658087964315 | 2.3441912035685277 | 0.529553624374745 |
| 474.94 | 477.2343522450378 | -2.294352245037828 | -0.5182949859646964 |
| 478.47 | 473.38659302581107 | 5.083406974188961 | 1.1483434385624023 |
| 481.3 | 473.3858358704527 | 7.914164129547316 | 1.7878124840322416 |
| 477.19 | 472.2522916899094 | 4.937708310090613 | 1.1154300586630213 |
| 479.61 | 471.9871102146372 | 7.6228897853628155 | 1.7220135063654567 |
| 474.03 | 476.66325912606675 | -2.6332591260667755 | -0.5948541705999838 |
| 484.94 | 473.0585846959978 | 11.88141530400219 | 2.6840159315323495 |
| 478.76 | 472.5409240450936 | 6.219075954906373 | 1.4048914641301884 |
| 477.23 | 472.5905086170794 | 4.639491382920596 | 1.048062749037262 |
| 477.93 | 473.2433331311532 | 4.686666868846828 | 1.0587197080412232 |
| 483.54 | 473.33367076102724 | 10.206329238972785 | 2.305613395286325 |
| 470.66 | 472.25860679152254 | -1.5986067915225135 | -0.36112584123348884 |
| 468.57 | 472.68332438968736 | -4.113324389687364 | -0.9292014386335458 |
| 475.46 | 469.26491536026253 | 6.195084639737445 | 1.3994718175237673 |
| 467.6 | 476.77045249286635 | -9.170452492866332 | -2.07160847091363 |
| 479.16 | 472.2986932100967 | 6.861306789903324 | 1.5499716375563712 |
| 473.72 | 475.9280130742943 | -2.2080130742942856 | -0.49879093666907914 |
| 470.9 | 474.1703211862971 | -3.27032118629711 | -0.7387667159730148 |
| 479.2 | 473.8182300033406 | 5.381769996659386 | 1.215743751168344 |
| 476.32 | 474.4834318795844 | 1.8365681204156203 | 0.41488138983573053 |
| 477.34 | 473.75018039571677 | 3.589819604283207 | 0.8109415219227073 |
| 472.34 | 471.57861538146454 | 0.7613846185354305 | 0.17199705539157537 |
| 473.29 | 471.6415723647869 | 1.6484276352131246 | 0.37238038749473606 |
| 477.3 | 472.7701885173113 | 4.529811482688729 | 1.0232860206712358 |
| 472.11 | 471.77143688745184 | 0.3385631125481723 | 7.648152721354506e-2 |
| 468.09 | 474.5636411763966 | -6.4736411763965975 | -1.462397837959548 |
| 477.62 | 472.7148284274264 | 4.905171572573579 | 1.1080799981170868 |
| 468.84 | 472.4884135100371 | -3.6484135100371304 | -0.8241779060158768 |
| 477.2 | 474.2579394355058 | 2.94206056449417 | 0.6646125250731286 |
| 473.16 | 475.5315818680234 | -2.3715818680233838 | -0.5357411851906123 |
| 467.46 | 472.3352405654698 | -4.875240565469824 | -1.1013185729957693 |
| 476.24 | 471.7121476384473 | 4.527852361552732 | 1.0228434545117104 |
| 462.07 | 471.87019509945225 | -9.800195099452253 | -2.2138675491121655 |
| 478.3 | 473.39040211351204 | 4.909597886487973 | 1.1090799040003696 |
| 474.64 | 472.2988980097268 | 2.3411019902731596 | 0.528855769995572 |
| 476.18 | 473.7408434668035 | 2.439156533196524 | 0.5510063260220736 |
| 472.02 | 474.7548947976392 | -2.7348947976392424 | -0.6178137048585499 |
| 468.75 | 475.5773739728955 | -6.8273739728954865 | -1.5423062021582727 |
| 476.87 | 476.2403822241514 | 0.629617775848601 | 0.14223087889069697 |
| 472.27 | 469.24091010473035 | 3.0290898952696352 | 0.6842724817647582 |
| 476.7 | 472.7393314749417 | 3.9606685250582814 | 0.8947164246665408 |
| 473.93 | 472.63331852543564 | 1.296681474564366 | 0.2929208050392189 |
| 476.04 | 471.38775711954423 | 4.652242880455788 | 1.0509433168534397 |
| 471.92 | 475.2099855538805 | -3.289985553880456 | -0.7432089035850772 |
| 469.9 | 471.84963289726664 | -1.9496328972666674 | -0.4404227630306511 |
| 467.19 | 474.9342554366788 | -7.744255436678827 | -1.749430050046347 |
| 464.07 | 472.0765967355643 | -8.006596735564301 | -1.8086930425174812 |
| 472.45 | 477.1428353332941 | -4.692835333294113 | -1.0601131663478296 |
| 475.51 | 470.47813470324115 | 5.031865296758838 | 1.1367001553490168 |
| 476.91 | 473.32755876405025 | 3.582441235949773 | 0.8092747458990647 |
| 469.04 | 472.10000669812564 | -3.060006698125619 | -0.6912565984961676 |
| 474.49 | 471.88884153658796 | 2.601158463412048 | 0.5876026195201212 |
| 474.29 | 471.68655893301997 | 2.6034410669800536 | 0.588118260475777 |
| 465.61 | 472.62327183365306 | -7.01327183365305 | -1.5843005948416944 |
| 477.71 | 472.2836129719507 | 5.426387028049305 | 1.2258227543850637 |
| 460.6 | 470.4334505230224 | -9.83345052302235 | -2.221379961092406 |
| 474.72 | 471.91129640538503 | 2.8087035946149967 | 0.634487138275479 |
| 476.89 | 474.3054501914308 | 2.584549808569193 | 0.5838507185000019 |
| 471.56 | 474.6794786091428 | -3.1194786091427886 | -0.7046913242897344 |
| 473.54 | 475.187496898361 | -1.6474968983609983 | -0.37217013370970675 |
| 467.96 | 469.771457326924 | -1.8114573269240282 | -0.40920885267913865 |
| 475.13 | 472.08490735121984 | 3.0450926487801553 | 0.6878875094589886 |
| 470.88 | 473.8032225628117 | -2.923222562811702 | -0.6603570138111878 |
| 474.22 | 472.09588182193215 | 2.1241181780678744 | 0.47983904985387016 |
| 476.41 | 471.4926212025492 | 4.917378797450851 | 1.1108376145467862 |
| 465.45 | 471.19014476340215 | -5.7401447634021565 | -1.2967007380916762 |
| 478.51 | 471.43677609572336 | 7.073223904276631 | 1.597843788860663 |
| 464.43 | 469.7436046712689 | -5.313604671268877 | -1.2003451799842708 |
| 475.19 | 471.72684171223557 | 3.463158287764429 | 0.7823286855941306 |
| 476.12 | 474.3311768410223 | 1.7888231589777206 | 0.4040957861116898 |
| 466.52 | 470.11266519044227 | -3.5926651904422897 | -0.8115843408453783 |
| 466.75 | 469.0361408145477 | -2.286140814547707 | -0.5164400209043697 |
| 476.97 | 474.02676004123384 | 2.943239958766185 | 0.6648789506575099 |
| 476.73 | 470.9633331252549 | 5.766666874745113 | 1.302692092451488 |
| 473.82 | 471.12775460619287 | 2.692245393807127 | 0.6081791894050589 |
| 478.29 | 473.91084477665686 | 4.379155223343162 | 0.9892527182470402 |
| 473.79 | 470.2438958288006 | 3.546104171199431 | 0.8010661900831437 |
| 477.75 | 471.7153938676623 | 6.034606132337672 | 1.3632196657801583 |
| 470.33 | 474.55914246739445 | -4.2291424673944675 | -0.95536478346845 |
| 474.57 | 469.8009518761225 | 4.769048123877496 | 1.0773296627734226 |
| 464.57 | 470.2642322610151 | -5.694232261015088 | -1.2863290875171947 |
| 469.34 | 471.3638012594579 | -2.023801259457912 | -0.45717742235729764 |
| 465.82 | 471.92094622344274 | -6.100946223442747 | -1.3782059158917384 |
| 477.74 | 471.85580587680386 | 5.884194123196153 | 1.3292415395636934 |
| 474.28 | 470.30107562853505 | 3.9789243714649274 | 0.898840426845182 |
| 464.14 | 466.34356012780466 | -2.2035601278046784 | -0.49778501447760914 |
| 454.18 | 466.58075506687766 | -12.40075506687765 | -2.8013349681766146 |
| 467.21 | 469.3396022995035 | -2.1296022995035173 | -0.4810779148314091 |
| 470.36 | 471.72756140636227 | -1.367561406362256 | -0.30893260677359485 |
| 476.84 | 470.5786344502193 | 6.261365549780692 | 1.4144446986124883 |
| 474.35 | 470.6068799512958 | 3.7431200487042133 | 0.8455721466933415 |
| 477.61 | 472.5235663152981 | 5.086433684701888 | 1.149027173540852 |
| 478.1 | 471.9097423001995 | 6.1902576998004974 | 1.398381410079887 |
| 462.1 | 471.2847044384862 | -9.184704438486165 | -2.07482799048434 |
| 474.82 | 471.2662319288046 | 3.5537680711954067 | 0.802797467810633 |
| 463.03 | 469.73593028388524 | -6.705930283885266 | -1.5148720297345803 |
| 472.68 | 470.7066911497908 | 1.9733088502092073 | 0.4457711691982717 |
| 474.91 | 471.33177180371854 | 3.5782281962814864 | 0.8083230187436264 |
| 468.91 | 470.52692515963395 | -1.6169251596339222 | -0.3652639670874125 |
| 464.45 | 470.2092170118331 | -5.75921701183313 | -1.301009165777192 |
| 466.83 | 466.2832533837298 | 0.546746616270184 | 0.12351025454739102 |
| 475.24 | 470.18594349686214 | 5.054056503137872 | 1.1417131567412993 |
| 473.84 | 475.4613835085186 | -1.6213835085186474 | -0.36627110968185583 |
| 475.13 | 474.5994035325814 | 0.5305964674186043 | 0.11986193019333387 |
| 476.42 | 472.0863918606857 | 4.333608139314322 | 0.9789636158092969 |
| 474.46 | 470.7913069417126 | 3.6686930582873742 | 0.8287590631587972 |
| 468.13 | 467.56407826412726 | 0.5659217358727346 | 0.12784192086705481 |
| 470.27 | 471.5687225134983 | -1.2987225134983191 | -0.2933818764583589 |
| 471.6 | 469.9595844589464 | 1.6404155410536418 | 0.37057045258220545 |
| 472.49 | 471.6990473850642 | 0.7909526149358044 | 0.17867647626623853 |
| 479.32 | 473.0943993730868 | 6.225600626913206 | 1.4063653898508444 |
| 471.65 | 470.00140680122996 | 1.6485931987700155 | 0.3724177883609634 |
| 474.71 | 472.62554215897904 | 2.084457841020935 | 0.47087976564737744 |
| 467.2 | 468.51057584459073 | -1.310575844590744 | -0.29605954815653446 |
| 468.37 | 466.99762401287654 | 1.372375987123462 | 0.3100202222752913 |
| 464.4 | 467.7515630344035 | -3.3515630344035117 | -0.7571192782768728 |
| 467.47 | 468.17545309508614 | -0.7054530950861135 | -0.159362104405368 |
| 468.43 | 466.2393815815799 | 2.190618418420115 | 0.4948614777371899 |
| 466.05 | 470.09910156010346 | -4.049101560103452 | -0.914693478650593 |
| 471.1 | 470.4126440262426 | 0.6873559737574055 | 0.1552739582781331 |
| 468.01 | 470.1081379355774 | -2.0981379355774266 | -0.47397010386004174 |
| 477.41 | 473.04817642574005 | 4.361823574259972 | 0.9853374925715822 |
| 469.56 | 470.7822892277393 | -1.2222892277392816 | -0.27611557009437565 |
| 467.18 | 467.70575905298347 | -0.5257590529834602 | -0.11876915655659566 |
| 461.35 | 469.8226213597647 | -8.472621359764673 | -1.9139681704240736 |
| 474.4 | 474.3780743285961 | 2.192567140389201e-2 | 4.953016947212214e-3 |
| 473.26 | 468.30493209232344 | 4.9550679076765505 | 1.1193515979151634 |
| 470.04 | 469.5568353664925 | 0.4831646335074993 | 0.10914706208866293 |
| 472.31 | 471.0025099661929 | 1.3074900338070847 | 0.2953624624059635 |
| 467.19 | 469.9361134525985 | -2.746113452598479 | -0.6203480029931175 |
| 476.2 | 469.7928661275291 | 6.407133872470865 | 1.447373814412471 |
| 461.88 | 469.1737219130261 | -7.293721913026104 | -1.6476543672481883 |
| 471.24 | 474.2884906123142 | -3.048490612314197 | -0.688655110626592 |
| 473.02 | 468.06258267245875 | 4.957417327541236 | 1.119882332695939 |
| 471.32 | 473.485146860658 | -2.1651468606580124 | -0.489107443803926 |
| 474.23 | 472.61404029065585 | 1.6159597093441675 | 0.36504587152451 |
| 466.85 | 469.903915514171 | -3.053915514170967 | -0.6898805978802584 |
| 465.78 | 470.2469481759357 | -4.466948175935727 | -1.0090851773780105 |
| 473.46 | 467.632447347929 | 5.827552652071006 | 1.3164462111456963 |
| 466.64 | 470.9425442114299 | -4.302544211429904 | -0.9719462634817788 |
| 466.2 | 471.494284203131 | -5.294284203130985 | -1.195980679378918 |
| 467.28 | 468.30907898088435 | -1.0290789808843783 | -0.2324693067978492 |
| 475.73 | 468.87573922525866 | 6.854260774741363 | 1.5483799402320506 |
| 470.89 | 474.237754775417 | -3.347754775417002 | -0.7562589912210242 |
| 466.09 | 465.69130458295615 | 0.3986954170438253 | 9.006543612814395e-2 |
| 475.61 | 472.8180343417018 | 2.7919656582982384 | 0.6307060325245487 |
| 465.75 | 466.9269506815448 | -1.1769506815447812 | -0.2658735764273784 |
| 474.81 | 469.0391508364556 | 5.770849163544426 | 1.3036368729747179 |
| 474.26 | 468.1238036853617 | 6.136196314638312 | 1.3861689239960837 |
| 471.48 | 471.9340237695587 | -0.45402376955865975 | -0.10256413058630598 |
| 475.77 | 467.85769076077656 | 7.9123092392234184 | 1.7873934636501403 |
| 470.31 | 468.53947222591256 | 1.7705277740874408 | 0.39996285217557137 |
| 469.12 | 473.38202950699997 | -4.262029506999966 | -0.962793977380421 |
| 475.13 | 469.76472317857633 | 5.365276821423663 | 1.2120179370324367 |
| 467.41 | 468.04615604018346 | -0.6361560401834367 | -0.14370787512306984 |
| 469.83 | 469.00047164404333 | 0.8295283559566542 | 0.1873907498457296 |
| 474.46 | 474.0667420199239 | 0.39325798007610047 | 8.883711718846676e-2 |
| 472.18 | 467.138305828078 | 5.041694171922018 | 1.1389205017346977 |
| 468.46 | 468.5292032616302 | -6.920326163020718e-2 | -1.5633041348778628e-2 |
| 475.03 | 468.1969526319267 | 6.833047368073267 | 1.5435878241412961 |
| 473.49 | 469.4125049461586 | 4.077495053841403 | 0.9211075789572168 |
| 469.02 | 468.259374271566 | 0.760625728433979 | 0.17182562184847788 |
| 468.9 | 473.50603668317063 | -4.606036683170657 | -1.0405053204973118 |
| 471.19 | 468.47422142636185 | 2.715778573638147 | 0.6134953430761453 |
| 474.13 | 470.0383017110002 | 4.091698288999794 | 0.924316094817409 |
| 472.01 | 468.0562294553348 | 3.95377054466519 | 0.893158168449016 |
| 475.89 | 472.76694427363464 | 3.123055726365351 | 0.7054993963391207 |
| 471.0 | 466.0557279256278 | 4.944272074372179 | 1.1169128153424053 |
| 472.37 | 466.7407287783581 | 5.629271221641886 | 1.2716543656809969 |
| 466.08 | 464.79214736202914 | 1.2878526379708433 | 0.2909263677211387 |
| 471.61 | 470.21248865654456 | 1.3975113434554487 | 0.3156983081862408 |
| 471.44 | 472.8484021647681 | -1.4084021647681197 | -0.3181585485837661 |
| 462.3 | 470.24854120855605 | -7.948541208556037 | -1.79557827837392 |
| 461.49 | 470.2646001553115 | -8.774600155311475 | -1.9821852874504897 |
| 464.7 | 465.51076750880605 | -0.810767508806066 | -0.18315266781990724 |
| 467.68 | 466.3776971038656 | 1.3023028961343925 | 0.29419068616581573 |
| 472.41 | 466.5610830112806 | 5.848916988719452 | 1.321272422372547 |
| 468.58 | 470.92659992793443 | -2.3465999279344487 | -0.5300977560633852 |
| 472.22 | 468.98765579029424 | 3.2323442097057864 | 0.7301877034905364 |
| 469.18 | 467.5995301073336 | 1.5804698926664287 | 0.35702870934871034 |
| 473.88 | 474.44599166986796 | -0.5659916698679694 | -0.12785771898138407 |
| 461.12 | 467.5690713933053 | -6.449071393305303 | -1.4568475152442806 |
| 472.4 | 467.1768048505422 | 5.2231951494577515 | 1.1799216369387262 |
| 458.49 | 470.12758725908657 | -11.637587259086558 | -2.6289350896996853 |
| 475.36 | 469.2980914389405 | 6.061908561059511 | 1.36938729411264 |
| 473.0 | 469.1672380696391 | 3.8327619303609026 | 0.8658222795557158 |
| 461.44 | 470.1249314556345 | -8.684931455634512 | -1.961929096387823 |
| 463.9 | 471.81028761580865 | -7.91028761580867 | -1.7869367782036754 |
| 461.06 | 471.424799923348 | -10.364799923348016 | -2.3414119790965118 |
| 466.46 | 469.58969888462434 | -3.1296988846243607 | -0.7070000881461707 |
| 472.46 | 467.6133054100996 | 4.84669458990038 | 1.094870026989326 |
| 471.73 | 466.56182696263306 | 5.168173037366955 | 1.1674921223009784 |
| 471.05 | 469.14226719628124 | 1.9077328037187726 | 0.4309575170361471 |
| 467.21 | 469.69281643752356 | -2.4828164375235815 | -0.5608691138957983 |
| 472.59 | 468.5153727218602 | 4.074627278139758 | 0.9204597474090102 |
| 466.33 | 464.3095114085993 | 2.0204885914006923 | 0.45642908946816274 |
| 472.04 | 466.169002951847 | 5.870997048153015 | 1.3262603156304236 |
| 472.17 | 469.2063750462149 | 2.9636249537851427 | 0.6694839282628849 |
| 466.51 | 472.79218089209587 | -6.28218089209588 | -1.4191468918247268 |
| 460.8 | 469.8764663630868 | -9.076466363086809 | -2.0503769708593955 |
| 463.97 | 470.87346939701916 | -6.903469397019137 | -1.559496185458982 |
| 464.56 | 468.2319508045607 | -3.6719508045607085 | -0.8294949891974716 |
| 469.12 | 472.4449714286485 | -3.324971428648496 | -0.7511122251047269 |
| 471.26 | 467.1630433917525 | 4.096956608247467 | 0.9255039510005931 |
| 466.06 | 469.3130021437414 | -3.253002143741412 | -0.7348543381165886 |
| 470.66 | 470.0399558829108 | 0.6200441170892077 | 0.14006818598115695 |
| 463.65 | 464.0442884425802 | -0.3942884425802049 | -8.906989903365076e-2 |
| 470.35 | 467.7114787859095 | 2.638521214090531 | 0.5960428781510217 |
| 465.92 | 470.46126451393184 | -4.5412645139318215 | -1.02587326449146 |
| 466.67 | 467.51203531407947 | -0.8420353140794532 | -0.19021607612190924 |
| 471.38 | 469.4046202019984 | 1.9753797980015975 | 0.4462389970391413 |
| 468.45 | 470.40326389642064 | -1.9532638964206512 | -0.4412430070274576 |
| 467.22 | 469.66353144520883 | -2.443531445208805 | -0.5519946201974366 |
| 462.88 | 465.52654943877593 | -2.646549438775935 | -0.5978564569550795 |
| 473.56 | 467.6786715108734 | 5.881328489126588 | 1.3285941918109334 |
| 469.81 | 465.64964430715304 | 4.160355692846963 | 0.9398258266993826 |
| 468.66 | 467.8607823790049 | 0.7992176209951367 | 0.18054354406665346 |
| 468.35 | 467.26426723260613 | 1.0857327673938926 | 0.24526741726554308 |
| 469.94 | 467.0983914780623 | 2.841608521937701 | 0.6419203730291116 |
| 463.49 | 467.7236799517389 | -4.233679951738907 | -0.9563898027913122 |
| 465.48 | 467.5049711098421 | -2.024971109842056 | -0.4574416919740238 |
| 464.14 | 466.31353063126903 | -2.1735306312690454 | -0.49100134055869604 |
| 471.16 | 467.46352561567426 | 3.696474384325768 | 0.8350348745649541 |
| 472.67 | 467.6987016384138 | 4.971298361586207 | 1.1230180632103244 |
| 468.88 | 470.13332337428324 | -1.2533233742832408 | -0.2831261948065207 |
| 464.79 | 466.32771593378925 | -1.5377159337892294 | -0.34737057487344897 |
| 461.18 | 461.3756491943329 | -0.19564919433287287 | -4.4197222396898606e-2 |
| 468.82 | 467.03627043956425 | 1.783729560435745 | 0.4029451403943494 |
| 461.96 | 465.6730488393365 | -3.7130488393365226 | -0.8387790498308709 |
| 462.81 | 466.20636936169376 | -3.3963693616937576 | -0.76724104350462 |
| 465.62 | 471.8419294419814 | -6.221929441981388 | -1.405536067872571 |
| 464.79 | 467.025423832653 | -2.235423832652998 | -0.5049830367049549 |
| 465.39 | 467.36024219232854 | -1.9702421923285556 | -0.4450784100952837 |
| 465.51 | 470.36369995609516 | -4.85369995609517 | -1.0964525416975441 |
| 465.25 | 466.34250670048556 | -1.0925067004855578 | -0.24679765115367583 |
| 469.75 | 466.9286675356811 | 2.821332464318914 | 0.6373400044210754 |
| 468.64 | 466.5450197688411 | 2.0949802311588996 | 0.47325677731185334 |
| 456.71 | 469.52890927618625 | -12.818909276186275 | -2.895796152379444 |
| 468.87 | 464.17371789096717 | 4.696282109032836 | 1.0608917942097844 |
| 463.77 | 466.22172115408836 | -2.451721154088375 | -0.5538446783382351 |
| 464.16 | 468.9140947361635 | -4.754094736163495 | -1.0739516871848376 |
| 472.42 | 469.50273867747836 | 2.9172613225216537 | 0.6590103675152038 |
| 466.12 | 465.2396919188332 | 0.8803080811667883 | 0.19886190778235394 |
| 465.89 | 469.291500068156 | -3.401500068155997 | -0.7684000719143101 |
| 463.3 | 467.5233892738298 | -4.2233892738298096 | -0.9540651350015262 |
| 472.32 | 468.6339203654876 | 3.686079634512396 | 0.8326866968950778 |
| 472.88 | 468.23518412428797 | 4.644815875712027 | 1.049265553847453 |
| 472.52 | 467.40072495010907 | 5.119275049890916 | 1.1564460495859417 |
| 466.2 | 470.2308690130346 | -4.030869013034589 | -0.9105747398006214 |
| 464.32 | 470.06934793478035 | -5.74934793478036 | -1.298779737770393 |
| 471.52 | 468.2061275247934 | 3.313872475206608 | 0.7486049675853867 |
| 471.05 | 466.5674959516581 | 4.482504048341923 | 1.0125992544722646 |
| 465.0 | 467.4457105564226 | -2.4457105564226254 | -0.552486882193573 |
| 468.51 | 468.3292283291916 | 0.18077167080838308 | 4.083638455563342e-2 |
| 466.2 | 466.52892029567596 | -0.3289202956759709 | -7.43032114617912e-2 |
| 466.67 | 464.97984688167367 | 1.69015311832635 | 0.38180618893011364 |
| 458.19 | 469.62052738975217 | -11.430527389752172 | -2.5821601917725587 |
| 466.75 | 466.9234567199092 | -0.1734567199092112 | -3.9183934552884936e-2 |
| 467.83 | 471.0002382734735 | -3.170238273473501 | -0.7161579504665817 |
| 463.54 | 468.4888161194508 | -4.9488161194507825 | -1.1179393167374423 |
| 467.21 | 466.1266303832576 | 1.0833696167424023 | 0.24473358069515785 |
| 468.92 | 467.25704554531416 | 1.6629544546858597 | 0.37566200116631454 |
| 466.17 | 469.9193063400854 | -3.749306340085411 | -0.8469696321106422 |
| 473.56 | 469.33864000185486 | 4.221359998145147 | 0.9536067209045733 |
| 462.54 | 462.5353944778779 | 4.60552212211951e-3 | 1.040389080973342e-3 |
| 460.42 | 469.0357845125853 | -8.615784512585265 | -1.9463087774264596 |
| 463.1 | 465.4074674853422 | -2.3074674853421584 | -0.5212577234014426 |
| 469.35 | 469.2340241993221 | 0.11597580067791569 | 2.6198974509955283e-2 |
| 464.5 | 465.48772540492894 | -0.9877254049289377 | -0.22312751932133126 |
| 467.01 | 468.6698381136833 | -1.6598381136832927 | -0.3749580186284632 |
| 470.13 | 466.5182432985413 | 3.6117567014587166 | 0.8158971199557807 |
| 467.25 | 464.80335793821706 | 2.4466420617829385 | 0.5526973095848743 |
| 464.6 | 469.0300144538734 | -4.430014453873355 | -1.0007418364636667 |
| 464.56 | 468.0542535304745 | -3.4942535304745093 | -0.7893531119518733 |
| 470.44 | 466.2407858068176 | 4.199214193182399 | 0.948603975708334 |
| 464.23 | 466.68020136327283 | -2.450201363272811 | -0.5535013570539268 |
| 470.8 | 468.07562484757494 | 2.7243751524250683 | 0.615437313273332 |
| 470.19 | 468.4620083288529 | 1.7279916711470946 | 0.390353931433719 |
| 465.45 | 465.35539799683 | 9.46020031699959e-2 | 2.1370626071593804e-2 |
| 465.14 | 465.34301144661265 | -0.20301144661266335 | -4.58603577983067e-2 |
| 457.21 | 465.4373435562456 | -8.227343556245614 | -1.8585598276086635 |
| 462.69 | 470.2899851111997 | -7.5999851111997145 | -1.7168393323476852 |
| 460.66 | 465.705988879045 | -5.045988879044955 | -1.1398906775971094 |
| 462.64 | 464.775180629532 | -2.1351806295319875 | -0.4823380615634837 |
| 475.98 | 471.7241650123044 | 4.255834987695607 | 0.96139463327236 |
| 469.61 | 470.86762962520095 | -1.2576296252009342 | -0.28409897841626214 |
| 464.95 | 470.0085068177468 | -5.0585068177468315 | -1.1427184843899973 |
| 467.18 | 465.090966572103 | 2.089033427896993 | 0.47191339234564744 |
| 463.6 | 467.14100725665213 | -3.541007256652108 | -0.7999147952790192 |
| 462.77 | 465.7842034756254 | -3.014203475625436 | -0.6809096342868951 |
| 464.93 | 467.49881987016624 | -2.5688198701662373 | -0.5802973198353383 |
| 465.49 | 466.18608052784475 | -0.6960805278447424 | -0.15724483814107668 |
| 465.82 | 465.9570356485115 | -0.13703564851152805 | -3.095640160553223e-2 |
| 461.52 | 465.6600911572598 | -4.140091157259803 | -0.935248060922378 |
| 460.54 | 461.72665249570616 | -1.1866524957061415 | -0.2680652196876797 |
| 460.54 | 466.6642858393167 | -6.124285839316656 | -1.3834783433962559 |
| 463.22 | 462.1388114830899 | 1.0811885169101174 | 0.24424086946940488 |
| 467.32 | 467.1954080636746 | 0.12459193632537335 | 2.8145362608873965e-2 |
| 467.66 | 467.57038570428426 | 8.961429571576218e-2 | 2.0243901188534075e-2 |
| 470.71 | 466.8779893764293 | 3.8320106235706817 | 0.8656525590853151 |
| 466.34 | 468.28371557585353 | -1.9437155758535596 | -0.43908603802454604 |
| 464.0 | 465.49312878230023 | -1.4931287823002322 | -0.3372983215369459 |
| 454.16 | 460.9202947940051 | -6.760294794005063 | -1.527152991853827 |
| 460.19 | 464.1520666190232 | -3.962066619023176 | -0.8950322545891167 |
| 467.71 | 465.5258417359535 | 2.1841582640464594 | 0.49340212657275334 |
| 465.01 | 464.28156360850716 | 0.7284363914928349 | 0.16455403921586076 |
| 462.87 | 464.1635216732549 | -1.2935216732548724 | -0.2922070048026174 |
| 466.73 | 466.3736543730528 | 0.35634562694724536 | 8.049860352378384e-2 |
| 469.34 | 467.08552274770574 | 2.2544772522942367 | 0.5092872109601911 |
| 458.99 | 465.25377872135545 | -6.263778721355436 | -1.4149898349271792 |
| 465.72 | 464.56900508273293 | 1.1509949172670986 | 0.26001016007049604 |
| 465.63 | 461.87533959682145 | 3.7546604031785478 | 0.848179116862477 |
| 471.24 | 464.4149735638803 | 6.8250264361196855 | 1.541775892767944 |
| 456.41 | 468.5317634787664 | -12.121763478766354 | -2.7383106694634813 |
| 460.51 | 466.42415027580006 | -5.914150275800068 | -1.3360086450624162 |
| 467.77 | 466.6719213555882 | 1.0980786444117712 | 0.24805635526298545 |
| 456.63 | 464.60786745115865 | -7.977867451158659 | -1.8022030869799206 |
| 468.02 | 467.544960954765 | 0.4750390452350075 | 0.10731148881574702 |
| 463.93 | 466.26419991369926 | -2.3341999136992513 | -0.5272965884493478 |
| 463.12 | 465.43441550001705 | -2.3144155000170485 | -0.522827282380973 |
| 460.06 | 467.4928608009505 | -7.432860800950493 | -1.6790858913831084 |
| 461.6 | 466.41675498345813 | -4.816754983458111 | -1.0881066592743878 |
| 463.27 | 466.61302775183003 | -3.34302775183005 | -0.7551911549160485 |
| 467.82 | 462.99098636012917 | 4.829013639870823 | 1.0908758941061858 |
| 462.59 | 464.3870766666383 | -1.7970766666383042 | -0.40596025641974887 |
| 460.15 | 465.9823776729848 | -5.832377672984819 | -1.3175361850815699 |
| 463.1 | 464.5934173732165 | -1.4934173732164595 | -0.33736351432728534 |
| 461.6 | 464.9855482511964 | -3.385548251196383 | -0.7647965508049966 |
| 462.64 | 466.6093718548404 | -3.9693718548404036 | -0.8966825099514081 |
| 460.56 | 459.9584434481331 | 0.6015565518669064 | 0.13589183843986818 |
| 459.85 | 461.87302187524506 | -2.0230218752450355 | -0.45700135918716 |
| 465.99 | 465.2019993258076 | 0.7880006741924035 | 0.17800963180524587 |
| 464.49 | 465.03190605144545 | -0.5419060514454372 | -0.12241676923652416 |
| 468.62 | 464.51031407803373 | 4.109685921966275 | 0.928379507484781 |
| 458.26 | 464.7510595901315 | -6.491059590131499 | -1.4663326638006033 |
| 466.5 | 462.10563966655616 | 4.394360333443842 | 0.9926875580120624 |
| 459.31 | 463.52420449023833 | -4.21420449023833 | -0.9519902891303528 |
| 459.77 | 465.12795484714036 | -5.357954847140377 | -1.2103639004447255 |
| 457.1 | 458.39794118753656 | -1.2979411875365372 | -0.29320537464645474 |
| 469.18 | 464.49240158564226 | 4.687598414357751 | 1.0589301445025614 |
| 472.28 | 463.4938095964465 | 8.786190403553462 | 1.9848035286394352 |
| 459.15 | 464.74092024201923 | -5.590920242019251 | -1.262990865781086 |
| 466.63 | 468.69706628494185 | -2.067066284941859 | -0.4669510069602996 |
| 462.69 | 464.01147313398934 | -1.3214731339893433 | -0.29852124969696514 |
| 468.53 | 466.52540885533836 | 2.0045911446616174 | 0.4528378506109543 |
| 453.99 | 463.6262300043777 | -9.636230004377694 | -2.176827775976124 |
| 471.97 | 469.18664060318713 | 2.783359396812898 | 0.6287618750023547 |
| 457.74 | 458.7253721171194 | -0.9853721171193683 | -0.22259591077043436 |
| 466.83 | 462.85471321992253 | 3.975286780077454 | 0.8980186936604918 |
| 466.2 | 469.16650047432273 | -2.9665004743227428 | -0.6701335093722809 |
| 465.14 | 464.90298984127037 | 0.23701015872961761 | 5.3540678924927795e-2 |
| 457.36 | 466.2852656032666 | -8.92526560326661 | -2.0162206655848456 |
| 439.21 | 465.39633546049805 | -26.18633546049807 | -5.915502468864325 |
| 462.59 | 462.31339644999167 | 0.2766035500083035 | 6.248484005862629e-2 |
| 457.3 | 457.58467899039783 | -0.28467899039782196 | -6.430908490699098e-2 |
| 463.57 | 462.46440155189674 | 1.105598448103251 | 0.24975508158417795 |
| 462.09 | 461.6436673101792 | 0.44633268982079244 | 0.10082671294548266 |
| 465.41 | 465.13586499514474 | 0.27413500485528175 | 6.192719483296151e-2 |
| 464.17 | 468.0777122780922 | -3.9077122780921627 | -0.8827535896932235 |
| 464.59 | 463.94240853336476 | 0.6475914666352196 | 0.14629114201469284 |
| 464.38 | 462.7655254273002 | 1.6144745726998053 | 0.3647103786297681 |
| 464.95 | 464.3512532840681 | 0.5987467159318953 | 0.13525709550549558 |
| 455.15 | 458.1774466179169 | -3.027446617916951 | -0.6839012648279135 |
| 456.91 | 458.36321179088014 | -1.4532117908801183 | -0.3282810590165173 |
| 456.21 | 467.4754642409999 | -11.265464240999904 | -2.5448723679212413 |
| 467.3 | 462.93565136830296 | 4.364348631697055 | 0.9859079039422615 |
| 464.72 | 460.79339608207107 | 3.926603917928958 | 0.8870212178332524 |
| 462.58 | 466.71318747004864 | -4.133187470048654 | -0.9336885155326297 |
| 466.6 | 469.2173130099923 | -2.6173130099923014 | -0.5912519373226456 |
| 459.42 | 464.720482732063 | -5.30048273206296 | -1.19738092926328 |
| 464.14 | 464.335595847906 | -0.19559584790602003 | -4.418517142015048e-2 |
| 460.45 | 465.0880608446689 | -4.638060844668928 | -1.0477395899387958 |
| 468.91 | 465.0497057218854 | 3.8602942781146226 | 0.8720418466790518 |
| 466.39 | 467.13452750857033 | -0.7445275085703429 | -0.16818902827121646 |
| 467.22 | 463.63133669846115 | 3.5886633015388725 | 0.8106803127226195 |
| 462.77 | 462.43127985662505 | 0.3387201433749283 | 7.651700053300835e-2 |
| 464.95 | 462.0817954663473 | 2.8682045336527153 | 0.6479284215091841 |
| 457.12 | 462.7228887148107 | -5.602888714810717 | -1.2656945480299158 |
| 461.5 | 463.2389898882613 | -1.738989888261301 | -0.3928384325809046 |
| 458.46 | 465.7322155460913 | -7.272215546091331 | -1.6427960713292782 |
| 465.89 | 465.9391561803266 | -4.91561803266336e-2 | -1.1104398571567456e-2 |
| 466.15 | 464.67588000342283 | 1.4741199965771443 | 0.33300423009965163 |
| 455.48 | 462.03627301808893 | -6.556273018088916 | -1.4810644003074978 |
| 464.95 | 468.46315966006046 | -3.5131596600604666 | -0.7936240133313774 |
| 454.88 | 456.7218449478403 | -1.8418449478402863 | -0.41607342702266226 |
| 457.41 | 460.9973530979208 | -3.5873530979207544 | -0.8103843372605553 |
| 468.88 | 468.60583110746245 | 0.2741688925375456 | 6.1934850072404936e-2 |
| 461.86 | 468.0452621538944 | -6.185262153894371 | -1.3972529144877643 |
| 456.08 | 461.16748971043216 | -5.08748971043218 | -1.1492657301278992 |
| 462.94 | 462.09664222758136 | 0.8433577724186421 | 0.19051481992981023 |
| 460.87 | 460.8634611992421 | 6.538800757880381e-3 | 1.4771174105289492e-3 |
| 461.06 | 464.39198726436115 | -3.331987264361146 | -0.7526971048807434 |
| 454.94 | 458.02055270119473 | -3.0805527011947333 | -0.6958979478771827 |
| 466.22 | 464.5123542802937 | 1.7076457197063064 | 0.3857577737865013 |
| 459.95 | 461.1346442030387 | -1.1846442030387152 | -0.26761154566176326 |
| 463.47 | 463.18977819659995 | 0.2802218034000816 | 6.330220478322962e-2 |
| 456.58 | 464.6775725663142 | -8.097572566314227 | -1.8292445149531718 |
| 463.57 | 465.5402356742791 | -1.9702356742790812 | -0.4450769376655955 |
| 462.44 | 462.1000323229214 | 0.33996767707861864 | 7.679881883917433e-2 |
| 448.59 | 459.28384302135794 | -10.693843021357964 | -2.4157429316491066 |
| 462.5 | 464.0520812837949 | -1.5520812837949052 | -0.35061571253514523 |
| 463.76 | 462.0021066209579 | 1.757893379042116 | 0.39710873785339107 |
| 459.48 | 464.6824431291315 | -5.202443129131495 | -1.1752337481862478 |
| 455.05 | 457.21309738475657 | -2.163097384756554 | -0.488644466470834 |
| 456.11 | 461.3420214113817 | -5.232021411381709 | -1.1819154926380382 |
| 472.17 | 467.91529134370023 | 4.254708656299783 | 0.9611401946105317 |
| 461.54 | 465.4513233379416 | -3.9113233379415533 | -0.88356932939405 |
| 459.69 | 461.3200136826322 | -1.6300136826321818 | -0.3682206690752407 |
| 461.54 | 459.19347653551756 | 2.346523464482459 | 0.5300804829424589 |
| 462.48 | 459.1522349051031 | 3.327765094896904 | 0.7517433153011605 |
| 442.48 | 460.6299621913311 | -18.1499621913311 | -4.100082896844213 |
| 465.14 | 465.94867946942674 | -0.8086794694267496 | -0.18268097898348856 |
| 464.62 | 462.5145658398506 | 2.1054341601494 | 0.4756183235788339 |
| 465.78 | 463.3496922983309 | 2.430307701669051 | 0.5490073718413218 |
| 462.52 | 461.99087118644087 | 0.5291288135591117 | 0.11953038666589516 |
| 454.78 | 457.1797966088055 | -2.3997966088055023 | -0.5421149051411095 |
| 465.03 | 464.9190479188059 | 0.11095208119405697 | 2.5064114496629736e-2 |
| 463.74 | 462.72099107520137 | 1.0190089247986407 | 0.230194477556215 |
| 459.59 | 461.9941154580848 | -2.4041154580848456 | -0.5430905347252163 |
| 465.6 | 458.7011562788462 | 6.89884372115381 | 1.5584512436402695 |
| 464.7 | 461.4647200415145 | 3.235279958485478 | 0.7308508901811838 |
| 460.54 | 462.40970062782714 | -1.8697006278271147 | -0.4223660350121439 |
| 465.52 | 462.47440172010425 | 3.045598279895728 | 0.6880017317073447 |
| 465.64 | 464.23959443772316 | 1.4004055622768306 | 0.3163521132445753 |
| 468.22 | 468.12040219816635 | 9.959780183368139e-2 | 2.2499178761738656e-2 |
| 464.2 | 461.97170222353844 | 2.2282977764615453 | 0.5033732580836815 |
| 472.63 | 464.18342103476823 | 8.446578965231765 | 1.908085184261808 |
| 460.31 | 460.4941449517029 | -0.18414495170287637 | -4.159840990620793e-2 |
| 458.25 | 461.5472235985969 | -3.2972235985968723 | -0.7448439804538625 |
| 459.25 | 462.26646301676067 | -3.0164630167606674 | -0.681420065430807 |
| 463.62 | 462.2293585659873 | 1.3906414340127071 | 0.3141463932063569 |
| 457.26 | 464.22591401634116 | -6.9659140163411735 | -1.573602447113043 |
| 460.0 | 459.5632798612247 | 0.4367201387752857 | 9.865523425471445e-2 |
| 460.25 | 461.3519558174483 | -1.1019558174482995 | -0.24893221002717708 |
| 464.6 | 460.4994805562497 | 4.100519443750329 | 0.9263087968044336 |
| 461.49 | 463.40687222781077 | -1.9168722278107566 | -0.43302211618029557 |
| 449.98 | 456.35386691539543 | -6.373866915395411 | -1.4398587970092795 |
| 464.72 | 462.67093183857946 | 2.049068161420564 | 0.462885224472874 |
| 460.08 | 461.81109623075827 | -1.7310962307582827 | -0.391055252551079 |
| 454.19 | 460.0644340540906 | -5.874434054090614 | -1.3270367364908389 |
| 464.27 | 462.014274652855 | 2.25572534714496 | 0.5095691560296709 |
| 456.55 | 460.5836092644097 | -4.033609264409677 | -0.9111937635582255 |
| 464.5 | 463.360199769915 | 1.1398002300849726 | 0.25748127626527917 |
| 468.8 | 464.34868588625807 | 4.451314113741944 | 1.0055534371830066 |
| 465.16 | 461.6467454037935 | 3.5132545962065365 | 0.7936454594404486 |
| 463.65 | 462.4960995942487 | 1.1539004057513012 | 0.26066651094965915 |
| 466.36 | 460.43082906265056 | 5.929170937349454 | 1.3394018178342768 |
| 455.53 | 462.1757595182412 | -6.645759518241221 | -1.5012794324329175 |
| 454.06 | 459.4288341311474 | -5.368834131147423 | -1.212821534560818 |
| 453.0 | 454.3258801823864 | -1.3258801823864133 | -0.2995168034930508 |
| 461.47 | 460.83972369537236 | 0.6302763046276709 | 0.14237964077547013 |
| 457.67 | 459.652078633235 | -1.9820786332350053 | -0.44775226629445647 |
| 458.67 | 457.84281119185783 | 0.8271888081421821 | 0.18686224516459876 |
| 461.01 | 462.29977029786545 | -1.2897702978654593 | -0.2913595677715297 |
| 458.34 | 458.99629100134234 | -0.6562910013423675 | -0.14825636999077735 |
| 457.01 | 456.94689658887796 | 6.310341112202877e-2 | 1.4255082955353757e-2 |
| 455.75 | 455.92052796835753 | -0.1705279683575327 | -3.85223285385276e-2 |
| 456.62 | 455.0999707958263 | 1.5200292041736816 | 0.34337513637978356 |
| 457.67 | 463.6885973525347 | -6.018597352534698 | -1.3596032767443251 |
| 456.16 | 456.72206228080466 | -0.5620622808046392 | -0.12697006859821677 |
| 455.97 | 460.34419741616995 | -4.37419741616992 | -0.9881327478480624 |
| 457.05 | 457.5432003190509 | -0.49320031905091355 | -0.11141412701259497 |
| 457.71 | 456.69285780084897 | 1.0171421991510101 | 0.22977278357028447 |
| 459.43 | 457.13828420727975 | 2.291715792720254 | 0.5176994104510022 |
| 457.98 | 457.7082041695146 | 0.27179583048541645 | 6.13987745130998e-2 |
| 459.13 | 460.61769921749 | -1.4876992174899897 | -0.3360717809204362 |
| 457.35 | 460.2397779497155 | -2.8897779497154943 | -0.6528018638498932 |
| 456.56 | 454.03599822567077 | 2.5240017743292356 | 0.5701728960887047 |
| 454.57 | 457.06546864918414 | -2.495468649184147 | -0.5637272529976811 |
| 451.9 | 457.7462919446442 | -5.846291944644236 | -1.3206794239848478 |
| 457.33 | 456.68265807481106 | 0.6473419251889254 | 0.1462347705134622 |
| 467.59 | 461.5754309315541 | 6.014569068445894 | 1.358693285939825 |
| 463.99 | 464.77705443177615 | -0.7870544317761414 | -0.17779587530777846 |
| 452.55 | 459.8014970847607 | -7.251497084760672 | -1.6381157635658086 |
| 452.28 | 454.94641693932414 | -2.666416939324165 | -0.602344532375961 |
| 453.55 | 454.8958623057626 | -1.34586230576258 | -0.3040307722514201 |
| 448.88 | 452.5071708494883 | -3.627170849488323 | -0.8193791814630933 |
| 448.71 | 455.24182428555105 | -6.5318242855510675 | -1.4755414229551909 |
| 454.59 | 459.58273190303845 | -4.992731903038475 | -1.1278599078269054 |
| 451.14 | 453.10756062981807 | -1.967560629818081 | -0.4444726441730127 |
| 458.01 | 456.42171751548034 | 1.5882824845196524 | 0.35879357661949324 |
| 456.55 | 456.5005129659639 | 4.948703403613308e-2 | 1.1179138541897764e-2 |
| 458.04 | 456.4920988999565 | 1.547901100043532 | 0.34967140754299175 |
| 463.31 | 463.61908166044077 | -0.3090816604407678 | -6.982165672536109e-2 |
| 454.34 | 454.4243094498896 | -8.430944988964484e-2 | -1.904553463477631e-2 |
| 454.29 | 454.14166454199494 | 0.14833545800507864 | 3.3509032578186985e-2 |
| 455.82 | 453.8961915229473 | 1.9238084770527166 | 0.43458901734435373 |
| 451.44 | 453.6649941349345 | -2.224994134934491 | -0.5026269642909126 |
| 442.45 | 449.9074433355401 | -7.45744333554012 | -1.684639094666372 |
| 454.98 | 459.22851589459475 | -4.248515894594732 | -0.9597412475447774 |
| 465.26 | 459.05202239898534 | 6.2079776010146475 | 1.4023843420494426 |
| 451.06 | 452.94903640134396 | -1.889036401343958 | -0.4267339931080307 |
| 452.77 | 453.1802042498492 | -0.41020424984924375 | -9.266528554109059e-2 |
| 463.47 | 461.3678096135659 | 2.102190386434131 | 0.47488555394598253 |
| 461.73 | 460.89674705500823 | 0.8332529449917843 | 0.18823213582988355 |
| 467.54 | 463.8188758742396 | 3.7211241257604115 | 0.8406032598983453 |
| 454.74 | 459.1270333631719 | -4.387033363171895 | -0.9910323928287407 |
| 451.04 | 455.213182837326 | -4.173182837325953 | -0.9427234831869965 |
| 464.13 | 457.4956830813669 | 6.63431691863309 | 1.4986945451227311 |
| 456.25 | 452.8810496638165 | 3.3689503361835023 | 0.7610470759162783 |
| 457.43 | 456.48090497858726 | 0.9490950214127452 | 0.21440090198276052 |
| 448.17 | 456.5591352486571 | -8.389135248657112 | -1.8951086283122098 |
| 452.93 | 453.45921998591575 | -0.5292199859157449 | -0.1195509825335976 |
| 455.24 | 454.7581350474279 | 0.4818649525720957 | 0.10885346370435622 |
| 454.44 | 461.43742015467615 | -6.997420154676149 | -1.580719694938195 |
| 460.27 | 463.53345887643894 | -3.2634588764389605 | -0.7372165177419857 |
| 450.5 | 452.02894677235804 | -1.52894677235804 | -0.3453896181957068 |
| 455.22 | 455.963340998892 | -0.7433409988919948 | -0.1679209953140206 |
| 457.17 | 456.69115708185933 | 0.47884291814068547 | 0.10817078505437988 |
| 458.47 | 455.70816977608035 | 2.7618302239196737 | 0.623898427209341 |
| 456.49 | 455.5784197907349 | 0.9115802092650824 | 0.2059262926120357 |
| 451.29 | 457.8698842565306 | -6.57988425653059 | -1.4863981874464185 |
| 453.78 | 454.0602820488982 | -0.2802820488982434 | -6.331581426263354e-2 |
| 463.2 | 460.7479119618785 | 2.452088038121474 | 0.5539275575714465 |
| 452.1 | 453.92151217696727 | -1.8215121769672464 | -0.4114802468703741 |
| 451.93 | 453.0532072948905 | -1.1232072948905056 | -0.2537329263193067 |
| 450.55 | 454.758298968509 | -4.208298968508984 | -0.9506562296769948 |
| 461.49 | 456.7605922899199 | 4.729407710080125 | 1.068374879236074 |
| 452.52 | 454.0242328275001 | -1.5042328275001182 | -0.3398067292862239 |
| 451.23 | 452.6641989591518 | -1.4341989591517859 | -0.32398605358520316 |
| 455.44 | 457.32803096446935 | -1.8880309644693511 | -0.42650686456142684 |
| 440.64 | 450.3190565318654 | -9.679056531865399 | -2.186502303726239 |
| 467.51 | 461.3397464375879 | 6.170253562412086 | 1.3938624683483432 |
| 452.37 | 456.8872770659861 | -4.517277065986093 | -1.0204544914921314 |
| 462.05 | 460.8339970164602 | 1.216002983539795 | 0.27469550529997006 |
| 464.48 | 462.10615685280686 | 2.3738431471931563 | 0.5362520089571434 |
| 453.79 | 457.0494808979769 | -3.2594808979768572 | -0.7363178909964009 |
| 449.55 | 452.3356980438659 | -2.785698043865864 | -0.6292901762011418 |
| 459.45 | 455.30258286482143 | 4.147417135178557 | 0.9369030019327613 |
| 463.02 | 462.7299869900826 | 0.2900130099174021 | 6.551404180844994e-2 |
| 455.79 | 455.2223463598715 | 0.5676536401285261 | 0.1282331586527475 |
| 453.25 | 453.31727625144936 | -6.7276251449357e-2 | -1.5197729065410778e-2 |
| 454.71 | 459.55748654713716 | -4.84748654713718 | -1.0950489304100275 |
| 450.74 | 456.7379403460756 | -5.997940346075609 | -1.3549368516581415 |
| 453.9 | 456.5169354267787 | -2.6169354267786957 | -0.5911666411407626 |
| 450.87 | 451.9912034594031 | -1.121203459403091 | -0.2532802591719359 |
| 453.28 | 457.39523074964615 | -4.115230749646173 | -0.9296320860244299 |
| 454.47 | 454.91390716792046 | -0.44390716792042895 | -0.1002787866878538 |
| 446.7 | 453.70377279073705 | -7.0037727907370595 | -1.5821547576776454 |
| 454.58 | 458.80810089986693 | -4.228100899866945 | -0.9551294929945369 |
| 458.64 | 459.2317295422404 | -0.5917295422403868 | -0.13367191347958032 |
| 454.02 | 452.8313052555021 | 1.188694744497866 | 0.2685265644141194 |
| 455.88 | 453.2048261109298 | 2.6751738890702086 | 0.6043227304296972 |
| 460.19 | 460.78946144208953 | -0.5994614420895346 | -0.13541855239802086 |
| 457.58 | 456.60185019595883 | 0.978149804041152 | 0.22096438768429855 |
| 459.68 | 456.60000256329795 | 3.079997436702058 | 0.6957725134313342 |
| 454.6 | 461.5337919917975 | -6.933791991797477 | -1.566346070374885 |
| 457.55 | 455.08314377928764 | 2.4668562207123728 | 0.5572636952570301 |
| 455.42 | 454.7344713102365 | 0.6855286897635438 | 0.15486117417577897 |
| 455.76 | 454.4253732327452 | 1.3346267672548038 | 0.3014926601172488 |
| 447.47 | 450.84382297953874 | -3.3738229795387156 | -0.7621478077785558 |
| 455.7 | 454.2509501800389 | 1.4490498199610897 | 0.32734086830965053 |
| 455.95 | 463.1377560912732 | -7.187756091273229 | -1.6237166505279563 |
| 451.05 | 455.0606464477159 | -4.010646447715885 | -0.9060064551221478 |
| 449.89 | 452.78710305999687 | -2.89710305999688 | -0.6544566088606873 |
| 451.49 | 452.43308379405045 | -0.9430837940504375 | -0.21304296358942265 |
| 456.04 | 453.7206916287245 | 2.319308371275497 | 0.5239325837337663 |
| 458.06 | 453.97719041615505 | 4.0828095838449485 | 0.9223081331700905 |
| 448.17 | 452.20842313451385 | -4.038423134513835 | -0.9122812185222428 |
| 451.8 | 452.90074763749135 | -1.100747637491338 | -0.24865928174635601 |
| 465.66 | 461.8378158906793 | 3.8221841093207445 | 0.8634327460307729 |
| 449.26 | 452.41543202652065 | -3.155432026520657 | -0.7128132140281332 |
| 451.08 | 457.75864371455697 | -6.6786437145569835 | -1.5087079840447288 |
| 467.72 | 461.3187533462041 | 6.4012466537959085 | 1.4460438896255918 |
| 463.35 | 462.37131638295244 | 0.9786836170475794 | 0.22108497623179488 |
| 453.47 | 454.29376940501464 | -0.823769405014616 | -0.18608980078521345 |
| 450.88 | 453.15381920341724 | -2.273819203417247 | -0.5136565645795023 |
| 456.08 | 457.3375291656138 | -1.2575291656137892 | -0.2840762845598929 |
| 448.04 | 451.78495851322157 | -3.7449585132215475 | -0.8459874564799138 |
| 450.17 | 451.8524216632198 | -1.6824216632197704 | -0.380059650479177 |
| 450.54 | 455.69553323527913 | -5.155533235279108 | -1.1646367865259744 |
| 456.61 | 453.9716415466405 | 2.6383584533595013 | 0.5960061104441506 |
| 448.73 | 447.41632457686086 | 1.3136754231391592 | 0.29675974405004724 |
| 456.57 | 456.66826631159057 | -9.826631159057797e-2 | -2.2198394643539735e-2 |
| 456.06 | 451.3868160236597 | 4.673183976340283 | 1.0556739178415897 |
| 457.41 | 455.8596185860852 | 1.5503814139148062 | 0.35023171132628417 |
| 446.29 | 451.60844251280884 | -5.318442512808815 | -1.2014380501041335 |
| 453.84 | 451.542577765937 | 2.297422234062992 | 0.5189884975744112 |
| 456.03 | 454.3347436794228 | 1.6952563205771867 | 0.38295900412869216 |
| 461.16 | 456.98501253896984 | 4.1749874610301845 | 0.9431311483218855 |
| 450.5 | 450.35966629930124 | 0.14033370069876128 | 3.170143276445295e-2 |
| 459.12 | 453.2290277149437 | 5.890972285056307 | 1.3307727287321278 |
| 444.87 | 450.8892362675085 | -6.019236267508518 | -1.3597476078635353 |
| 468.27 | 460.77739524450277 | 7.4926047554972115 | 1.6925820719064946 |
| 452.04 | 451.5662781980391 | 0.4737218019609486 | 0.10701392309290869 |
| 456.89 | 455.22151625901614 | 1.668483740983845 | 0.3769110689022767 |
| 456.55 | 458.86327155628703 | -2.3132715562870203 | -0.5225688650866008 |
| 455.07 | 454.4962025118092 | 0.5737974881907917 | 0.12962105610924668 |
| 454.94 | 452.9973261711798 | 1.9426738288202046 | 0.4388507070002017 |
| 444.64 | 451.56134671760844 | -6.921346717608458 | -1.5635346785212256 |
| 464.54 | 461.26403285215525 | 3.275967147844767 | 0.7400421406892627 |
| 454.28 | 454.96273138933435 | -0.6827313893343785 | -0.1542292630166233 |
| 450.26 | 450.45712572408434 | -0.19712572408434426 | -4.453077099148361e-2 |
| 450.44 | 452.7340333117575 | -2.2940333117575165 | -0.5182229388235478 |
| 449.23 | 451.3669335966618 | -2.136933596661777 | -0.48273405745988357 |
| 452.41 | 450.86300710254875 | 1.5469928974512754 | 0.34946624425526 |
| 451.75 | 450.87541624186673 | 0.8745837581332694 | 0.1975687811786549 |
| 449.66 | 451.1697942873412 | -1.5097942873411512 | -0.34106306503697253 |
| 446.03 | 450.6475445784032 | -4.617544578403226 | -1.0431049581122194 |
| 456.36 | 453.93684539976664 | 2.4231546002333744 | 0.5473914836897852 |
| 451.22 | 452.6438110022995 | -1.4238110022994874 | -0.32163941044764294 |
| 440.03 | 449.38085095551605 | -9.35085095551608 | -2.1123605476138505 |
| 462.86 | 459.86949886435633 | 2.990501135643683 | 0.6755552672777567 |
| 451.14 | 454.5790164502554 | -3.4390164502553944 | -0.7768750359376887 |
| 456.03 | 455.7041227162542 | 0.32587728374579683 | 7.361579398740335e-2 |
| 446.35 | 451.5591661954991 | -5.209166195499051 | -1.1767524912633618 |
| 448.85 | 451.4495789708702 | -2.5995789708701977 | -0.5872458115946685 |
| 448.71 | 449.41092940189 | -0.7009294018899936 | -0.15834020050780284 |
| 459.39 | 456.368436088565 | 3.0215639114350097 | 0.682572359347037 |
| 456.53 | 455.3814815227917 | 1.1485184772082562 | 0.25945073138280605 |
| 455.58 | 454.77441817350825 | 0.805581826491732 | 0.18198122034572886 |
| 459.47 | 455.56852272126105 | 3.90147727873898 | 0.8813451011277746 |
| 453.8 | 453.3887778929569 | 0.4112221070431019 | 9.289521984221884e-2 |
| 447.1 | 450.7408294300028 | -3.640829430002782 | -0.8224646596460314 |
| 446.57 | 451.436105216529 | -4.866105216528979 | -1.0992548944297087 |
| 455.28 | 454.1835978057384 | 1.0964021942616 | 0.24767764457942207 |
| 453.96 | 461.1739549532308 | -7.213954953230825 | -1.6296349827369583 |
| 454.5 | 450.58677338372456 | 3.9132266162754377 | 0.8839992806447705 |
| 449.31 | 450.2319334353776 | -0.9219334353775821 | -0.20826509006315633 |
| 449.63 | 454.6358406173096 | -5.005840617309616 | -1.1308211710304394 |
| 448.92 | 452.85108866192866 | -3.9310886619286407 | -0.888034323093527 |
| 457.14 | 451.05259673451775 | 6.087403265482237 | 1.375146556884749 |
| 450.98 | 451.5232844413466 | -0.5432844413466 | -0.12272814800411408 |
| 452.67 | 450.58585768893414 | 2.084142311065875 | 0.4708084873186104 |
| 449.03 | 451.0815006280822 | -2.0515006280822377 | -0.4634347195545461 |
| 453.33 | 451.80697350389795 | 1.5230264961020339 | 0.3440522256895429 |
| 455.13 | 453.19662660647845 | 1.9333733935215491 | 0.4367497353673552 |
| 454.36 | 455.59739505823774 | -1.2373950582377233 | -0.2795279825620374 |
| 454.84 | 451.430922332517 | 3.4090776674829613 | 0.7701118542900588 |
| 454.23 | 454.4162557726164 | -0.18625577261639137 | -4.2075245099282746e-2 |
| 447.06 | 447.51848167260005 | -0.458481672600044 | -0.10357117246458049 |
| 457.57 | 457.4899833309971 | 8.001666900287319e-2 | 1.80757939097979e-2 |
| 455.49 | 454.7734968589603 | 0.7165031410397091 | 0.16185831370575537 |
| 462.44 | 459.9896954813451 | 2.450304518654889 | 0.5535246598913276 |
| 451.59 | 453.5274885789799 | -1.9374885789799237 | -0.4376793572220653 |
| 452.45 | 451.13958592197287 | 1.3104140780271223 | 0.2960230050324334 |
| 448.79 | 451.01129177115814 | -2.2212917711581213 | -0.501790599000596 |
| 450.25 | 452.16097295111786 | -1.9109729511178557 | -0.4316894674828857 |
| 456.11 | 455.0355456385039 | 1.074454361496123 | 0.24271962137276715 |
| 445.58 | 448.2416124360999 | -2.661612436099915 | -0.6012591933934375 |
| 452.96 | 450.748723139911 | 2.211276860088958 | 0.499528227037766 |
| 452.94 | 453.9480760674323 | -1.0080760674322846 | -0.227724741199252 |
| 452.39 | 459.1480198621134 | -6.758019862113429 | -1.5266390839325845 |
| 445.96 | 448.54249260358677 | -2.582492603586786 | -0.5833859951647822 |
| 452.8 | 450.84813038147 | 1.9518696185299973 | 0.44092803915740614 |
| 458.97 | 452.9523382793844 | 6.017661720615649 | 1.3593919171619417 |
| 460.1 | 457.2797775931854 | 2.8202224068146506 | 0.637089242037123 |
| 453.83 | 450.67534900317014 | 3.1546509968298437 | 0.712636779143946 |
| 443.76 | 448.7439698617689 | -4.983969861768912 | -1.1258805595962003 |
| 450.46 | 449.81767655065505 | 0.6423234493449286 | 0.1451010950402414 |
| 446.53 | 449.46273191454395 | -2.9327319145439787 | -0.6625051797404924 |
| 446.34 | 450.23276134284185 | -3.8927613428418795 | -0.8793761681170623 |
| 449.72 | 448.3011628860887 | 1.4188371139113087 | 0.3205158072965067 |
| 447.14 | 446.65132022669536 | 0.4886797733046251 | 0.11039293412506312 |
| 455.5 | 459.6412858596406 | -4.141285859640618 | -0.935517944613967 |
| 448.22 | 449.8084139802319 | -1.5884139802318487 | -0.35882328154752013 |
| 447.84 | 450.0492245621943 | -2.209224562194322 | -0.4990646122154142 |
| 447.58 | 450.1534891767275 | -2.5734891767274917 | -0.5813521178437366 |
| 447.06 | 447.2352893702614 | -0.17528937026139602 | -3.959793091748769e-2 |
| 447.69 | 452.1889854808296 | -4.498985480829617 | -1.016322415917227 |
| 462.01 | 457.2763644769643 | 4.733635523035673 | 1.069329943682345 |
| 448.92 | 449.22543586447597 | -0.30543586447595317 | -6.899807012373943e-2 |
| 442.82 | 446.9636998742384 | -4.1436998742383935 | -0.9360632713678595 |
| 453.46 | 456.1194901357003 | -2.6594901357003096 | -0.600779764980383 |
| 446.2 | 450.6991034809647 | -4.49910348096472 | -1.0163490721896018 |
| 443.77 | 447.0742816761689 | -3.3042816761688982 | -0.7464384026808953 |
| 448.9 | 447.93156732156075 | 0.9684326784392283 | 0.21876928556414296 |
| 460.6 | 459.53962876300176 | 1.0603712369982645 | 0.23953823855339262 |
| 444.44 | 450.41539217977487 | -5.975392179774872 | -1.349843212892932 |
| 445.95 | 450.2091683575852 | -4.259168357585224 | -0.9621476427127764 |
| 453.18 | 450.5270199129538 | 2.652980087046217 | 0.599309142680298 |
| 443.46 | 446.7475520504839 | -3.287552050483896 | -0.7426591743046266 |
| 444.64 | 444.5071996193093 | 0.13280038069069633 | 2.999965310254963e-2 |
| 465.57 | 459.3265106832487 | 6.2434893167512655 | 1.4104064512304106 |
| 446.41 | 447.6568115900025 | -1.246811590002494 | -0.281655180427759 |
| 450.39 | 454.7627521673882 | -4.372752167388228 | -0.9878062656356458 |
| 452.38 | 454.2185133216816 | -1.838513321681603 | -0.4153208114916822 |
| 445.66 | 445.39798853968557 | 0.2620114603144543 | 5.918848182094669e-2 |
| 444.31 | 446.5197598700026 | -2.2097598700025856 | -0.4991855384391755 |
| 450.95 | 449.2875712413884 | 1.662428758611611 | 0.37554324623667645 |
| 442.02 | 446.61697690688305 | -4.5969769068830715 | -1.0384587138204282 |
| 453.88 | 452.35263519535 | 1.5273648046500057 | 0.34503225112933517 |
| 454.15 | 453.8829146493028 | 0.26708535069718664 | 6.033467545812348e-2 |
| 455.22 | 449.0737291884585 | 6.146270811541513 | 1.388444756419958 |
| 446.44 | 446.2148995417375 | 0.2251004582624887 | 5.085027336501233e-2 |
| 449.6 | 453.8663667211949 | -4.266366721194856 | -0.9637737556054642 |
| 457.89 | 451.0124137711604 | 6.8775862288395615 | 1.553649168586412 |
| 446.02 | 448.5883814092364 | -2.5683814092363946 | -0.5801982713557641 |
| 445.4 | 449.5097304398748 | -4.109730439874795 | -0.9283895640961064 |
| 449.74 | 448.5033053489824 | 1.2366946510176149 | 0.2793697603225793 |
| 448.31 | 446.9172206057899 | 1.3927793942100948 | 0.3146293591732849 |
| 458.63 | 453.8324720669487 | 4.797527933051299 | 1.0837632617676214 |
| 449.93 | 453.30606496401464 | -3.376064964014631 | -0.7626542728669009 |
| 452.39 | 449.08008122232087 | 3.3099187776791155 | 0.7477118259115428 |
| 443.29 | 445.36190546480196 | -2.0719054648019437 | -0.4680441794071581 |
| 446.22 | 449.83432112958235 | -3.614321129582322 | -0.8164764251785102 |
| 439.0 | 444.60209183702824 | -5.60209183702824 | -1.2655145330562003 |
| 452.75 | 455.3330390953461 | -2.5830390953461233 | -0.5835094478470579 |
| 445.65 | 450.28095549994475 | -4.6309554999447755 | -1.046134490045332 |
| 445.27 | 449.3942290809264 | -4.124229080926398 | -0.9316648122523561 |
| 451.95 | 448.76188428428145 | 3.1881157157185385 | 0.7201964710108949 |
| 445.02 | 449.49526226001734 | -4.4752622600173595 | -1.0109633319210711 |
| 450.74 | 449.4914112072868 | 1.2485887927132353 | 0.2820566511504923 |
| 443.93 | 449.5171242611325 | -5.587124261132487 | -1.262133352352325 |
| 445.91 | 447.2640843894497 | -1.3540843894496675 | -0.30588814387272023 |
| 445.71 | 451.75573664689705 | -6.045736646897069 | -1.3657340529671387 |
| 452.7 | 449.47769197848703 | 3.2223080215129585 | 0.7279205250179235 |
| 449.38 | 448.9807967933042 | 0.3992032066958018 | 9.01801459906393e-2 |
| 445.09 | 452.83851864090616 | -7.748518640906184 | -1.7503931093934726 |
| 450.22 | 448.99070865761826 | 1.2292913423817708 | 0.27769735027577597 |
| 441.41 | 448.9314689751323 | -7.521468975132279 | -1.6991025093602443 |
| 462.19 | 458.19122302999057 | 3.9987769700094304 | 0.9033251358981357 |
| 446.17 | 449.6590005617065 | -3.4890005617064617 | -0.7881664644439343 |
| 444.19 | 446.545237358833 | -2.3552373588329942 | -0.532048954767078 |
| 453.15 | 456.3129527756159 | -3.1629527756159064 | -0.7145121539163726 |
| 451.49 | 449.9268730104538 | 1.5631269895462196 | 0.35311094138229787 |
| 453.38 | 448.0350183689646 | 5.3449816310354095 | 1.2074332463249917 |
| 452.1 | 453.06361431502756 | -0.9636143150275416 | -0.21768081556036123 |
| 446.33 | 449.51211271813366 | -3.1821127181336806 | -0.718840391727202 |
| 452.46 | 450.672947544889 | 1.7870524551109952 | 0.40369578348012486 |
| 444.87 | 447.9552056621943 | -3.085205662194312 | -0.6969490534174008 |
| 444.04 | 448.2666586698033 | -4.226658669803271 | -0.9548036927115915 |
| 449.12 | 448.66968221055265 | 0.4503177894473538 | 0.10172694836464449 |
| 443.15 | 445.7146703190735 | -2.564670319073514 | -0.5793599347716393 |
| 446.84 | 452.8021698612517 | -5.962169861251709 | -1.3468562864485707 |
| 443.93 | 446.7443660008083 | -2.8143660008083202 | -0.6357662778430159 |
| 455.18 | 448.49796091182566 | 6.682039088174349 | 1.5094750001492365 |
| 451.88 | 449.90863388073797 | 1.9713661192620293 | 0.44533230558821424 |
| 458.47 | 453.53620348431275 | 4.933796515687277 | 1.1145463829197875 |
| 449.26 | 451.60241101975805 | -2.3424110197580603 | -0.5291514802205262 |
| 444.16 | 448.28015134415546 | -4.120151344155431 | -0.9307436500694146 |
| 441.05 | 445.940139097113 | -4.890139097112979 | -1.1046841565784926 |
| 447.88 | 452.5181226448485 | -4.638122644848522 | -1.047753550621287 |
| 444.91 | 449.080854354919 | -4.1708543549189585 | -0.9421974781853397 |
| 439.01 | 443.764677908941 | -4.754677908941005 | -1.0740834261221295 |
| 453.17 | 449.2796285666682 | 3.890371433331836 | 0.8788362867110385 |
-- Now we can display the RMSE as a Histogram. Clearly this shows that the RMSE is centered around 0 with the vast majority of the error within 2 RMSEs.
SELECT Within_RSME from Power_Plant_RMSE_Evaluation
| Within_RSME |
|---|
| -0.6545286058914855 |
| -1.4178948518800556 |
| 0.17524239493619823 |
| 0.44163480044197995 |
| 0.26132139752130357 |
| 0.28066570579802425 |
| 1.3625651590092023 |
| 0.9078489886839033 |
| -0.3442308494884213 |
| -0.11630232674577932 |
| 1.7506729821805804 |
| -8.284953745386567e-2 |
| -1.692818871916148 |
| 2.608373132048146e-2 |
| 0.8783285919200484 |
| 1.8262340682999108 |
| 0.9515196517846441 |
| 0.9951418870719423 |
| -0.12833292050229034 |
| 1.8547757064958097 |
| -6.520499829010114e-2 |
| 1.7540228045303743 |
| 1.712912449331917 |
| -0.567063215206105 |
| 1.758470293691663 |
| 0.36292285373571487 |
| 1.3722994239368362 |
| -0.4518791902667791 |
| -0.711637096481805 |
| 0.7477551488923485 |
| -9.075206274161249e-2 |
| 0.33498359634397323 |
| -2.717087121436152 |
| 1.6347795140090344 |
| 0.5413939250993844 |
| 1.8721087453250556 |
| 1.2897763086344445 |
| -0.6298581141929721 |
| -0.3869554869711247 |
| 1.5590952476122018 |
| -5.164688385271067e-2 |
| 1.021860350507925 |
| 4.2073297187840204e-2 |
| 3.6816563372465104e-2 |
| 2.4273181887690556 |
| 0.2760063132279849 |
| -0.2711988922825122 |
| -0.49565448805243767 |
| 1.6046669568831393 |
| -0.5946690382578993 |
| 1.1099602752189677 |
| 0.42249584921569994 |
| 0.3776264153858503 |
| 1.1253464244922151 |
| 2.313691606156222 |
| 0.15702246269194528 |
| 1.1180526463224945 |
| 1.0326707046913566 |
| -0.5009264197587914 |
| 2.1055253303633577 |
| 0.883079580425271 |
| 0.8562239455498787 |
| 1.0581479782654901 |
| -0.5881175213603873 |
| -1.0469362575856187 |
| 0.9845051707078616 |
| -0.32033221960491765 |
| -6.11464435454739e-2 |
| 1.7966491781271794 |
| 1.449961394951398 |
| 0.1429791665816065 |
| 0.17796926331974147 |
| 0.590178010121793 |
| -0.4427473424095181 |
| -1.4586722394340677 |
| -0.14061563348410486 |
| -0.4778797517789611 |
| 0.16287170617772706 |
| 1.6205038004183012 |
| 0.6655668633335223 |
| 0.9552928474721311 |
| -0.9255614318409298 |
| -0.20841065925050425 |
| -0.5044470235035043 |
| -0.9064055675522111 |
| -0.5331117277702286 |
| 1.0604764411029242 |
| 0.6163555912803304 |
| 1.502475041125319 |
| 0.11025852973791857 |
| 1.027322682875218e-2 |
| 1.2068500286116204 |
| 0.7807519242835766 |
| 0.5378958494817917 |
| 1.4921557049542535 |
| 0.8423417649127792 |
| -0.8074228949228237 |
| -1.273287251952606 |
| -0.8366248339128552 |
| 0.5703862219885535 |
| 0.9915076732202934 |
| 1.392578067734949 |
| -1.1161712376708273 |
| -0.5177873225895746 |
| 1.2543530104022107 |
| 0.8332547944829863 |
| -0.15024202389261102 |
| -0.7461833621634392 |
| -2.6333071255094267 |
| 1.7090186657480726 |
| 1.17171695524459 |
| 0.16538389150158914 |
| -0.6043651708916123 |
| 1.5140936302699408 |
| -0.40248969630754267 |
| -0.5274788489010923 |
| 1.673819828943716 |
| 0.3998536629456538 |
| 1.5247431144210337 |
| 0.681669465709775 |
| -0.5067930534728822 |
| 1.3329317905059794 |
| 0.2815190232236669 |
| 0.9594271583094607 |
| 0.7195804749163808 |
| 1.563628994525865 |
| -0.5164095221527084 |
| 0.5408652916763936 |
| 1.2169907361146226 |
| 1.3691416206809235 |
| 0.9990582667496749 |
| 0.2866573009496584 |
| -0.1322508701375881 |
| -0.11350226422907182 |
| 1.5478953375265612 |
| 0.6261411303745675 |
| -0.3570008639436786 |
| 0.11097880108736301 |
| -1.997789295120947e-2 |
| 0.8015184563019332 |
| 1.0694995653480546 |
| -0.44658623651141477 |
| 1.5261372030315674 |
| 9.637974736911713e-2 |
| -0.8289854400119429 |
| 1.6226588690300885 |
| 1.2686705940133918 |
| -0.17369127241360752 |
| 1.465503060651426 |
| -0.8493901094223798 |
| -3.19863982083975e-3 |
| 0.775250166241454 |
| -1.476480677907779 |
| 1.6164395333609067 |
| -0.7292074491181413 |
| -2.0719934236347326 |
| 6.689462100697262e-2 |
| 0.41987991622217147 |
| 1.5698655082870474 |
| 0.9108140262190783 |
| 1.4283829990671366 |
| -0.7468033961054397 |
| 1.8565224554443056e-2 |
| -0.631158789082455 |
| -2.423658794064658e-2 |
| 0.1268789228414677 |
| 1.2467256943415417 |
| 0.9532625827884375 |
| 1.858427070746631 |
| 3.080438049074982 |
| -0.7935508099728905 |
| -0.33001241533337206 |
| 1.5352270267793358 |
| -0.8014312542128782 |
| 1.0796607821279804 |
| 0.8682110816429235 |
| 0.8502714264690703 |
| 0.271857163937664 |
| 1.4407225588170043 |
| -7.658599891256687e-2 |
| 0.4048751439362759 |
| -1.6508290646045036 |
| 0.9392995195813987 |
| 0.3512225476325008 |
| 1.3510289270963245e-2 |
| 1.214207255223955 |
| 1.357197397311141 |
| 0.7924624787403274 |
| 0.9747529486084029 |
| -0.2189330666129607 |
| 1.1957383378087658 |
| -0.33808517274788225 |
| 0.12841983400410018 |
| 2.6901995797195863e-2 |
| 0.6811317532938489 |
| -0.14977990817360218 |
| 0.3021002532054435 |
| 6.309664277182285e-2 |
| -1.4280877316330394 |
| -0.31036099175789383 |
| 0.23847593115995214 |
| -1.2544261801023373 |
| 1.487015909020143 |
| 1.4770822031817277e-2 |
| 1.5464079765310332 |
| -0.9661142653632034 |
| 0.12365480382094843 |
| 0.2213694799459989 |
| -0.371717945334288 |
| -7.16964482466269e-2 |
| -1.3370460279083884 |
| -0.4791979479015218 |
| 0.7258131397464832 |
| -0.9603785228015735 |
| 1.761740374853143 |
| 0.756705284425051 |
| -7.97943379405968e-2 |
| -0.12401745329336308 |
| 0.6066524918978817 |
| -1.827662052785967 |
| -1.591900543337891 |
| -2.2076662292962226 |
| -0.6113138148859982 |
| -0.6998705392764937 |
| -0.950596077665842 |
| 0.8530061355541199 |
| -0.3389719928622738 |
| 1.166670943264595 |
| -0.793497343451686 |
| -0.17702361157448407 |
| -1.2193083364383357 |
| -2.0572343953484604 |
| -0.3306196669058454 |
| 0.30773883474602903 |
| -0.23209777066088685 |
| 1.4407294606630792 |
| -0.25326355704561365 |
| -4.4359271514481574e-2 |
| 0.6878454023126037 |
| -1.1071350441930146 |
| 1.1936865467290878 |
| -0.3007124612846165 |
| 1.4096765924648265 |
| 0.6164656679054759 |
| 0.9857137966698644 |
| 1.0741485680349747 |
| -0.5956816180527323 |
| 0.42955444320288233 |
| 0.3977787059312943 |
| -0.21498609418317555 |
| 0.9605949185920998 |
| -0.9629884170069595 |
| 1.0232242351169787 |
| -0.5846231192914554 |
| 1.3665650089641064 |
| 1.746749569478921 |
| 0.20876984487810568 |
| 1.2521345827220822 |
| 0.5034033523179731 |
| 0.2428222722587704 |
| -0.8164837978146291 |
| 0.8816626861116392 |
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| -2.1123605476138505 |
| 0.6755552672777567 |
| -0.7768750359376887 |
| 7.361579398740335e-2 |
| -1.1767524912633618 |
| -0.5872458115946685 |
| -0.15834020050780284 |
| 0.682572359347037 |
| 0.25945073138280605 |
| 0.18198122034572886 |
| 0.8813451011277746 |
| 9.289521984221884e-2 |
| -0.8224646596460314 |
| -1.0992548944297087 |
| 0.24767764457942207 |
| -1.6296349827369583 |
| 0.8839992806447705 |
| -0.20826509006315633 |
| -1.1308211710304394 |
| -0.888034323093527 |
| 1.375146556884749 |
| -0.12272814800411408 |
| 0.4708084873186104 |
| -0.4634347195545461 |
| 0.3440522256895429 |
| 0.4367497353673552 |
| -0.2795279825620374 |
| 0.7701118542900588 |
| -4.2075245099282746e-2 |
| -0.10357117246458049 |
| 1.80757939097979e-2 |
| 0.16185831370575537 |
| 0.5535246598913276 |
| -0.4376793572220653 |
| 0.2960230050324334 |
| -0.501790599000596 |
| -0.4316894674828857 |
| 0.24271962137276715 |
| -0.6012591933934375 |
| 0.499528227037766 |
| -0.227724741199252 |
| -1.5266390839325845 |
| -0.5833859951647822 |
| 0.44092803915740614 |
| 1.3593919171619417 |
| 0.637089242037123 |
| 0.712636779143946 |
| -1.1258805595962003 |
| 0.1451010950402414 |
| -0.6625051797404924 |
| -0.8793761681170623 |
| 0.3205158072965067 |
| 0.11039293412506312 |
| -0.935517944613967 |
| -0.35882328154752013 |
| -0.4990646122154142 |
| -0.5813521178437366 |
| -3.959793091748769e-2 |
| -1.016322415917227 |
| 1.069329943682345 |
| -6.899807012373943e-2 |
| -0.9360632713678595 |
| -0.600779764980383 |
| -1.0163490721896018 |
| -0.7464384026808953 |
| 0.21876928556414296 |
| 0.23953823855339262 |
| -1.349843212892932 |
| -0.9621476427127764 |
| 0.599309142680298 |
| -0.7426591743046266 |
| 2.999965310254963e-2 |
| 1.4104064512304106 |
| -0.281655180427759 |
| -0.9878062656356458 |
| -0.4153208114916822 |
| 5.918848182094669e-2 |
| -0.4991855384391755 |
| 0.37554324623667645 |
| -1.0384587138204282 |
| 0.34503225112933517 |
| 6.033467545812348e-2 |
| 1.388444756419958 |
| 5.085027336501233e-2 |
| -0.9637737556054642 |
| 1.553649168586412 |
| -0.5801982713557641 |
| -0.9283895640961064 |
| 0.2793697603225793 |
| 0.3146293591732849 |
| 1.0837632617676214 |
| -0.7626542728669009 |
| 0.7477118259115428 |
| -0.4680441794071581 |
| -0.8164764251785102 |
| -1.2655145330562003 |
| -0.5835094478470579 |
| -1.046134490045332 |
| -0.9316648122523561 |
| 0.7201964710108949 |
| -1.0109633319210711 |
| 0.2820566511504923 |
| -1.262133352352325 |
| -0.30588814387272023 |
| -1.3657340529671387 |
| 0.7279205250179235 |
| 9.01801459906393e-2 |
| -1.7503931093934726 |
| 0.27769735027577597 |
| -1.6991025093602443 |
| 0.9033251358981357 |
| -0.7881664644439343 |
| -0.532048954767078 |
| -0.7145121539163726 |
| 0.35311094138229787 |
| 1.2074332463249917 |
| -0.21768081556036123 |
| -0.718840391727202 |
| 0.40369578348012486 |
| -0.6969490534174008 |
| -0.9548036927115915 |
| 0.10172694836464449 |
| -0.5793599347716393 |
| -1.3468562864485707 |
| -0.6357662778430159 |
| 1.5094750001492365 |
| 0.44533230558821424 |
| 1.1145463829197875 |
| -0.5291514802205262 |
| -0.9307436500694146 |
| -1.1046841565784926 |
| -1.047753550621287 |
| -0.9421974781853397 |
| -1.0740834261221295 |
| 0.8788362867110385 |
We can see this definitively if we count the number of predictions within + or - 1.0 and + or - 2.0 and display this as a pie chart:
SELECT case when Within_RSME <= 1.0 and Within_RSME >= -1.0 then 1 when Within_RSME <= 2.0 and Within_RSME >= -2.0 then 2 else 3 end RSME_Multiple, COUNT(*) count from Power_Plant_RMSE_Evaluation
group by case when Within_RSME <= 1.0 and Within_RSME >= -1.0 then 1 when Within_RSME <= 2.0 and Within_RSME >= -2.0 then 2 else 3 end
| RSME_Multiple | count |
|---|---|
| 1.0 | 1267.0 |
| 3.0 | 77.0 |
| 2.0 | 512.0 |
So we have about 70% of our training data within 1 RMSE and about 97% (70% + 27%) within 2 RMSE. So the model is pretty decent. Let's see if we can tune the model to improve it further.
NOTE: these numbers will vary across runs due to the seed in random sampling of training and test set, number of iterations, and other stopping rules in optimization, for example.
Step 7: Tuning and Evaluation
Now that we have a model with all of the data let's try to make a better model by tuning over several parameters.
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.ml.evaluation._
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.ml.evaluation._
First let's use a cross validator to split the data into training and validation subsets. See http://spark.apache.org/docs/latest/ml-tuning.html.
//Let's set up our evaluator class to judge the model based on the best root mean squared error
val regEval = new RegressionEvaluator()
regEval.setLabelCol("PE")
.setPredictionCol("Predicted_PE")
.setMetricName("rmse")
regEval: org.apache.spark.ml.evaluation.RegressionEvaluator = RegressionEvaluator: uid=regEval_c75f9d66a6cb, metricName=rmse, throughOrigin=false
res101: regEval.type = RegressionEvaluator: uid=regEval_c75f9d66a6cb, metricName=rmse, throughOrigin=false
We now treat the lrPipeline as an Estimator, wrapping it in a CrossValidator instance.
This will allow us to jointly choose parameters for all Pipeline stages.
A CrossValidator requires an Estimator, an Evaluator (which we set next).
//Let's create our crossvalidator with 3 fold cross validation
val crossval = new CrossValidator()
crossval.setEstimator(lrPipeline)
crossval.setNumFolds(3)
crossval.setEvaluator(regEval)
crossval: org.apache.spark.ml.tuning.CrossValidator = cv_bc136566b6a6
res102: crossval.type = cv_bc136566b6a6
A CrossValidator also requires a set of EstimatorParamMaps which we set next.
For this we need a regularization parameter (more generally a hyper-parameter that is model-specific).
Now, let's tune over our regularization parameter from 0.01 to 0.10.
val regParam = ((1 to 10) toArray).map(x => (x /100.0))
warning: there was one feature warning; for details, enable `:setting -feature' or `:replay -feature'
regParam: Array[Double] = Array(0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1)
Check out the scala docs for syntactic details on org.apache.spark.ml.tuning.ParamGridBuilder.
val paramGrid = new ParamGridBuilder()
.addGrid(lr.regParam, regParam)
.build()
crossval.setEstimatorParamMaps(paramGrid)
paramGrid: Array[org.apache.spark.ml.param.ParamMap] =
Array({
linReg_3b8dffe4015f-regParam: 0.01
}, {
linReg_3b8dffe4015f-regParam: 0.02
}, {
linReg_3b8dffe4015f-regParam: 0.03
}, {
linReg_3b8dffe4015f-regParam: 0.04
}, {
linReg_3b8dffe4015f-regParam: 0.05
}, {
linReg_3b8dffe4015f-regParam: 0.06
}, {
linReg_3b8dffe4015f-regParam: 0.07
}, {
linReg_3b8dffe4015f-regParam: 0.08
}, {
linReg_3b8dffe4015f-regParam: 0.09
}, {
linReg_3b8dffe4015f-regParam: 0.1
})
res103: crossval.type = cv_bc136566b6a6
//Now let's create our model
val cvModel = crossval.fit(trainingSet)
cvModel: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_07136143d91d, numFolds=3
In addition to CrossValidator Spark also offers TrainValidationSplit for hyper-parameter tuning. TrainValidationSplit only evaluates each combination of parameters once as opposed to k times in case of CrossValidator. It is therefore less expensive, but will not produce as reliable results when the training dataset is not sufficiently large.
Now that we have tuned let's see what we got for tuning parameters and what our RMSE was versus our intial model
val predictionsAndLabels = cvModel.transform(testSet)
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").rdd.map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])))
val rmse = metrics.rootMeanSquaredError
val explainedVariance = metrics.explainedVariance
val r2 = metrics.r2
predictionsAndLabels: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 5 more fields]
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@671c9416
rmse: Double = 4.4286032895695895
explainedVariance: Double = 271.5750243564875
r2: Double = 0.9313732865178349
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")
println (f"R2: $r2")
Root Mean Squared Error: 4.4286032895695895
Explained Variance: 271.5750243564875
R2: 0.9313732865178349
Let us explore other models to see if we can predict the power output better
There are several families of models in Spark's scalable machine learning library:
So our initial untuned and tuned linear regression models are statistically identical.
Given that the only linearly correlated variable is Temperature, it makes sense try another machine learning method such a Decision Tree to handle non-linear data and see if we can improve our model
A Decision Tree creates a model based on splitting variables using a tree structure. We will first start with a single decision tree model.
Reference Decision Trees: https://en.wikipedia.org/wiki/Decisiontreelearning
//Let's build a decision tree pipeline
import org.apache.spark.ml.regression.DecisionTreeRegressor
// we are using a Decision Tree Regressor as opposed to a classifier we used for the hand-written digit classification problem
val dt = new DecisionTreeRegressor()
dt.setLabelCol("PE")
dt.setPredictionCol("Predicted_PE")
dt.setFeaturesCol("features")
dt.setMaxBins(100)
val dtPipeline = new Pipeline()
dtPipeline.setStages(Array(vectorizer, dt))
import org.apache.spark.ml.regression.DecisionTreeRegressor
dt: org.apache.spark.ml.regression.DecisionTreeRegressor = dtr_92691f930484
dtPipeline: org.apache.spark.ml.Pipeline = pipeline_ddfeb1cfff04
res106: dtPipeline.type = pipeline_ddfeb1cfff04
//Let's just resuse our CrossValidator
crossval.setEstimator(dtPipeline)
res107: crossval.type = cv_bc136566b6a6
val paramGrid = new ParamGridBuilder()
.addGrid(dt.maxDepth, Array(2, 3))
.build()
paramGrid: Array[org.apache.spark.ml.param.ParamMap] =
Array({
dtr_92691f930484-maxDepth: 2
}, {
dtr_92691f930484-maxDepth: 3
})
crossval.setEstimatorParamMaps(paramGrid)
res108: crossval.type = cv_bc136566b6a6
val dtModel = crossval.fit(trainingSet) // fit decitionTree with cv
dtModel: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_ddfeb1cfff04, numFolds=3
import org.apache.spark.ml.regression.DecisionTreeRegressionModel
import org.apache.spark.ml.PipelineModel
dtModel.bestModel.asInstanceOf[PipelineModel].stages.last.asInstanceOf[DecisionTreeRegressionModel].toDebugString
import org.apache.spark.ml.regression.DecisionTreeRegressionModel
import org.apache.spark.ml.PipelineModel
res109: String =
"DecisionTreeRegressionModel: uid=dtr_92691f930484, depth=3, numNodes=15, numFeatures=4
If (feature 0 <= 18.595)
If (feature 0 <= 11.885000000000002)
If (feature 0 <= 8.575)
Predict: 483.994943457189
Else (feature 0 > 8.575)
Predict: 476.04374262101527
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 15.475000000000001)
Predict: 467.5564649956784
Else (feature 0 > 15.475000000000001)
Predict: 459.5010376134889
Else (feature 0 > 18.595)
If (feature 1 <= 66.21000000000001)
If (feature 0 <= 22.055)
Predict: 452.01076923076914
Else (feature 0 > 22.055)
Predict: 443.46937926330156
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 25.325)
Predict: 440.73731707317074
Else (feature 0 > 25.325)
Predict: 433.86131215469624
"
The line above will pull the Decision Tree model from the Pipeline and display it as an if-then-else string.
Next let's visualize it as a decision tree for regression.
display(dtModel.bestModel.asInstanceOf[PipelineModel].stages.last.asInstanceOf[DecisionTreeRegressionModel])
| treeNode |
|---|
| {"index":7,"featureType":"continuous","prediction":null,"threshold":18.595,"categories":null,"feature":0,"overflow":false} |
| {"index":3,"featureType":"continuous","prediction":null,"threshold":11.885000000000002,"categories":null,"feature":0,"overflow":false} |
| {"index":1,"featureType":"continuous","prediction":null,"threshold":8.575,"categories":null,"feature":0,"overflow":false} |
| {"index":0,"featureType":null,"prediction":483.994943457189,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":2,"featureType":null,"prediction":476.04374262101527,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":5,"featureType":"continuous","prediction":null,"threshold":15.475000000000001,"categories":null,"feature":0,"overflow":false} |
| {"index":4,"featureType":null,"prediction":467.5564649956784,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":6,"featureType":null,"prediction":459.5010376134889,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":11,"featureType":"continuous","prediction":null,"threshold":66.21000000000001,"categories":null,"feature":1,"overflow":false} |
| {"index":9,"featureType":"continuous","prediction":null,"threshold":22.055,"categories":null,"feature":0,"overflow":false} |
| {"index":8,"featureType":null,"prediction":452.01076923076914,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":10,"featureType":null,"prediction":443.46937926330156,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":13,"featureType":"continuous","prediction":null,"threshold":25.325,"categories":null,"feature":0,"overflow":false} |
| {"index":12,"featureType":null,"prediction":440.73731707317074,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":14,"featureType":null,"prediction":433.86131215469624,"threshold":null,"categories":null,"feature":null,"overflow":false} |
Now let's see how our DecisionTree model compares to our LinearRegression model
val predictionsAndLabels = dtModel.bestModel.transform(testSet)
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])).rdd)
val rmse = metrics.rootMeanSquaredError
val explainedVariance = metrics.explainedVariance
val r2 = metrics.r2
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")
println (f"R2: $r2")
Root Mean Squared Error: 5.111944542729953
Explained Variance: 261.4355220034205
R2: 0.908560906504635
predictionsAndLabels: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 5 more fields]
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@684c4c50
rmse: Double = 5.111944542729953
explainedVariance: Double = 261.4355220034205
r2: Double = 0.908560906504635
So our DecisionTree was slightly worse than our LinearRegression model (LR: 4.6 vs DT: 5.2). Maybe we can try an Ensemble method such as Gradient-Boosted Decision Trees to see if we can strengthen our model by using an ensemble of weaker trees with weighting to reduce the error in our model.
*Note since this is a complex model, the cell below can take about *16 minutes* or so to run on a small cluster with a couple nodes with about 6GB RAM, go out and grab a coffee and come back :-).*
This GBTRegressor code will be way faster on a larger cluster of course.
A visual explanation of gradient boosted trees:
Let's see what a boosting algorithm, a type of ensemble method, is all about in more detail.
import org.apache.spark.ml.regression.GBTRegressor
val gbt = new GBTRegressor()
gbt.setLabelCol("PE")
gbt.setPredictionCol("Predicted_PE")
gbt.setFeaturesCol("features")
gbt.setSeed(100088121L)
gbt.setMaxBins(100)
gbt.setMaxIter(120)
val gbtPipeline = new Pipeline()
gbtPipeline.setStages(Array(vectorizer, gbt))
//Let's just resuse our CrossValidator
crossval.setEstimator(gbtPipeline)
val paramGrid = new ParamGridBuilder()
.addGrid(gbt.maxDepth, Array(2, 3))
.build()
crossval.setEstimatorParamMaps(paramGrid)
//gbt.explainParams
val gbtModel = crossval.fit(trainingSet)
import org.apache.spark.ml.regression.GBTRegressor
gbt: org.apache.spark.ml.regression.GBTRegressor = gbtr_0a275b363831
gbtPipeline: org.apache.spark.ml.Pipeline = pipeline_8b2722444cff
paramGrid: Array[org.apache.spark.ml.param.ParamMap] =
Array({
gbtr_0a275b363831-maxDepth: 2
}, {
gbtr_0a275b363831-maxDepth: 3
})
gbtModel: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_8b2722444cff, numFolds=3
import org.apache.spark.ml.regression.GBTRegressionModel
val predictionsAndLabels = gbtModel.bestModel.transform(testSet)
val metrics = new RegressionMetrics(predictionsAndLabels.select("Predicted_PE", "PE").map(r => (r(0).asInstanceOf[Double], r(1).asInstanceOf[Double])).rdd)
val rmse = metrics.rootMeanSquaredError
val explainedVariance = metrics.explainedVariance
val r2 = metrics.r2
println (f"Root Mean Squared Error: $rmse")
println (f"Explained Variance: $explainedVariance")
println (f"R2: $r2")
Root Mean Squared Error: 3.7034735091301307
Explained Variance: 272.208177448154
R2: 0.9520069744321176
import org.apache.spark.ml.regression.GBTRegressionModel
predictionsAndLabels: org.apache.spark.sql.DataFrame = [AT: double, V: double ... 5 more fields]
metrics: org.apache.spark.mllib.evaluation.RegressionMetrics = org.apache.spark.mllib.evaluation.RegressionMetrics@79d7946a
rmse: Double = 3.7034735091301307
explainedVariance: Double = 272.208177448154
r2: Double = 0.9520069744321176
We can use the toDebugString method to dump out what our trees and weighting look like:
gbtModel.bestModel.asInstanceOf[PipelineModel].stages.last.asInstanceOf[GBTRegressionModel].toDebugString
res116: String =
"GBTRegressionModel: uid=gbtr_0a275b363831, numTrees=120, numFeatures=4
Tree 0 (weight 1.0):
If (feature 0 <= 18.595)
If (feature 0 <= 11.885000000000002)
If (feature 0 <= 8.575)
Predict: 483.994943457189
Else (feature 0 > 8.575)
Predict: 476.04374262101527
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 15.475000000000001)
Predict: 467.5564649956784
Else (feature 0 > 15.475000000000001)
Predict: 459.5010376134889
Else (feature 0 > 18.595)
If (feature 1 <= 66.21000000000001)
If (feature 0 <= 22.055)
Predict: 452.01076923076914
Else (feature 0 > 22.055)
Predict: 443.46937926330156
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 25.325)
Predict: 440.73731707317074
Else (feature 0 > 25.325)
Predict: 433.86131215469624
Tree 1 (weight 0.1):
If (feature 3 <= 82.55000000000001)
If (feature 0 <= 26.505000000000003)
If (feature 3 <= 64.11500000000001)
Predict: 6.095232469256877
Else (feature 3 > 64.11500000000001)
Predict: 1.4337156041793564
Else (feature 0 > 26.505000000000003)
If (feature 1 <= 65.55000000000001)
Predict: -7.6527692217902885
Else (feature 1 > 65.55000000000001)
Predict: -0.5844786594493007
Else (feature 3 > 82.55000000000001)
If (feature 1 <= 45.754999999999995)
If (feature 3 <= 93.075)
Predict: 0.6108402960070604
Else (feature 3 > 93.075)
Predict: -3.913147108789527
Else (feature 1 > 45.754999999999995)
If (feature 0 <= 18.595)
Predict: -9.443118256805649
Else (feature 0 > 18.595)
Predict: -3.9650066253122347
Tree 2 (weight 0.1):
If (feature 1 <= 45.754999999999995)
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 14.285)
Predict: 1.1090321642566017
Else (feature 0 > 14.285)
Predict: -3.898383406004269
Else (feature 0 > 15.475000000000001)
If (feature 0 <= 18.595)
Predict: 4.874047316205584
Else (feature 0 > 18.595)
Predict: 10.653531335032094
Else (feature 1 > 45.754999999999995)
If (feature 0 <= 18.595)
If (feature 1 <= 58.19)
Predict: -5.0005796708960615
Else (feature 1 > 58.19)
Predict: -13.749053154552547
Else (feature 0 > 18.595)
If (feature 2 <= 1009.295)
Predict: -3.1410617295379555
Else (feature 2 > 1009.295)
Predict: 0.8001319329379373
Tree 3 (weight 0.1):
If (feature 3 <= 85.52000000000001)
If (feature 1 <= 55.825)
If (feature 0 <= 26.505000000000003)
Predict: 2.695149105254423
Else (feature 0 > 26.505000000000003)
Predict: -6.7564705067621675
Else (feature 1 > 55.825)
If (feature 1 <= 66.21000000000001)
Predict: -2.282355645191304
Else (feature 1 > 66.21000000000001)
Predict: 0.5508297910243797
Else (feature 3 > 85.52000000000001)
If (feature 1 <= 40.629999999999995)
If (feature 2 <= 1022.8399999999999)
Predict: 2.236331037122985
Else (feature 2 > 1022.8399999999999)
Predict: -7.220151471077029
Else (feature 1 > 40.629999999999995)
If (feature 3 <= 89.595)
Predict: -2.175085332960937
Else (feature 3 > 89.595)
Predict: -4.54715329410012
Tree 4 (weight 0.1):
If (feature 2 <= 1010.335)
If (feature 1 <= 43.005)
If (feature 0 <= 15.475000000000001)
Predict: -0.004818717215773318
Else (feature 0 > 15.475000000000001)
Predict: 5.355639866401019
Else (feature 1 > 43.005)
If (feature 1 <= 65.15)
Predict: -5.132340755716604
Else (feature 1 > 65.15)
Predict: -0.7228608840382099
Else (feature 2 > 1010.335)
If (feature 3 <= 74.525)
If (feature 0 <= 29.455)
Predict: 3.0313483965828176
Else (feature 0 > 29.455)
Predict: -2.560863167947768
Else (feature 3 > 74.525)
If (feature 1 <= 40.629999999999995)
Predict: 2.228142113797423
Else (feature 1 > 40.629999999999995)
Predict: -1.6052323952407273
Tree 5 (weight 0.1):
If (feature 3 <= 81.045)
If (feature 0 <= 27.585)
If (feature 3 <= 46.92)
Predict: 8.005444802948366
Else (feature 3 > 46.92)
Predict: 1.301219291279602
Else (feature 0 > 27.585)
If (feature 1 <= 65.55000000000001)
Predict: -6.568175152421089
Else (feature 1 > 65.55000000000001)
Predict: -0.8331220915230161
Else (feature 3 > 81.045)
If (feature 1 <= 41.519999999999996)
If (feature 2 <= 1019.835)
Predict: 1.7054425589091675
Else (feature 2 > 1019.835)
Predict: -2.6266728146418195
Else (feature 1 > 41.519999999999996)
If (feature 1 <= 41.805)
Predict: -11.130585327952137
Else (feature 1 > 41.805)
Predict: -2.111742422947989
Tree 6 (weight 0.1):
If (feature 2 <= 1009.625)
If (feature 1 <= 43.005)
If (feature 0 <= 15.475000000000001)
Predict: -0.4497329015715072
Else (feature 0 > 15.475000000000001)
Predict: 3.661440765591046
Else (feature 1 > 43.005)
If (feature 1 <= 66.85499999999999)
Predict: -3.861085082339984
Else (feature 1 > 66.85499999999999)
Predict: -0.7492674320362704
Else (feature 2 > 1009.625)
If (feature 3 <= 69.32499999999999)
If (feature 0 <= 29.455)
Predict: 2.621042446270476
Else (feature 0 > 29.455)
Predict: -1.4554021183385886
Else (feature 3 > 69.32499999999999)
If (feature 1 <= 58.19)
Predict: 0.42605173456002876
Else (feature 1 > 58.19)
Predict: -1.9554878629891888
Tree 7 (weight 0.1):
If (feature 3 <= 86.625)
If (feature 1 <= 52.795)
If (feature 0 <= 18.595)
Predict: 0.4988124937300424
Else (feature 0 > 18.595)
Predict: 4.447321094438702
Else (feature 1 > 52.795)
If (feature 1 <= 66.21000000000001)
Predict: -1.721618872277871
Else (feature 1 > 66.21000000000001)
Predict: 0.6365209437219329
Else (feature 3 > 86.625)
If (feature 0 <= 6.895)
If (feature 3 <= 87.32499999999999)
Predict: -10.224737687790185
Else (feature 3 > 87.32499999999999)
Predict: 3.712660431036073
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
Predict: -7.105091794629204
Else (feature 0 > 8.575)
Predict: -1.5060749360589718
Tree 8 (weight 0.1):
If (feature 2 <= 1008.745)
If (feature 1 <= 41.665)
If (feature 0 <= 15.475000000000001)
Predict: 0.11492519898093705
Else (feature 0 > 15.475000000000001)
Predict: 3.532699993937119
Else (feature 1 > 41.665)
If (feature 1 <= 66.85499999999999)
Predict: -3.366654434213031
Else (feature 1 > 66.85499999999999)
Predict: -0.9165259817521934
Else (feature 2 > 1008.745)
If (feature 3 <= 82.955)
If (feature 1 <= 51.905)
Predict: 1.841638760642105
Else (feature 1 > 51.905)
Predict: 0.33265178659776373
Else (feature 3 > 82.955)
If (feature 3 <= 95.68)
Predict: -0.4850135884105616
Else (feature 3 > 95.68)
Predict: -3.812639024859598
Tree 9 (weight 0.1):
If (feature 0 <= 29.455)
If (feature 3 <= 61.89)
If (feature 1 <= 71.18)
Predict: 1.9301726185107941
Else (feature 1 > 71.18)
Predict: 8.938327477606107
Else (feature 3 > 61.89)
If (feature 0 <= 5.8149999999999995)
Predict: 4.428862111265314
Else (feature 0 > 5.8149999999999995)
Predict: -0.3308347482908286
Else (feature 0 > 29.455)
If (feature 1 <= 68.28999999999999)
If (feature 2 <= 1009.875)
Predict: -8.941194411728706
Else (feature 2 > 1009.875)
Predict: -1.8834474815204216
Else (feature 1 > 68.28999999999999)
If (feature 2 <= 1014.405)
Predict: -0.5760094442636617
Else (feature 2 > 1014.405)
Predict: -5.50575933697771
Tree 10 (weight 0.1):
If (feature 2 <= 1005.345)
If (feature 0 <= 27.355)
If (feature 3 <= 99.3)
Predict: -0.5544332197918127
Else (feature 3 > 99.3)
Predict: -6.049841121821514
Else (feature 0 > 27.355)
If (feature 1 <= 70.815)
Predict: -8.03479585317622
Else (feature 1 > 70.815)
Predict: 0.0619269945107018
Else (feature 2 > 1005.345)
If (feature 3 <= 72.41499999999999)
If (feature 0 <= 24.755000000000003)
Predict: 2.020104454165069
Else (feature 0 > 24.755000000000003)
Predict: -0.22964512858748945
Else (feature 3 > 72.41499999999999)
If (feature 1 <= 40.7)
Predict: 1.2987210163143512
Else (feature 1 > 40.7)
Predict: -0.8347660227231797
Tree 11 (weight 0.1):
If (feature 0 <= 5.0600000000000005)
If (feature 1 <= 42.3)
If (feature 1 <= 39.765)
Predict: 4.261094449524424
Else (feature 1 > 39.765)
Predict: 9.140536590115639
Else (feature 1 > 42.3)
If (feature 2 <= 1007.835)
Predict: 0.9746298984436711
Else (feature 2 > 1007.835)
Predict: -6.317973546286112
Else (feature 0 > 5.0600000000000005)
If (feature 3 <= 95.68)
If (feature 2 <= 1009.295)
Predict: -0.8626626541525325
Else (feature 2 > 1009.295)
Predict: 0.3906915507169364
Else (feature 3 > 95.68)
If (feature 0 <= 11.885000000000002)
Predict: -6.002679638413293
Else (feature 0 > 11.885000000000002)
Predict: -0.7509189525586508
Tree 12 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 1 <= 51.905)
If (feature 0 <= 13.695)
Predict: 0.5958576966099496
Else (feature 0 > 13.695)
Predict: -1.2694566117501789
Else (feature 1 > 51.905)
If (feature 1 <= 58.19)
Predict: -4.248373100253223
Else (feature 1 > 58.19)
Predict: -9.144729000689791
Else (feature 0 > 18.595)
If (feature 1 <= 47.64)
If (feature 2 <= 1012.675)
Predict: 0.8560563666774771
Else (feature 2 > 1012.675)
Predict: 10.801233083620462
Else (feature 1 > 47.64)
If (feature 0 <= 20.015)
Predict: 3.1978561830060213
Else (feature 0 > 20.015)
Predict: -0.16716555702980626
Tree 13 (weight 0.1):
If (feature 0 <= 28.655)
If (feature 1 <= 66.21000000000001)
If (feature 1 <= 58.19)
Predict: 0.34765099851241454
Else (feature 1 > 58.19)
Predict: -1.8189329025195076
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 22.525)
Predict: 6.490484815153365
Else (feature 0 > 22.525)
Predict: 0.7933381751314565
Else (feature 0 > 28.655)
If (feature 1 <= 68.28999999999999)
If (feature 2 <= 1005.665)
Predict: -8.922459399148678
Else (feature 2 > 1005.665)
Predict: -2.3989441108132668
Else (feature 1 > 68.28999999999999)
If (feature 2 <= 1018.215)
Predict: -0.3023338438804105
Else (feature 2 > 1018.215)
Predict: 14.559949112833806
Tree 14 (weight 0.1):
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 13.695)
If (feature 0 <= 11.885000000000002)
Predict: -0.8897676968273979
Else (feature 0 > 11.885000000000002)
Predict: 3.5874467728768487
Else (feature 0 > 13.695)
If (feature 1 <= 46.345)
Predict: -3.2690852418064273
Else (feature 1 > 46.345)
Predict: -8.844433418899179
Else (feature 0 > 15.475000000000001)
If (feature 1 <= 43.175)
If (feature 1 <= 41.805)
Predict: 3.298855216614116
Else (feature 1 > 41.805)
Predict: 9.150659154207167
Else (feature 1 > 43.175)
If (feature 2 <= 1012.495)
Predict: -0.7109832273625656
Else (feature 2 > 1012.495)
Predict: 1.1631179843236674
Tree 15 (weight 0.1):
If (feature 3 <= 90.055)
If (feature 0 <= 30.41)
If (feature 1 <= 66.21000000000001)
Predict: -0.01732155577258642
Else (feature 1 > 66.21000000000001)
Predict: 1.3432966941310502
Else (feature 0 > 30.41)
If (feature 1 <= 69.465)
Predict: -3.6657811330207344
Else (feature 1 > 69.465)
Predict: -0.38803561342243853
Else (feature 3 > 90.055)
If (feature 2 <= 1015.725)
If (feature 1 <= 47.64)
Predict: 1.5063080039677825
Else (feature 1 > 47.64)
Predict: -1.860635777865782
Else (feature 2 > 1015.725)
If (feature 1 <= 40.7)
Predict: 0.21921885078759318
Else (feature 1 > 40.7)
Predict: -4.793242614118129
Tree 16 (weight 0.1):
If (feature 0 <= 6.895)
If (feature 3 <= 67.42500000000001)
If (feature 1 <= 39.620000000000005)
Predict: -1.8477082180199003
Else (feature 1 > 39.620000000000005)
Predict: 16.387935166882745
Else (feature 3 > 67.42500000000001)
If (feature 0 <= 5.0600000000000005)
Predict: 4.152127693563297
Else (feature 0 > 5.0600000000000005)
Predict: 0.7556214745509566
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
If (feature 2 <= 1022.8399999999999)
Predict: -2.9436476590152036
Else (feature 2 > 1022.8399999999999)
Predict: -8.032234733606465
Else (feature 0 > 8.575)
If (feature 0 <= 9.325)
Predict: 5.082738415532321
Else (feature 0 > 9.325)
Predict: -0.032287806549855816
Tree 17 (weight 0.1):
If (feature 3 <= 61.89)
If (feature 0 <= 28.655)
If (feature 0 <= 11.885000000000002)
Predict: -4.455566502931916
Else (feature 0 > 11.885000000000002)
Predict: 1.9027943448616742
Else (feature 0 > 28.655)
If (feature 1 <= 66.21000000000001)
Predict: -3.1810487244675034
Else (feature 1 > 66.21000000000001)
Predict: 0.11525363261816625
Else (feature 3 > 61.89)
If (feature 1 <= 43.175)
If (feature 0 <= 18.595)
Predict: 0.22262511130066664
Else (feature 0 > 18.595)
Predict: 10.13639446335034
Else (feature 1 > 43.175)
If (feature 0 <= 22.055)
Predict: -1.4775024201129299
Else (feature 0 > 22.055)
Predict: 0.20591189539330298
Tree 18 (weight 0.1):
If (feature 2 <= 1005.345)
If (feature 1 <= 70.815)
If (feature 0 <= 23.564999999999998)
Predict: -0.178777362918765
Else (feature 0 > 23.564999999999998)
Predict: -4.52113806132068
Else (feature 1 > 70.815)
If (feature 3 <= 60.025000000000006)
Predict: 4.247605743793766
Else (feature 3 > 60.025000000000006)
Predict: -0.26865379510738685
Else (feature 2 > 1005.345)
If (feature 1 <= 66.21000000000001)
If (feature 1 <= 55.825)
Predict: 0.3773109266288433
Else (feature 1 > 55.825)
Predict: -1.3659380946007862
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 21.335)
Predict: 8.448170645066464
Else (feature 0 > 21.335)
Predict: 0.5048679905700459
Tree 19 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 1 <= 51.905)
If (feature 0 <= 17.655)
Predict: -0.003054757854134698
Else (feature 0 > 17.655)
Predict: -2.9290357116654935
Else (feature 1 > 51.905)
If (feature 1 <= 66.525)
Predict: -4.061825439604536
Else (feature 1 > 66.525)
Predict: -11.67844591879286
Else (feature 0 > 18.595)
If (feature 0 <= 23.945)
If (feature 3 <= 74.525)
Predict: 3.807909998319029
Else (feature 3 > 74.525)
Predict: 0.008459259251505081
Else (feature 0 > 23.945)
If (feature 1 <= 74.915)
Predict: -0.6429811796826012
Else (feature 1 > 74.915)
Predict: 2.099670946504916
Tree 20 (weight 0.1):
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 14.285)
If (feature 0 <= 11.885000000000002)
Predict: -0.7234702732306101
Else (feature 0 > 11.885000000000002)
Predict: 1.589299673192479
Else (feature 0 > 14.285)
If (feature 2 <= 1016.495)
Predict: -2.4381805412578545
Else (feature 2 > 1016.495)
Predict: -6.544687605774689
Else (feature 0 > 15.475000000000001)
If (feature 1 <= 43.005)
If (feature 2 <= 1008.405)
Predict: 0.8119248626873221
Else (feature 2 > 1008.405)
Predict: 5.506769369239865
Else (feature 1 > 43.005)
If (feature 2 <= 1012.935)
Predict: -0.5028175384892806
Else (feature 2 > 1012.935)
Predict: 1.0290531238165197
Tree 21 (weight 0.1):
If (feature 0 <= 6.895)
If (feature 3 <= 67.42500000000001)
If (feature 1 <= 39.620000000000005)
Predict: -0.9752219334588972
Else (feature 1 > 39.620000000000005)
Predict: 13.369575512848845
Else (feature 3 > 67.42500000000001)
If (feature 1 <= 42.705)
Predict: 1.850492181024754
Else (feature 1 > 42.705)
Predict: -2.033710059657357
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
If (feature 2 <= 1022.8399999999999)
Predict: -2.256120115332435
Else (feature 2 > 1022.8399999999999)
Predict: -6.380948299395909
Else (feature 0 > 8.575)
If (feature 0 <= 9.594999999999999)
Predict: 3.4448565562285904
Else (feature 0 > 9.594999999999999)
Predict: -0.05858832726578875
Tree 22 (weight 0.1):
If (feature 0 <= 22.055)
If (feature 0 <= 21.095)
If (feature 1 <= 66.21000000000001)
Predict: -0.17987193554240402
Else (feature 1 > 66.21000000000001)
Predict: 6.044335675519052
Else (feature 0 > 21.095)
If (feature 1 <= 66.21000000000001)
Predict: -5.809637597188877
Else (feature 1 > 66.21000000000001)
Predict: 2.7761443044302214
Else (feature 0 > 22.055)
If (feature 0 <= 23.564999999999998)
If (feature 1 <= 64.74000000000001)
Predict: 5.951769778696803
Else (feature 1 > 64.74000000000001)
Predict: -0.30197045172071896
Else (feature 0 > 23.564999999999998)
If (feature 1 <= 44.519999999999996)
Predict: -7.283292958317056
Else (feature 1 > 44.519999999999996)
Predict: -0.19387573131258415
Tree 23 (weight 0.1):
If (feature 3 <= 53.025000000000006)
If (feature 0 <= 24.945)
If (feature 0 <= 18.29)
Predict: 1.0381040338364682
Else (feature 0 > 18.29)
Predict: 5.164469183375362
Else (feature 0 > 24.945)
If (feature 1 <= 66.21000000000001)
Predict: -1.0707595335847206
Else (feature 1 > 66.21000000000001)
Predict: 0.8108674466901084
Else (feature 3 > 53.025000000000006)
If (feature 1 <= 73.725)
If (feature 1 <= 71.505)
Predict: -0.0525992725981239
Else (feature 1 > 71.505)
Predict: -2.807221561152817
Else (feature 1 > 73.725)
If (feature 2 <= 1017.295)
Predict: 1.037788102485777
Else (feature 2 > 1017.295)
Predict: 7.287181806639116
Tree 24 (weight 0.1):
If (feature 2 <= 1004.505)
If (feature 1 <= 70.815)
If (feature 1 <= 59.144999999999996)
Predict: -0.3743851611431973
Else (feature 1 > 59.144999999999996)
Predict: -4.648812647772385
Else (feature 1 > 70.815)
If (feature 0 <= 27.884999999999998)
Predict: 2.4545579195950995
Else (feature 0 > 27.884999999999998)
Predict: -0.32777974793969294
Else (feature 2 > 1004.505)
If (feature 0 <= 22.055)
If (feature 0 <= 20.795)
Predict: 0.045218490740101897
Else (feature 0 > 20.795)
Predict: -3.3620385127109333
Else (feature 0 > 22.055)
If (feature 0 <= 22.955)
Predict: 4.485324626081587
Else (feature 0 > 22.955)
Predict: 0.13166549369330485
Tree 25 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 1 <= 58.19)
If (feature 0 <= 17.655)
Predict: -0.023636417468335308
Else (feature 0 > 17.655)
Predict: -2.607492744918568
Else (feature 1 > 58.19)
If (feature 1 <= 66.525)
Predict: -4.8994505786627665
Else (feature 1 > 66.525)
Predict: -10.07085237281708
Else (feature 0 > 18.595)
If (feature 0 <= 19.725)
If (feature 1 <= 66.21000000000001)
Predict: 2.2716539497960406
Else (feature 1 > 66.21000000000001)
Predict: 13.879740983230867
Else (feature 0 > 19.725)
If (feature 1 <= 43.005)
Predict: 5.616411926544602
Else (feature 1 > 43.005)
Predict: -0.00532316539213451
Tree 26 (weight 0.1):
If (feature 3 <= 93.075)
If (feature 1 <= 55.825)
If (feature 0 <= 22.055)
Predict: 0.15441544139943786
Else (feature 0 > 22.055)
Predict: 2.7626508552722306
Else (feature 1 > 55.825)
If (feature 1 <= 66.21000000000001)
Predict: -1.2060290652635515
Else (feature 1 > 66.21000000000001)
Predict: 0.4827535356971057
Else (feature 3 > 93.075)
If (feature 2 <= 1015.725)
If (feature 0 <= 7.34)
Predict: 4.241421869391143
Else (feature 0 > 7.34)
Predict: -0.704916847045625
Else (feature 2 > 1015.725)
If (feature 0 <= 11.885000000000002)
Predict: -5.4904249010577795
Else (feature 0 > 11.885000000000002)
Predict: 0.0976872424106726
Tree 27 (weight 0.1):
If (feature 2 <= 1008.745)
If (feature 1 <= 41.665)
If (feature 1 <= 39.765)
Predict: -1.0511879426704696
Else (feature 1 > 39.765)
Predict: 1.894883627758776
Else (feature 1 > 41.665)
If (feature 1 <= 41.805)
Predict: -13.576686832482961
Else (feature 1 > 41.805)
Predict: -0.7168167899342832
Else (feature 2 > 1008.745)
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 13.695)
Predict: 0.303140697564446
Else (feature 0 > 13.695)
Predict: -2.8034862119109945
Else (feature 0 > 15.475000000000001)
If (feature 1 <= 55.825)
Predict: 2.0736158373993576
Else (feature 1 > 55.825)
Predict: -0.03336709818500243
Tree 28 (weight 0.1):
If (feature 2 <= 1001.785)
If (feature 3 <= 69.86500000000001)
If (feature 1 <= 69.465)
Predict: -7.299995136853619
Else (feature 1 > 69.465)
Predict: 0.23757420536270835
Else (feature 3 > 69.86500000000001)
If (feature 0 <= 7.34)
Predict: 3.4313360513286284
Else (feature 0 > 7.34)
Predict: -1.276734468456009
Else (feature 2 > 1001.785)
If (feature 0 <= 11.885000000000002)
If (feature 0 <= 11.335)
Predict: 0.04499264982858887
Else (feature 0 > 11.335)
Predict: -6.156735713959919
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 12.715)
Predict: 4.4864210547378915
Else (feature 0 > 12.715)
Predict: 0.030721690290287165
Tree 29 (weight 0.1):
If (feature 1 <= 66.21000000000001)
If (feature 0 <= 26.505000000000003)
If (feature 0 <= 22.055)
Predict: -0.3694472776628706
Else (feature 0 > 22.055)
Predict: 1.5208925945902059
Else (feature 0 > 26.505000000000003)
If (feature 1 <= 43.510000000000005)
Predict: -17.99975152056241
Else (feature 1 > 43.510000000000005)
Predict: -2.4033598663496183
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 22.055)
If (feature 0 <= 18.595)
Predict: -7.600012529438
Else (feature 0 > 18.595)
Predict: 6.469471961408998
Else (feature 0 > 22.055)
If (feature 0 <= 25.325)
Predict: -2.6540186758683166
Else (feature 0 > 25.325)
Predict: 0.9869775581610103
Tree 30 (weight 0.1):
If (feature 0 <= 5.0600000000000005)
If (feature 1 <= 42.3)
If (feature 3 <= 95.68)
Predict: 2.6307256050737506
Else (feature 3 > 95.68)
Predict: 7.168878406232186
Else (feature 1 > 42.3)
If (feature 2 <= 1007.835)
Predict: -0.1884983669158752
Else (feature 2 > 1007.835)
Predict: -5.920088632100639
Else (feature 0 > 5.0600000000000005)
If (feature 1 <= 37.815)
If (feature 0 <= 11.885000000000002)
Predict: -3.8096010917307517
Else (feature 0 > 11.885000000000002)
Predict: 3.5943708074284917
Else (feature 1 > 37.815)
If (feature 1 <= 41.519999999999996)
Predict: 0.6752927073561888
Else (feature 1 > 41.519999999999996)
Predict: -0.14342050966250913
Tree 31 (weight 0.1):
If (feature 3 <= 99.3)
If (feature 0 <= 31.155)
If (feature 3 <= 46.92)
Predict: 2.011051499336945
Else (feature 3 > 46.92)
Predict: 0.012791339921714258
Else (feature 0 > 31.155)
If (feature 2 <= 1014.405)
Predict: -0.916397796921109
Else (feature 2 > 1014.405)
Predict: -6.832504867736499
Else (feature 3 > 99.3)
If (feature 1 <= 39.765)
If (feature 1 <= 38.41)
Predict: -5.505405887296888
Else (feature 1 > 38.41)
Predict: -16.748187635942333
Else (feature 1 > 39.765)
If (feature 0 <= 16.04)
Predict: 0.7708563952983728
Else (feature 0 > 16.04)
Predict: -3.909609799859729
Tree 32 (weight 0.1):
If (feature 1 <= 66.21000000000001)
If (feature 1 <= 64.74000000000001)
If (feature 2 <= 1011.515)
Predict: -1.0002623040885374
Else (feature 2 > 1011.515)
Predict: 0.3591184679910596
Else (feature 1 > 64.74000000000001)
If (feature 2 <= 1006.775)
Predict: -13.788703659238209
Else (feature 2 > 1006.775)
Predict: -2.4620285364725656
Else (feature 1 > 66.21000000000001)
If (feature 1 <= 69.09)
If (feature 0 <= 22.525)
Predict: 6.088217885246919
Else (feature 0 > 22.525)
Predict: 1.0364537580784474
Else (feature 1 > 69.09)
If (feature 0 <= 25.325)
Predict: -2.7946704533493856
Else (feature 0 > 25.325)
Predict: 0.6607665941219878
Tree 33 (weight 0.1):
If (feature 2 <= 1028.14)
If (feature 0 <= 29.455)
If (feature 1 <= 66.21000000000001)
Predict: -0.08158010506593574
Else (feature 1 > 66.21000000000001)
Predict: 0.8815313937000332
Else (feature 0 > 29.455)
If (feature 1 <= 44.519999999999996)
Predict: -12.581939252812768
Else (feature 1 > 44.519999999999996)
Predict: -0.77008278158028
Else (feature 2 > 1028.14)
If (feature 0 <= 8.575)
If (feature 1 <= 39.45)
Predict: 0.6766771966451415
Else (feature 1 > 39.45)
Predict: -8.445008186341592
Else (feature 0 > 8.575)
If (feature 0 <= 8.96)
Predict: 3.777292703648982
Else (feature 0 > 8.96)
Predict: -2.398045803854934
Tree 34 (weight 0.1):
If (feature 0 <= 6.895)
If (feature 3 <= 67.42500000000001)
If (feature 1 <= 39.620000000000005)
Predict: -0.6804590872791323
Else (feature 1 > 39.620000000000005)
Predict: 10.518093452986212
Else (feature 3 > 67.42500000000001)
If (feature 1 <= 42.705)
Predict: 1.3098801739379287
Else (feature 1 > 42.705)
Predict: -1.7450883352732074
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
If (feature 0 <= 7.765)
Predict: -0.7816329288490581
Else (feature 0 > 7.765)
Predict: -3.7319393815256547
Else (feature 0 > 8.575)
If (feature 0 <= 9.965)
Predict: 2.5600814387681337
Else (feature 0 > 9.965)
Predict: -0.06944896856946733
Tree 35 (weight 0.1):
If (feature 3 <= 61.89)
If (feature 1 <= 39.34)
If (feature 0 <= 16.695)
Predict: -4.078381955056753
Else (feature 0 > 16.695)
Predict: -16.616821684050763
Else (feature 1 > 39.34)
If (feature 1 <= 40.82)
Predict: 4.4394084324272045
Else (feature 1 > 40.82)
Predict: 0.4663514281701948
Else (feature 3 > 61.89)
If (feature 1 <= 58.19)
If (feature 0 <= 22.055)
Predict: -0.027831559557693095
Else (feature 0 > 22.055)
Predict: 2.492136574233702
Else (feature 1 > 58.19)
If (feature 0 <= 18.595)
Predict: -4.183073089901298
Else (feature 0 > 18.595)
Predict: -0.40866909936948326
Tree 36 (weight 0.1):
If (feature 2 <= 1004.505)
If (feature 1 <= 38.754999999999995)
If (feature 3 <= 77.195)
Predict: 9.579430817769946
Else (feature 3 > 77.195)
Predict: 2.6305173165509195
Else (feature 1 > 38.754999999999995)
If (feature 1 <= 70.065)
Predict: -1.801035005150415
Else (feature 1 > 70.065)
Predict: 0.4054557218074593
Else (feature 2 > 1004.505)
If (feature 2 <= 1017.475)
If (feature 1 <= 43.175)
Predict: 1.09341093192285
Else (feature 1 > 43.175)
Predict: -0.03229585395374685
Else (feature 2 > 1017.475)
If (feature 2 <= 1019.365)
Predict: -1.7719467091167656
Else (feature 2 > 1019.365)
Predict: 0.2537098831339789
Tree 37 (weight 0.1):
If (feature 0 <= 15.475000000000001)
If (feature 0 <= 13.695)
If (feature 0 <= 11.885000000000002)
Predict: -0.5104239863875834
Else (feature 0 > 11.885000000000002)
Predict: 2.1202163252308432
Else (feature 0 > 13.695)
If (feature 3 <= 78.38499999999999)
Predict: -3.379040627299855
Else (feature 3 > 78.38499999999999)
Predict: -0.9911440360990582
Else (feature 0 > 15.475000000000001)
If (feature 0 <= 16.395)
If (feature 3 <= 67.42500000000001)
Predict: 0.4887402058080506
Else (feature 3 > 67.42500000000001)
Predict: 3.884289185845989
Else (feature 0 > 16.395)
If (feature 2 <= 1020.565)
Predict: -0.02862374251288089
Else (feature 2 > 1020.565)
Predict: 3.15734851002617
Tree 38 (weight 0.1):
If (feature 3 <= 43.385)
If (feature 0 <= 24.945)
If (feature 2 <= 1014.405)
Predict: 1.603183026814036
Else (feature 2 > 1014.405)
Predict: 7.05673098720603
Else (feature 0 > 24.945)
If (feature 0 <= 26.295)
Predict: -5.886157652325024
Else (feature 0 > 26.295)
Predict: 0.7387886738447553
Else (feature 3 > 43.385)
If (feature 1 <= 73.725)
If (feature 1 <= 71.505)
Predict: 0.008754170391068655
Else (feature 1 > 71.505)
Predict: -2.0178119354056765
Else (feature 1 > 73.725)
If (feature 1 <= 77.42)
Predict: 1.8396223170900134
Else (feature 1 > 77.42)
Predict: -1.5747478023010713
Tree 39 (weight 0.1):
If (feature 0 <= 22.055)
If (feature 0 <= 20.795)
If (feature 0 <= 18.595)
Predict: -0.25201916420658693
Else (feature 0 > 18.595)
Predict: 1.5718496558845116
Else (feature 0 > 20.795)
If (feature 1 <= 66.21000000000001)
Predict: -3.7201562729508804
Else (feature 1 > 66.21000000000001)
Predict: 2.087916157719636
Else (feature 0 > 22.055)
If (feature 0 <= 23.564999999999998)
If (feature 2 <= 1012.015)
Predict: 0.7371264627237751
Else (feature 2 > 1012.015)
Predict: 4.6381746481309065
Else (feature 0 > 23.564999999999998)
If (feature 1 <= 44.519999999999996)
Predict: -5.772062593218147
Else (feature 1 > 44.519999999999996)
Predict: -0.12977023255758624
Tree 40 (weight 0.1):
If (feature 0 <= 5.0600000000000005)
If (feature 3 <= 95.68)
If (feature 2 <= 1011.105)
Predict: -3.365769010003795
Else (feature 2 > 1011.105)
Predict: 2.955050770650336
Else (feature 3 > 95.68)
If (feature 2 <= 1008.205)
Predict: 2.728200788704399
Else (feature 2 > 1008.205)
Predict: 6.919382935391042
Else (feature 0 > 5.0600000000000005)
If (feature 3 <= 96.61)
If (feature 1 <= 37.815)
Predict: -1.3949762323292278
Else (feature 1 > 37.815)
Predict: 0.06411431759031856
Else (feature 3 > 96.61)
If (feature 0 <= 11.605)
Predict: -3.491961189446582
Else (feature 0 > 11.605)
Predict: -0.16406940197018208
Tree 41 (weight 0.1):
If (feature 0 <= 30.41)
If (feature 1 <= 66.21000000000001)
If (feature 1 <= 58.85)
Predict: 0.15540001609212736
Else (feature 1 > 58.85)
Predict: -1.1352706510871309
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 25.325)
Predict: -0.6390388689881783
Else (feature 0 > 25.325)
Predict: 1.3909471658872326
Else (feature 0 > 30.41)
If (feature 2 <= 1014.405)
If (feature 1 <= 56.894999999999996)
Predict: -9.841212730443857
Else (feature 1 > 56.894999999999996)
Predict: -0.5054201979116707
Else (feature 2 > 1014.405)
If (feature 1 <= 74.915)
Predict: -5.898616989217175
Else (feature 1 > 74.915)
Predict: 1.042314191558674
Tree 42 (weight 0.1):
If (feature 2 <= 1009.625)
If (feature 1 <= 43.005)
If (feature 1 <= 42.025000000000006)
Predict: -0.05077288158998095
Else (feature 1 > 42.025000000000006)
Predict: 3.619244461816007
Else (feature 1 > 43.005)
If (feature 0 <= 18.595)
Predict: -3.5057172058309307
Else (feature 0 > 18.595)
Predict: -0.32537979322253646
Else (feature 2 > 1009.625)
If (feature 2 <= 1017.475)
If (feature 1 <= 55.825)
Predict: 0.8076710222538108
Else (feature 1 > 55.825)
Predict: -0.01784249647067053
Else (feature 2 > 1017.475)
If (feature 2 <= 1019.365)
Predict: -1.4312272708033218
Else (feature 2 > 1019.365)
Predict: 0.20844195286029432
Tree 43 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 0 <= 17.655)
If (feature 0 <= 15.475000000000001)
Predict: -0.2913115404147692
Else (feature 0 > 15.475000000000001)
Predict: 1.1145997107947745
Else (feature 0 > 17.655)
If (feature 1 <= 37.815)
Predict: 14.538914074443028
Else (feature 1 > 37.815)
Predict: -3.0407663665320683
Else (feature 0 > 18.595)
If (feature 2 <= 1020.325)
If (feature 1 <= 43.175)
Predict: 3.881117973012625
Else (feature 1 > 43.175)
Predict: 0.04538590552170286
Else (feature 2 > 1020.325)
If (feature 1 <= 64.74000000000001)
Predict: 4.662857871344832
Else (feature 1 > 64.74000000000001)
Predict: -43.5190245896606
Tree 44 (weight 0.1):
If (feature 0 <= 6.895)
If (feature 3 <= 67.42500000000001)
If (feature 1 <= 39.620000000000005)
Predict: -0.7153417257252007
Else (feature 1 > 39.620000000000005)
Predict: 8.417772324738223
Else (feature 3 > 67.42500000000001)
If (feature 2 <= 1010.985)
Predict: 2.5111312207852263
Else (feature 2 > 1010.985)
Predict: 0.29950092637128345
Else (feature 0 > 6.895)
If (feature 0 <= 8.575)
If (feature 1 <= 40.06)
Predict: -3.30672576579827
Else (feature 1 > 40.06)
Predict: -0.45097750406116727
Else (feature 0 > 8.575)
If (feature 0 <= 9.325)
Predict: 3.226884779601992
Else (feature 0 > 9.325)
Predict: -0.041218673012550146
Tree 45 (weight 0.1):
If (feature 2 <= 1028.14)
If (feature 0 <= 10.395)
If (feature 3 <= 92.22999999999999)
Predict: 1.0355113706204075
Else (feature 3 > 92.22999999999999)
Predict: -1.4603765116034264
Else (feature 0 > 10.395)
If (feature 0 <= 11.885000000000002)
Predict: -2.630098257038007
Else (feature 0 > 11.885000000000002)
Predict: 0.09582680902226459
Else (feature 2 > 1028.14)
If (feature 0 <= 8.575)
If (feature 1 <= 39.45)
Predict: 1.107359449029309
Else (feature 1 > 39.45)
Predict: -6.096185488604492
Else (feature 0 > 8.575)
If (feature 3 <= 70.57499999999999)
Predict: -3.516006231223605
Else (feature 3 > 70.57499999999999)
Predict: -0.26144006873351155
Tree 46 (weight 0.1):
If (feature 0 <= 27.884999999999998)
If (feature 1 <= 66.21000000000001)
If (feature 0 <= 25.145)
Predict: 0.06412914679730906
Else (feature 0 > 25.145)
Predict: -1.554041618425865
Else (feature 1 > 66.21000000000001)
If (feature 0 <= 25.325)
Predict: -0.4748636523867588
Else (feature 0 > 25.325)
Predict: 2.4595627925276826
Else (feature 0 > 27.884999999999998)
If (feature 3 <= 61.89)
If (feature 1 <= 71.895)
Predict: -0.4429995884239883
Else (feature 1 > 71.895)
Predict: 1.4321627506429122
Else (feature 3 > 61.89)
If (feature 1 <= 71.505)
Predict: -0.2617373838209719
Else (feature 1 > 71.505)
Predict: -2.637373068367584
Tree 47 (weight 0.1):
If (feature 3 <= 61.89)
If (feature 1 <= 39.34)
If (feature 0 <= 16.695)
Predict: -2.855821258258964
Else (feature 0 > 16.695)
Predict: -13.80709248590955
Else (feature 1 > 39.34)
If (feature 1 <= 74.915)
Predict: 0.35443616318343385
Else (feature 1 > 74.915)
Predict: 2.9400682899293233
Else (feature 3 > 61.89)
If (feature 1 <= 71.505)
If (feature 1 <= 66.85499999999999)
Predict: -0.142107366817925
Else (feature 1 > 66.85499999999999)
Predict: 1.0352328327059535
Else (feature 1 > 71.505)
If (feature 1 <= 73.00999999999999)
Predict: -2.947898208728739
Else (feature 1 > 73.00999999999999)
Predict: -0.0830706492691125
Tree 48 (weight 0.1):
If (feature 2 <= 1004.505)
If (feature 1 <= 38.754999999999995)
If (feature 3 <= 86.625)
Predict: 4.921331281961159
Else (feature 3 > 86.625)
Predict: -5.961237317667383
Else (feature 1 > 38.754999999999995)
If (feature 1 <= 39.765)
Predict: -4.436072018762161
Else (feature 1 > 39.765)
Predict: -0.7283906418193187
Else (feature 2 > 1004.505)
If (feature 0 <= 22.055)
If (feature 0 <= 20.795)
Predict: 0.044899959208437666
Else (feature 0 > 20.795)
Predict: -2.2295677836603995
Else (feature 0 > 22.055)
If (feature 0 <= 23.564999999999998)
Predict: 2.3559950404053414
Else (feature 0 > 23.564999999999998)
Predict: -0.06950420285239005
Tree 49 (weight 0.1):
If (feature 1 <= 41.435)
If (feature 0 <= 11.885000000000002)
If (feature 0 <= 11.335)
Predict: -0.02697483446966382
Else (feature 0 > 11.335)
Predict: -3.7031660865730367
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 13.265)
Predict: 4.209724879257512
Else (feature 0 > 13.265)
Predict: 0.41602586770224464
Else (feature 1 > 41.435)
If (feature 1 <= 41.805)
If (feature 3 <= 89.595)
Predict: -1.6099299736449546
Else (feature 3 > 89.595)
Predict: -9.419799062932288
Else (feature 1 > 41.805)
If (feature 1 <= 43.175)
Predict: 1.5951066240527068
Else (feature 1 > 43.175)
Predict: -0.11481901338423915
Tree 50 (weight 0.1):
If (feature 1 <= 38.41)
If (feature 0 <= 11.885000000000002)
If (feature 2 <= 1016.665)
Predict: -5.344526613859499
Else (feature 2 > 1016.665)
Predict: 0.058134490545794976
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 16.985)
Predict: 1.537246393097642
Else (feature 0 > 16.985)
Predict: 9.920778277565365
Else (feature 1 > 38.41)
If (feature 0 <= 13.485)
If (feature 1 <= 51.045)
Predict: 0.6675749827847329
Else (feature 1 > 51.045)
Predict: -5.737026170963195
Else (feature 0 > 13.485)
If (feature 0 <= 15.475000000000001)
Predict: -1.7210713764803844
Else (feature 0 > 15.475000000000001)
Predict: 0.09027853735147576
Tree 51 (weight 0.1):
If (feature 0 <= 31.155)
If (feature 3 <= 48.230000000000004)
If (feature 2 <= 1020.325)
Predict: 0.9307527734280435
Else (feature 2 > 1020.325)
Predict: 9.070547343579028
Else (feature 3 > 48.230000000000004)
If (feature 1 <= 73.725)
Predict: -0.06359121142988887
Else (feature 1 > 73.725)
Predict: 1.049949579330099
Else (feature 0 > 31.155)
If (feature 1 <= 63.08)
If (feature 1 <= 44.519999999999996)
Predict: -6.355864033256125
Else (feature 1 > 44.519999999999996)
Predict: 13.6760008529786
Else (feature 1 > 63.08)
If (feature 1 <= 68.28999999999999)
Predict: -3.953494984806905
Else (feature 1 > 68.28999999999999)
Predict: -0.6145841300086397
Tree 52 (weight 0.1):
If (feature 2 <= 1012.495)
If (feature 1 <= 41.435)
If (feature 1 <= 39.975)
Predict: -0.4047468839922722
Else (feature 1 > 39.975)
Predict: 2.509624688414308
Else (feature 1 > 41.435)
If (feature 1 <= 41.805)
Predict: -6.811044477667833
Else (feature 1 > 41.805)
Predict: -0.26889174065489546
Else (feature 2 > 1012.495)
If (feature 3 <= 92.22999999999999)
If (feature 1 <= 58.19)
Predict: 0.7048711166950787
Else (feature 1 > 58.19)
Predict: -0.5475390646726656
Else (feature 3 > 92.22999999999999)
If (feature 1 <= 41.805)
Predict: -3.7013459723577355
Else (feature 1 > 41.805)
Predict: 0.7237930378019226
Tree 53 (weight 0.1):
If (feature 0 <= 18.595)
If (feature 0 <= 17.335)
If (feature 3 <= 74.08500000000001)
Predict: -0.8631764946312587
Else (feature 3 > 74.08500000000001)
Predict: 0.2803856631344212
Else (feature 0 > 17.335)
If (feature 1 <= 42.705)
Predict: 0.9335711174385192
Else (feature 1 > 42.705)
Predict: -2.950020164379197
Else (feature 0 > 18.595)
If (feature 2 <= 1013.395)
If (feature 1 <= 66.85499999999999)
Predict: -0.7231135633124072
Else (feature 1 > 66.85499999999999)
Predict: 0.41724670068145925
Else (feature 2 > 1013.395)
If (feature 1 <= 58.19)
Predict: 4.568024116358373
Else (feature 1 > 58.19)
Predict: -0.36270787813043714
Tree 54 (weight 0.1):
If (feature 2 <= 1001.785)
If (feature 3 <= 63.575)
If (feature 3 <= 60.025000000000006)
Predict: -1.7427137986580719
Else (feature 3 > 60.025000000000006)
Predict: -7.776342039375448
Else (feature 3 > 63.575)
If (feature 0 <= 27.119999999999997)
Predict: 0.026174663094801796
Else (feature 0 > 27.119999999999997)
Predict: -2.343138620856655
Else (feature 2 > 1001.785)
If (feature 0 <= 22.055)
If (feature 1 <= 66.21000000000001)
Predict: -0.27236570115397085
Else (feature 1 > 66.21000000000001)
Predict: 3.164590339421908
Else (feature 0 > 22.055)
If (feature 0 <= 22.725)
Predict: 2.9597120242025485
Else (feature 0 > 22.725)
Predict: 0.054881549113388446
Tree 55 (weight 0.1):
If (feature 2 <= 1028.14)
If (feature 1 <= 45.754999999999995)
If (feature 0 <= 26.715)
Predict: 0.2599176823406179
Else (feature 0 > 26.715)
Predict: -5.549910372715466
Else (feature 1 > 45.754999999999995)
If (feature 0 <= 15.475000000000001)
Predict: -2.867925531108855
Else (feature 0 > 15.475000000000001)
Predict: -0.0236838100703647
Else (feature 2 > 1028.14)
If (feature 0 <= 8.575)
If (feature 1 <= 39.45)
Predict: 0.7777761733424086
Else (feature 1 > 39.45)
Predict: -5.202001886405932
Else (feature 0 > 8.575)
If (feature 3 <= 68.945)
Predict: -3.641368616696809
Else (feature 3 > 68.945)
Predict: -0.6008141057791702
Tree 56 (weight 0.1):
If (feature 3 <= 61.89)
If (feature 0 <= 18.29)
If (feature 2 <= 1019.025)
Predict: -2.1632736300070996
Else (feature 2 > 1019.025)
Predict: 1.2430029950309498
Else (feature 0 > 18.29)
If (feature 1 <= 44.91)
Predict: 3.2134324628571003
Else (feature 1 > 44.91)
Predict: 0.2988556025240279
Else (feature 3 > 61.89)
If (feature 1 <= 71.505)
If (feature 1 <= 66.85499999999999)
Predict: -0.09866504525517336
Else (feature 1 > 66.85499999999999)
Predict: 0.7271499788275563
Else (feature 1 > 71.505)
If (feature 1 <= 73.00999999999999)
Predict: -2.395971400707892
Else (feature 1 > 73.00999999999999)
Predict: -0.16608323426526406
Tree 57 (weight 0.1):
If (feature 2 <= 1017.645)
If (feature 2 <= 1014.615)
If (feature 1 <= 43.175)
Predict: 1.0389282601473917
Else (feature 1 > 43.175)
Predict: -0.3646675630154698
Else (feature 2 > 1014.615)
If (feature 1 <= 43.510000000000005)
Predict: -0.6889797697082773
Else (feature 1 > 43.510000000000005)
Predict: 1.5908362409321974
Else (feature 2 > 1017.645)
If (feature 2 <= 1019.025)
If (feature 1 <= 71.18)
Predict: -1.6569133708803008
Else (feature 1 > 71.18)
Predict: 4.314967971455512
Else (feature 2 > 1019.025)
If (feature 3 <= 89.595)
Predict: 0.43765066032139976
Else (feature 3 > 89.595)
Predict: -1.7344432288008194
Tree 58 (weight 0.1):
If (feature 2 <= 1004.505)
If (feature 1 <= 70.815)
If (feature 1 <= 59.834999999999994)
Predict: -0.170418541124326
Else (feature 1 > 59.834999999999994)
Predict: -2.895038840312831
Else (feature 1 > 70.815)
If (feature 2 <= 1000.505)
Predict: -1.3832637115340176
Else (feature 2 > 1000.505)
Predict: 1.2249185299155396
Else (feature 2 > 1004.505)
If (feature 0 <= 22.055)
If (feature 1 <= 66.21000000000001)
Predict: -0.2418541439735617
Else (feature 1 > 66.21000000000001)
Predict: 3.0110894160107886
Else (feature 0 > 22.055)
If (feature 0 <= 23.945)
Predict: 1.4999960684883906
Else (feature 0 > 23.945)
Predict: -0.042407486119460915
Tree 59 (weight 0.1):
If (feature 1 <= 77.42)
If (feature 1 <= 74.915)
If (feature 1 <= 71.505)
Predict: 0.03983464447255206
Else (feature 1 > 71.505)
Predict: -0.7683021525819648
Else (feature 1 > 74.915)
If (feature 3 <= 72.82)
Predict: 2.774179202497669
Else (feature 3 > 72.82)
Predict: -0.32979787820368633
Else (feature 1 > 77.42)
If (feature 0 <= 21.595)
If (feature 0 <= 21.335)
Predict: -13.565760771414489
Else (feature 0 > 21.335)
Predict: -13.954507897669714
Else (feature 0 > 21.595)
If (feature 0 <= 22.955)
Predict: 10.853700477067264
Else (feature 0 > 22.955)
Predict: -1.4643467280880287
Tree 60 (weight 0.1):
If (feature 0 <= 10.395)
If (feature 1 <= 40.629999999999995)
If (feature 3 <= 75.545)
Predict: -2.3127871072788375
Else (feature 3 > 75.545)
Predict: 0.3992391857664209
Else (feature 1 > 40.629999999999995)
If (feature 3 <= 89.595)
Predict: 2.9152362525803523
Else (feature 3 > 89.595)
Predict: -1.4086879927580456
Else (feature 0 > 10.395)
If (feature 0 <= 11.885000000000002)
If (feature 2 <= 1025.2150000000001)
Predict: -2.2352340341897547
Else (feature 2 > 1025.2150000000001)
Predict: 5.952010328542228
Else (feature 0 > 11.885000000000002)
If (feature 0 <= 12.415)
Predict: 3.4957190546300447
Else (feature 0 > 12.415)
Predict: -0.03992973893781255
Tree 61 (weight 0.1):
If (feature 1 <= 40.82)
If (feature 1 <= 40.224999999999994)
If (feature 3 <= 75.545)
Predict: -1.2944988036912455
Else (feature 3 > 75.545)
Predict: 0.44866615475817667
Else (feature 1 > 40.224999999999994)
If (feature 2 <= 1025.2150000000001)
Predict: 0.7826360308981203
Else (feature 2 > 1025.2150000000001)
Predict: 9.875672812487991
Else (feature 1 > 40.82)
If (feature 2 <= 1025.2150000000001)
If (feature 0 <= 10.945)
Predict: 1.1628421208118285
Else (feature 0 > 10.945)
Predict: -0.12551627485602937
Else (feature 2 > 1025.2150000000001)
If (feature 1 <= 41.15)
Predict: -7.782369290305258
Else (feature 1 > 41.15)
Predict: -1.200273276366743
Tree 62 (weight 0.1):
If (feature 0 <= 22.055)
If (feature 0 <= 21.335)
If (feature 1 <= 66.21000000000001)
Predict: -0.09621281022933233
Else (feature 1 > 66.21000000000001)
Predict: 2.680549669942832
Else (feature 0 > 21.335)
If (feature 1 <= 63.864999999999995)
Predict: -3.207766197974041
Else (feature 1 > 63.864999999999995)
Predict: -0.14162445042909583
Else (feature 0 > 22.055)
If (feature 0 <= 22.725)
If (feature 2 <= 1011.515)
Predict: 0.5064350504779975
Else (feature 2 > 1011.515)
Predict: 3.659612527058891
Else (feature 0 > 22.725)
If (feature 3 <= 75.545)
Predict: 0.2595183211083464
Else (feature 3 > 75.545)
Predict: -0.6564588168227348
Tree 63 (weight 0.1):
If (feature 2 <= 1017.645)
If (feature 1 <= 44.67)
If (feature 1 <= 44.13)
Predict: 0.3869693354075677
Else (feature 1 > 44.13)
Predict: 4.723132901950317
Else (f...
Conclusion
Wow! So our best model is in fact our Gradient Boosted Decision tree model which uses an ensemble of 120 Trees with a depth of 3 to construct a better model than the single decision tree.
Step 8: Deployment will be done later
Now that we have a predictive model it is time to deploy the model into an operational environment.
In our example, let's say we have a series of sensors attached to the power plant and a monitoring station.
The monitoring station will need close to real-time information about how much power that their station will generate so they can relay that to the utility.
For this we need to create a Spark Streaming utility that we can use for this purpose. For this you need to be introduced to basic concepts in Spark Streaming first. See http://spark.apache.org/docs/latest/streaming-programming-guide.html if you can't wait!
After deployment you will be able to use the best predictions from gradient boosed regression trees to feed a real-time dashboard or feed the utility with information on how much power the peaker plant will deliver give current conditions.
Persisting Statistical Machine Learning Models
See https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html
Let's save our best model so we can load it without having to rerun the validation and training again.
gbtModel
res117: org.apache.spark.ml.tuning.CrossValidatorModel = CrossValidatorModel: uid=cv_bc136566b6a6, bestModel=pipeline_8b2722444cff, numFolds=3
gbtModel.bestModel.asInstanceOf[PipelineModel]
.write.overwrite().save("dbfs:///databricks/driver/MyTrainedBestPipelineModel")
When it is time to deploy the trained model to serve predicitons, we cna simply reload this model and proceed as shown later.
Datasource References:
- Pinar Tüfekci, Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods, International Journal of Electrical Power & Energy Systems, Volume 60, September 2014, Pages 126-140, ISSN 0142-0615, Web Link
- Heysem Kaya, Pinar Tüfekci , Sadik Fikret Gürgen: Local and Global Learning Methods for Predicting Power of a Combined Gas & Steam Turbine, Proceedings of the International Conference on Emerging Trends in Computer and Electronics Engineering ICETCEE 2012, pp. 13-18 (Mar. 2012, Dubai) Web Link
Activity Recognition from Accelerometer
- ETL Using SparkSQL Windows
- Prediction using Random Forest
This work is a simpler databricksification of Amira Lakhal's more complex framework for activity recognition:
See Section below on 0. Download and Load Data first.
- Once data is loaded come back here (if you have not downloaded and loaded data into the distributed file system under your hood).
val data = sc.textFile("dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv") // assumes data is loaded
data: org.apache.spark.rdd.RDD[String] = dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv MapPartitionsRDD[1] at textFile at command-561044440962565:1
data.take(5).foreach(println)
"user_id","activity","timeStampAsLong","x","y","z"
"user_001","Jumping",1446047227606,"4.33079","-12.72175","-3.18118"
"user_001","Jumping",1446047227671,"0.575403","-0.727487","2.95007"
"user_001","Jumping",1446047227735,"-1.60885","3.52607","-0.1922"
"user_001","Jumping",1446047227799,"0.690364","-0.037722","1.72382"
val dataDF = sqlContext.read
.format("com.databricks.spark.csv") // use spark.csv package
.option("header", "true") // Use first line of all files as header
.option("inferSchema", "true") // Automatically infer data types
.option("delimiter", ",") // Specify the delimiter as ','
.load("dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv")
dataDF: org.apache.spark.sql.DataFrame = [user_id: string, activity: string ... 4 more fields]
val dataDFnew = spark.read.format("csv")
.option("inferSchema", "true")
.option("header", "true")
.option("sep", ",")
.load("dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv")
dataDFnew: org.apache.spark.sql.DataFrame = [user_id: string, activity: string ... 4 more fields]
dataDFnew.printSchema()
root
|-- user_id: string (nullable = true)
|-- activity: string (nullable = true)
|-- timeStampAsLong: long (nullable = true)
|-- x: double (nullable = true)
|-- y: double (nullable = true)
|-- z: double (nullable = true)
display(dataDF) // zp.show(dataDF)
| user_id | activity | timeStampAsLong | x | y | z |
|---|---|---|---|---|---|
| user_001 | Jumping | 1.446047227606e12 | 4.33079 | -12.72175 | -3.18118 |
| user_001 | Jumping | 1.446047227671e12 | 0.575403 | -0.727487 | 2.95007 |
| user_001 | Jumping | 1.446047227735e12 | -1.60885 | 3.52607 | -0.1922 |
| user_001 | Jumping | 1.446047227799e12 | 0.690364 | -3.7722e-2 | 1.72382 |
| user_001 | Jumping | 1.446047227865e12 | 3.44943 | -1.68549 | 2.29862 |
| user_001 | Jumping | 1.44604722793e12 | 1.87829 | -1.91542 | 0.880768 |
| user_001 | Jumping | 1.446047227995e12 | 1.57173 | -5.86241 | -3.75599 |
| user_001 | Jumping | 1.446047228059e12 | 3.41111 | -17.93331 | 0.535886 |
| user_001 | Jumping | 1.446047228123e12 | 3.18118 | -19.58108 | 5.74745 |
| user_001 | Jumping | 1.446047228189e12 | 7.85626 | -19.2362 | 0.804128 |
| user_001 | Jumping | 1.446047228253e12 | 1.26517 | -8.85139 | 2.18366 |
| user_001 | Jumping | 1.446047228318e12 | 7.7239e-2 | 1.15021 | 1.53221 |
| user_001 | Jumping | 1.446047228383e12 | 0.230521 | 2.0699 | -1.41845 |
| user_001 | Jumping | 1.446047228447e12 | 0.652044 | -0.497565 | 1.76214 |
| user_001 | Jumping | 1.446047228512e12 | 1.53341 | -0.305964 | 1.41725 |
| user_001 | Jumping | 1.446047228578e12 | -1.07237 | -1.95374 | 0.191003 |
| user_001 | Jumping | 1.446047228642e12 | 2.75966 | -13.75639 | 0.191003 |
| user_001 | Jumping | 1.446047228707e12 | 7.43474 | -19.58108 | 2.95007 |
| user_001 | Jumping | 1.446047228771e12 | 5.63368 | -19.58108 | 5.59417 |
| user_001 | Jumping | 1.446047228836e12 | 5.02056 | -11.72542 | -1.38013 |
| user_001 | Jumping | 1.4460472289e12 | -2.10702 | 0.575403 | 1.07237 |
| user_001 | Jumping | 1.446047228966e12 | -1.30229 | 2.2615 | -1.26517 |
| user_001 | Jumping | 1.44604722903e12 | 1.68669 | -0.957409 | 1.57053 |
| user_001 | Jumping | 1.446047229095e12 | 2.60638 | -0.229323 | 2.14534 |
| user_001 | Jumping | 1.446047229159e12 | 1.30349 | -0.152682 | 0.497565 |
| user_001 | Jumping | 1.446047229224e12 | 1.64837 | -8.12331 | 1.60885 |
| user_001 | Jumping | 1.44604722929e12 | 0.15388 | -18.46979 | -1.03525 |
| user_001 | Jumping | 1.446047229354e12 | 4.98224 | -19.58108 | 0.995729 |
| user_001 | Jumping | 1.446047229419e12 | 5.17384 | -19.2362 | 0.612526 |
| user_001 | Jumping | 1.446047229483e12 | 2.03158 | -9.11964 | 1.34061 |
| user_001 | Jumping | 1.446047229549e12 | -1.95374 | -1.03405 | 0.574206 |
| user_001 | Jumping | 1.446047229612e12 | 0.1922 | 1.30349 | -1.03525 |
| user_001 | Jumping | 1.446047229678e12 | 1.64837 | -0.727487 | -0.307161 |
| user_001 | Jumping | 1.446047229743e12 | 2.56806 | -1.03405 | 0.267643 |
| user_001 | Jumping | 1.446047229806e12 | 1.41845 | -0.305964 | 0.727487 |
| user_001 | Jumping | 1.446047229872e12 | 1.03525 | -6.82042 | 0.957409 |
| user_001 | Jumping | 1.446047229936e12 | 3.94759 | -19.31284 | -1.22685 |
| user_001 | Jumping | 1.446047230001e12 | 6.13185 | -19.58108 | 2.72014 |
| user_001 | Jumping | 1.446047230066e12 | 7.89458 | -16.9753 | 0.229323 |
| user_001 | Jumping | 1.446047230131e12 | 2.6447 | -3.75479 | 0.114362 |
| user_001 | Jumping | 1.446047230195e12 | -2.41358 | 1.91661 | -5.99e-4 |
| user_001 | Jumping | 1.44604723026e12 | -1.95374 | -0.114362 | -0.268841 |
| user_001 | Jumping | 1.446047230325e12 | -0.229323 | -1.95374 | 2.75846 |
| user_001 | Jumping | 1.44604723039e12 | 2.68302 | 0.268841 | 0.535886 |
| user_001 | Jumping | 1.446047230455e12 | 1.15021 | -1.41725 | 0.880768 |
| user_001 | Jumping | 1.446047230519e12 | 2.98958 | -11.53382 | -0.920286 |
| user_001 | Jumping | 1.446047230584e12 | 8.00954 | -19.58108 | -0.1922 |
| user_001 | Jumping | 1.446047230649e12 | 7.31978 | -19.58108 | 2.52854 |
| user_001 | Jumping | 1.446047230713e12 | 5.44208 | -13.56479 | -0.498763 |
| user_001 | Jumping | 1.446047230779e12 | 0.728685 | -0.420925 | 1.68549 |
| user_001 | Jumping | 1.446047230842e12 | 1.18853 | 1.41845 | -2.22318 |
| user_001 | Jumping | 1.446047230907e12 | 0.15388 | -1.80046 | 2.03038 |
| user_001 | Jumping | 1.446047230972e12 | -3.7722e-2 | 1.07357 | -0.958607 |
| user_001 | Jumping | 1.446047231038e12 | 0.230521 | 0.728685 | -0.690364 |
| user_001 | Jumping | 1.446047231102e12 | 0.805325 | -1.14901 | 1.53221 |
| user_001 | Jumping | 1.446047231167e12 | 5.17384 | -9.15796 | -2.91294 |
| user_001 | Jumping | 1.446047231231e12 | 8.00954 | -19.54276 | 1.49389 |
| user_001 | Jumping | 1.446047231296e12 | 11.61165 | -19.58108 | 4.25296 |
| user_001 | Jumping | 1.446047231361e12 | 7.89458 | -18.35483 | 3.71647 |
| user_001 | Jumping | 1.446047231426e12 | 0.537083 | -3.21831 | 0.420925 |
| user_001 | Jumping | 1.44604723149e12 | -1.91542 | 4.10087 | -2.6447 |
| user_001 | Jumping | 1.446047231555e12 | -0.919089 | -0.382604 | 0.114362 |
| user_001 | Jumping | 1.44604723162e12 | 0.613724 | -0.842448 | 1.57053 |
| user_001 | Jumping | 1.446047231684e12 | 1.38013 | -0.459245 | 0.305964 |
| user_001 | Jumping | 1.446047231749e12 | 2.29982 | -1.49389 | -1.26517 |
| user_001 | Jumping | 1.446047231814e12 | 4.94392 | -10.95901 | -0.498763 |
| user_001 | Jumping | 1.446047231878e12 | 3.75599 | -19.27452 | -1.76333 |
| user_001 | Jumping | 1.446047231944e12 | 7.70298 | -19.58108 | -2.95126 |
| user_001 | Jumping | 1.446047232008e12 | 6.55337 | -17.51178 | -1.91661 |
| user_001 | Jumping | 1.446047232073e12 | 2.10822 | -2.03038 | -0.268841 |
| user_001 | Jumping | 1.446047232138e12 | -1.68549 | 3.67935 | -0.881966 |
| user_001 | Jumping | 1.446047232203e12 | 0.15388 | -0.114362 | 7.6042e-2 |
| user_001 | Jumping | 1.446047232267e12 | 2.79798 | -1.95374 | 0.689167 |
| user_001 | Jumping | 1.446047232332e12 | 1.45677 | -0.957409 | 0.420925 |
| user_001 | Jumping | 1.446047232397e12 | 1.18853 | -2.95007 | 0.650847 |
| user_001 | Jumping | 1.446047232462e12 | 3.44943 | -13.87135 | -3.71767 |
| user_001 | Jumping | 1.446047232526e12 | 3.79431 | -19.58108 | -0.345482 |
| user_001 | Jumping | 1.446047232591e12 | 6.09353 | -19.58108 | 2.87342 |
| user_001 | Jumping | 1.446047232655e12 | 4.48408 | -14.3312 | -0.498763 |
| user_001 | Jumping | 1.44604723272e12 | -3.7722e-2 | -1.68549 | 2.6435 |
| user_001 | Jumping | 1.446047232785e12 | 0.613724 | 2.68302 | -2.37646 |
| user_001 | Jumping | 1.44604723285e12 | 0.996927 | -0.497565 | 0.497565 |
| user_001 | Jumping | 1.446047232915e12 | 0.15388 | -0.689167 | 1.34061 |
| user_001 | Jumping | 1.446047232979e12 | 1.07357 | 1.07357 | -2.60638 |
| user_001 | Jumping | 1.446047233044e12 | 2.37646 | -3.29495 | -1.45677 |
| user_001 | Jumping | 1.446047233109e12 | 2.22318 | -16.09393 | 1.37893 |
| user_001 | Jumping | 1.446047233173e12 | 6.82161 | -19.58108 | 3.7722e-2 |
| user_001 | Jumping | 1.446047233238e12 | 8.39275 | -19.54276 | 1.22565 |
| user_001 | Jumping | 1.446047233303e12 | 1.22685 | -9.50284 | -0.307161 |
| user_001 | Jumping | 1.446047233368e12 | 1.83997 | -0.152682 | 1.41725 |
| user_001 | Jumping | 1.446047233432e12 | -0.765807 | 0.996927 | -1.91661 |
| user_001 | Jumping | 1.446047233497e12 | 0.728685 | -0.612526 | 0.152682 |
| user_001 | Jumping | 1.446047233562e12 | 0.996927 | -0.305964 | 0.995729 |
| user_001 | Jumping | 1.446047233626e12 | 1.22685 | -0.305964 | 3.7722e-2 |
| user_001 | Jumping | 1.446047233692e12 | 1.91661 | -2.95007 | -0.345482 |
| user_001 | Jumping | 1.446047233755e12 | 4.75232 | -14.63776 | -3.67935 |
| user_001 | Jumping | 1.446047233821e12 | 4.56072 | -19.58108 | -2.6447 |
| user_001 | Jumping | 1.446047233886e12 | 10.69197 | -19.38948 | -5.94025 |
| user_001 | Jumping | 1.44604723395e12 | 3.10454 | -10.42253 | -2.22318 |
| user_001 | Jumping | 1.446047234015e12 | -0.152682 | 1.61005 | 1.80046 |
| user_001 | Jumping | 1.44604723408e12 | -0.267643 | 1.83997 | -0.958607 |
| user_001 | Jumping | 1.446047234145e12 | 1.99325 | -0.842448 | -0.230521 |
| user_001 | Jumping | 1.44604723421e12 | 2.79798 | -0.114362 | 0.919089 |
| user_001 | Jumping | 1.446047234273e12 | 1.11189 | -0.152682 | 1.83878 |
| user_001 | Jumping | 1.446047234339e12 | 2.75966 | -5.05768 | -0.537083 |
| user_001 | Jumping | 1.446047234403e12 | 4.33079 | -16.82202 | -3.06622 |
| user_001 | Jumping | 1.446047234468e12 | 6.89825 | -19.58108 | -4.25415 |
| user_001 | Jumping | 1.446047234533e12 | 11.49669 | -19.50444 | -2.52974 |
| user_001 | Jumping | 1.446047234598e12 | 6.20849 | -9.08132 | -1.80165 |
| user_001 | Jumping | 1.446047234663e12 | 0.805325 | 1.15021 | -0.15388 |
| user_001 | Jumping | 1.446047234726e12 | -1.76214 | 2.14654 | -0.460442 |
| user_001 | Jumping | 1.446047234792e12 | 5.99e-4 | -1.11069 | -0.230521 |
| user_001 | Jumping | 1.446047234857e12 | 0.652044 | 0.230521 | -0.920286 |
| user_001 | Jumping | 1.446047234922e12 | 1.26517 | -0.114362 | -5.99e-4 |
| user_001 | Jumping | 1.446047234986e12 | 5.36544 | -4.09967 | -0.537083 |
| user_001 | Jumping | 1.446047235051e12 | 6.51505 | -17.05194 | -3.56439 |
| user_001 | Jumping | 1.446047235115e12 | 6.93658 | -19.58108 | -2.75966 |
| user_001 | Jumping | 1.446047235181e12 | 7.89458 | -19.58108 | -3.18118 |
| user_001 | Jumping | 1.446047235245e12 | 1.95493 | -9.6178 | -1.34181 |
| user_001 | Jumping | 1.44604723531e12 | -2.18366 | 3.14286 | -0.1922 |
| user_001 | Jumping | 1.446047235375e12 | -2.87342 | 1.99325 | -1.76333 |
| user_001 | Jumping | 1.446047235439e12 | 1.03525 | -1.80046 | 1.64717 |
| user_001 | Jumping | 1.446047235504e12 | 1.95493 | -0.689167 | 1.49389 |
| user_001 | Jumping | 1.446047235569e12 | 1.26517 | 0.383802 | -0.307161 |
| user_001 | Jumping | 1.446047235633e12 | 1.18853 | -2.72014 | 1.41725 |
| user_001 | Jumping | 1.446047235698e12 | 5.71033 | -16.51545 | -2.03158 |
| user_001 | Jumping | 1.446047235763e12 | 6.55337 | -19.58108 | 0.995729 |
| user_001 | Jumping | 1.446047235828e12 | 10.577 | -19.4278 | -1.57173 |
| user_001 | Jumping | 1.446047235892e12 | 2.79798 | -9.88604 | -0.843646 |
| user_001 | Jumping | 1.446047235957e12 | 0.652044 | -3.7722e-2 | 1.14901 |
| user_001 | Jumping | 1.446047236022e12 | 0.843646 | 1.95493 | -2.14654 |
| user_001 | Jumping | 1.446047236086e12 | 0.15388 | -0.305964 | 0.995729 |
| user_001 | Jumping | 1.446047236152e12 | 0.767005 | -0.114362 | 0.804128 |
| user_001 | Jumping | 1.446047236215e12 | -0.650847 | -0.650847 | -0.230521 |
| user_001 | Jumping | 1.446047236281e12 | 3.56439 | -5.90073 | 0.727487 |
| user_001 | Jumping | 1.446047236346e12 | 7.05154 | -17.97163 | -1.61005 |
| user_001 | Jumping | 1.44604723641e12 | 9.46572 | -19.58108 | 2.10702 |
| user_001 | Jumping | 1.446047236475e12 | 7.58802 | -18.54643 | 3.10335 |
| user_001 | Jumping | 1.44604723654e12 | 0.767005 | -6.28393 | 0.689167 |
| user_001 | Jumping | 1.446047236604e12 | 0.307161 | 2.29982 | -0.498763 |
| user_001 | Jumping | 1.446047236669e12 | 0.11556 | -0.114362 | -1.11189 |
| user_001 | Jumping | 1.446047236734e12 | 2.2615 | -1.45557 | 2.14534 |
| user_001 | Jumping | 1.446047236798e12 | 1.80165 | 0.345482 | 0.765807 |
| user_001 | Jumping | 1.446047236863e12 | -0.650847 | -0.305964 | -1.15021 |
| user_001 | Jumping | 1.446047236928e12 | 3.48775 | -7.31858 | -0.728685 |
| user_001 | Jumping | 1.446047236992e12 | 5.32712 | -18.92964 | 0.114362 |
| user_001 | Jumping | 1.446047237057e12 | 10.34708 | -19.58108 | 2.37526 |
| user_001 | Jumping | 1.446047237122e12 | 5.97857 | -17.78002 | 0.344284 |
| user_001 | Jumping | 1.446047237187e12 | 2.8363 | -3.25663 | 0.152682 |
| user_001 | Jumping | 1.446047237252e12 | -0.995729 | 3.37279 | -1.64837 |
| user_001 | Jumping | 1.446047237317e12 | -1.64717 | -0.650847 | 1.41725 |
| user_001 | Jumping | 1.446047237381e12 | 1.30349 | -0.152682 | 1.45557 |
| user_001 | Jumping | 1.446047237446e12 | 2.79798 | 5.99e-4 | -0.460442 |
| user_001 | Jumping | 1.44604723751e12 | 1.41845 | -0.459245 | -1.38013 |
| user_001 | Jumping | 1.446047237575e12 | 3.90927 | -6.62882 | -1.49509 |
| user_001 | Jumping | 1.44604723764e12 | 4.98224 | -19.27452 | 1.57053 |
| user_001 | Jumping | 1.446047237705e12 | 12.22478 | -19.58108 | 2.0687 |
| user_001 | Jumping | 1.446047237769e12 | 4.5224 | -16.74538 | -0.383802 |
| user_001 | Jumping | 1.446047237834e12 | 0.690364 | -0.880768 | -0.537083 |
| user_001 | Jumping | 1.446047237899e12 | 1.26517 | 2.18486 | -1.03525 |
| user_001 | Jumping | 1.446047237964e12 | 2.6447 | -3.7722e-2 | 1.18733 |
| user_001 | Jumping | 1.446047238028e12 | 2.56806 | -0.114362 | 1.03405 |
| user_001 | Jumping | 1.446047238094e12 | -1.14901 | 0.11556 | -0.307161 |
| user_001 | Jumping | 1.446047238157e12 | -0.574206 | -0.114362 | -0.920286 |
| user_001 | Jumping | 1.446047238223e12 | 2.60638 | -3.40991 | -0.422122 |
| user_001 | Jumping | 1.446047238287e12 | 6.32345 | -17.62674 | -0.728685 |
| user_001 | Jumping | 1.446047238352e12 | 10.53868 | -19.58108 | 0.919089 |
| user_001 | Jumping | 1.446047238417e12 | 7.85626 | -18.20155 | 0.995729 |
| user_001 | Jumping | 1.446047238481e12 | -0.152682 | -1.41725 | -1.22685 |
| user_001 | Jumping | 1.446047238546e12 | 1.26517 | 2.22318 | -1.87829 |
| user_001 | Jumping | 1.446047238611e12 | 0.537083 | -0.497565 | 1.76214 |
| user_001 | Jumping | 1.446047238676e12 | 1.95493 | 0.230521 | 1.60885 |
| user_001 | Jumping | 1.446047238741e12 | 0.920286 | 0.537083 | -0.422122 |
| user_001 | Jumping | 1.446047238806e12 | 5.99e-4 | -0.650847 | 0.497565 |
| user_001 | Jumping | 1.44604723887e12 | 1.64837 | -1.99206 | -1.72501 |
| user_001 | Jumping | 1.446047238935e12 | 5.0972 | -14.75272 | -1.41845 |
| user_001 | Jumping | 1.446047239e12 | 10.50036 | -19.58108 | 1.91542 |
| user_001 | Jumping | 1.446047239064e12 | 8.16283 | -19.58108 | 1.95374 |
| user_001 | Jumping | 1.446047239129e12 | 3.56439 | -9.4262 | -1.22685 |
| user_001 | Jumping | 1.446047239194e12 | 0.920286 | 2.79798 | -1.34181 |
| user_001 | Jumping | 1.446047239259e12 | 0.1922 | 0.652044 | -0.537083 |
| user_001 | Jumping | 1.446047239324e12 | 1.64837 | -0.957409 | 1.41725 |
| user_001 | Jumping | 1.446047239388e12 | 1.99325 | -0.191003 | 0.957409 |
| user_001 | Jumping | 1.446047239453e12 | 1.26517 | -0.305964 | -5.99e-4 |
| user_001 | Jumping | 1.446047239518e12 | 2.4531 | -4.17632 | -1.34181 |
| user_001 | Jumping | 1.446047239583e12 | 4.44576 | -16.05561 | -0.537083 |
| user_001 | Jumping | 1.446047239647e12 | 9.58068 | -19.58108 | 1.53221 |
| user_001 | Jumping | 1.446047239712e12 | 8.04786 | -19.2362 | 2.0687 |
| user_001 | Jumping | 1.446047239777e12 | 1.80165 | -6.01569 | -0.422122 |
| user_001 | Jumping | 1.446047239841e12 | 0.11556 | 2.10822 | -2.6447 |
| user_001 | Jumping | 1.446047239906e12 | 0.345482 | -0.612526 | -1.22685 |
| user_001 | Jumping | 1.446047239971e12 | 1.45677 | -0.880768 | 2.2603 |
| user_001 | Jumping | 1.446047240036e12 | 0.843646 | 1.11189 | -0.537083 |
| user_001 | Jumping | 1.4460472401e12 | 0.996927 | 3.8919e-2 | -0.575403 |
| user_001 | Jumping | 1.446047240166e12 | 3.29615 | -3.98471 | -0.422122 |
| user_001 | Jumping | 1.446047240231e12 | 5.21216 | -16.7837 | -0.613724 |
| user_001 | Jumping | 1.446047240295e12 | 7.39642 | -19.58108 | 1.11069 |
| user_001 | Jumping | 1.44604724036e12 | 10.23212 | -19.58108 | 0.459245 |
| user_001 | Jumping | 1.446047240424e12 | 1.80165 | -12.14694 | -1.22685 |
| user_001 | Jumping | 1.446047240489e12 | 0.575403 | 1.83997 | 3.7722e-2 |
| user_001 | Jumping | 1.446047240553e12 | -0.152682 | 2.22318 | -3.0279 |
| user_001 | Jumping | 1.446047240618e12 | 0.268841 | -1.91542 | 3.06503 |
| user_001 | Jumping | 1.446047240683e12 | 1.72501 | -0.535886 | 1.83878 |
| user_001 | Jumping | 1.446047240748e12 | -1.18733 | 0.996927 | -1.26517 |
| user_001 | Jumping | 1.446047240813e12 | 0.920286 | -0.535886 | -1.91661 |
| user_001 | Jumping | 1.446047240878e12 | 4.40743 | -11.61046 | 0.459245 |
| user_001 | Jumping | 1.446047240941e12 | 6.78329 | -19.58108 | 4.82776 |
| user_001 | Jumping | 1.446047241007e12 | 9.35075 | -19.58108 | 5.74745 |
| user_001 | Jumping | 1.446047241072e12 | 5.71033 | -15.74905 | -1.15021 |
| user_001 | Jumping | 1.446047241136e12 | -1.57053 | -1.80046 | -0.15388 |
| user_001 | Jumping | 1.446047241201e12 | -0.267643 | 2.18486 | -2.14654 |
| user_001 | Jumping | 1.446047241266e12 | 2.22318 | -1.80046 | 1.80046 |
| user_001 | Jumping | 1.44604724133e12 | -1.34061 | 0.767005 | -2.03158 |
| user_001 | Jumping | 1.446047241395e12 | -0.382604 | -0.842448 | 0.382604 |
| user_001 | Jumping | 1.446047241459e12 | -0.765807 | -1.41725 | 7.6042e-2 |
| user_001 | Jumping | 1.446047241525e12 | 5.97857 | -10.34589 | -1.49509 |
| user_001 | Jumping | 1.446047241589e12 | 11.34341 | -19.58108 | 6.13065 |
| user_001 | Jumping | 1.446047241654e12 | 10.15548 | -19.58108 | 8.58315 |
| user_001 | Jumping | 1.446047241718e12 | 4.67568 | -11.61046 | -0.307161 |
| user_001 | Jumping | 1.446047241783e12 | -1.37893 | -0.650847 | 0.459245 |
| user_001 | Jumping | 1.446047241849e12 | -0.919089 | 2.6447 | -3.14286 |
| user_001 | Jumping | 1.446047241913e12 | 1.07357 | -1.03405 | -0.613724 |
| user_001 | Jumping | 1.446047241977e12 | 0.15388 | -0.919089 | 2.18366 |
| user_001 | Jumping | 1.446047242042e12 | -0.995729 | 0.460442 | 0.305964 |
| user_001 | Jumping | 1.446047242107e12 | 2.03158 | -0.382604 | 7.6042e-2 |
| user_001 | Jumping | 1.446047242172e12 | 6.93658 | -10.84405 | -0.422122 |
| user_001 | Jumping | 1.446047242237e12 | 13.79591 | -19.58108 | 1.49389 |
| user_001 | Jumping | 1.446047242302e12 | 15.86521 | -19.50444 | -1.72501 |
| user_001 | Jumping | 1.446047242366e12 | 4.79064 | -12.03198 | 1.8771 |
| user_001 | Jumping | 1.446047242431e12 | -0.497565 | 1.45677 | -4.10087 |
| user_001 | Jumping | 1.446047242495e12 | 0.11556 | 0.613724 | -1.30349 |
| user_001 | Jumping | 1.44604724256e12 | -0.459245 | -0.305964 | 2.56686 |
| user_001 | Jumping | 1.446047242625e12 | 2.29982 | 1.18853 | -0.767005 |
| user_001 | Jumping | 1.44604724269e12 | 1.38013 | -0.305964 | -1.87829 |
| user_001 | Jumping | 1.446047242754e12 | 2.52974 | -2.75846 | 0.152682 |
| user_001 | Jumping | 1.446047242819e12 | 5.32712 | -13.21991 | -1.34181 |
| user_001 | Jumping | 1.446047242884e12 | 10.50036 | -19.58108 | 1.8771 |
| user_001 | Jumping | 1.446047242948e12 | 13.02951 | -19.58108 | -0.498763 |
| user_001 | Jumping | 1.446047243013e12 | 4.33079 | -10.34589 | -2.4531 |
| user_001 | Jumping | 1.446047243078e12 | 0.613724 | 0.498763 | 2.49022 |
| user_001 | Jumping | 1.446047243143e12 | 0.307161 | 1.53341 | -2.49142 |
| user_001 | Jumping | 1.446047243207e12 | 7.7239e-2 | -0.344284 | 1.91542 |
| user_001 | Jumping | 1.446047243272e12 | 2.03158 | 0.1922 | -5.99e-4 |
| user_001 | Jumping | 1.446047243337e12 | 1.22685 | 0.230521 | -1.87829 |
| user_001 | Jumping | 1.446047243401e12 | 0.537083 | -1.26397 | 0.535886 |
| user_001 | Jumping | 1.446047243467e12 | 5.2888 | -11.22725 | 0.689167 |
| user_001 | Jumping | 1.446047243531e12 | 11.57333 | -19.58108 | 0.344284 |
| user_001 | Jumping | 1.446047243596e12 | 14.524 | -19.58108 | 2.33694 |
| user_001 | Jumping | 1.446047243661e12 | 3.18118 | -11.8787 | 0.191003 |
| user_001 | Jumping | 1.446047243725e12 | -1.07237 | 1.15021 | 0.152682 |
| user_001 | Jumping | 1.44604724379e12 | 0.422122 | 1.95493 | -1.87829 |
| user_001 | Jumping | 1.446047243855e12 | -0.535886 | -0.957409 | 1.18733 |
| user_001 | Jumping | 1.44604724392e12 | 3.60271 | -0.114362 | 0.574206 |
| user_001 | Jumping | 1.446047243984e12 | 2.87462 | -0.535886 | -0.268841 |
| user_001 | Jumping | 1.446047244049e12 | 2.2615 | -1.53221 | -0.11556 |
| user_001 | Jumping | 1.446047244114e12 | 6.85994 | -10.61413 | -1.11189 |
| user_001 | Jumping | 1.446047244178e12 | 7.97122 | -19.58108 | -7.7239e-2 |
| user_001 | Jumping | 1.446047244243e12 | 14.86888 | -19.58108 | -1.41845 |
| user_001 | Jumping | 1.446047244308e12 | 8.50771 | -14.48448 | -0.613724 |
| user_001 | Jumping | 1.446047244373e12 | -0.535886 | -1.18733 | 1.64717 |
| user_001 | Jumping | 1.446047244437e12 | -4.5212 | 3.37279 | 0.191003 |
| user_001 | Jumping | 1.446047244502e12 | -1.72382 | 0.690364 | 0.191003 |
| user_003 | Jumping | 1.446047778034e12 | -2.75846 | -7.28026 | -1.45677 |
| user_003 | Jumping | 1.446047778099e12 | -3.75479 | -7.08866 | -2.37646 |
| user_003 | Jumping | 1.446047778164e12 | -2.14534 | -6.74378 | -2.22318 |
| user_003 | Jumping | 1.446047778229e12 | -2.22198 | -6.01569 | -2.2615 |
| user_003 | Jumping | 1.446047778293e12 | -3.63983 | -9.04299 | -2.6447 |
| user_003 | Jumping | 1.446047778358e12 | -4.82776 | -17.09026 | -4.02423 |
| user_003 | Jumping | 1.446047778422e12 | -5.82409 | -19.46612 | -6.70665 |
| user_003 | Jumping | 1.446047778488e12 | 0.805325 | -10.92069 | -1.49509 |
| user_003 | Jumping | 1.446047778552e12 | -4.67448 | 1.22685 | 1.64717 |
| user_003 | Jumping | 1.446047778616e12 | -1.37893 | 0.460442 | -2.18486 |
| user_003 | Jumping | 1.446047778682e12 | 0.1922 | -0.650847 | -7.7239e-2 |
| user_003 | Jumping | 1.446047778746e12 | -0.842448 | 0.11556 | -1.26517 |
| user_003 | Jumping | 1.446047778811e12 | -1.26397 | -11.91702 | -3.64103 |
| user_003 | Jumping | 1.446047778876e12 | -1.41725 | -19.58108 | -0.767005 |
| user_003 | Jumping | 1.44604777894e12 | -1.76214 | -15.44249 | -2.68302 |
| user_003 | Jumping | 1.446047779006e12 | -0.574206 | -9.00468 | -1.76333 |
| user_003 | Jumping | 1.446047779071e12 | -3.14167 | -4.36792 | -1.68669 |
| user_003 | Jumping | 1.446047779135e12 | -3.37159 | -4.3296 | -2.4531 |
| user_003 | Jumping | 1.4460477792e12 | -4.06135 | -4.75112 | -3.10454 |
| user_003 | Jumping | 1.446047779265e12 | -4.36792 | -11.26557 | -0.345482 |
| user_003 | Jumping | 1.446047779329e12 | -5.97737 | -19.12124 | -4.25415 |
| user_003 | Jumping | 1.446047779394e12 | -3.75479 | -19.19788 | -4.9056 |
| user_003 | Jumping | 1.446047779459e12 | -0.459245 | -7.39522 | -0.537083 |
| user_003 | Jumping | 1.446047779524e12 | -2.91174 | 3.10454 | -2.37646 |
| user_003 | Jumping | 1.446047779589e12 | -1.41725 | -0.995729 | -0.230521 |
| user_003 | Jumping | 1.446047779653e12 | 0.422122 | -0.114362 | 0.344284 |
| user_003 | Jumping | 1.446047779718e12 | -0.574206 | -0.535886 | -1.18853 |
| user_003 | Jumping | 1.446047779783e12 | -1.72382 | -13.98632 | -4.10087 |
| user_003 | Jumping | 1.446047779847e12 | -3.14167 | -19.58108 | -3.48775 |
| user_003 | Jumping | 1.446047779912e12 | -2.68182 | -13.21991 | -3.18118 |
| user_003 | Jumping | 1.446047779977e12 | -0.267643 | -8.92803 | -2.10822 |
| user_003 | Jumping | 1.446047780042e12 | -3.17999 | -3.98471 | -0.728685 |
| user_003 | Jumping | 1.446047780107e12 | -2.91174 | -4.48288 | -2.18486 |
| user_003 | Jumping | 1.446047780171e12 | -3.37159 | -5.78577 | -3.87095 |
| user_003 | Jumping | 1.446047780236e12 | -4.02303 | -9.84772 | -0.383802 |
| user_003 | Jumping | 1.446047780301e12 | -6.47553 | -17.81835 | -4.13919 |
| user_003 | Jumping | 1.446047780365e12 | -3.86975 | -19.58108 | -5.40376 |
| user_003 | Jumping | 1.44604778043e12 | -1.99206 | -14.44616 | -3.64103 |
| user_003 | Jumping | 1.446047780495e12 | -2.29862 | -1.18733 | -0.307161 |
| user_003 | Jumping | 1.446047780559e12 | -1.8771 | 1.99325 | -1.68669 |
| user_003 | Jumping | 1.446047780625e12 | 5.99e-4 | -0.344284 | 0.650847 |
| user_003 | Jumping | 1.446047780689e12 | -0.459245 | -0.191003 | -0.613724 |
| user_003 | Jumping | 1.446047780754e12 | -2.60518 | -6.9737 | -4.714 |
| user_003 | Jumping | 1.446047780818e12 | -1.41725 | -19.58108 | -2.56806 |
| user_003 | Jumping | 1.446047780884e12 | -3.40991 | -17.51178 | -4.59904 |
| user_003 | Jumping | 1.446047780948e12 | -1.95374 | -10.61413 | -2.91294 |
| user_003 | Jumping | 1.446047781013e12 | -3.06503 | -6.70546 | -1.76333 |
| user_003 | Jumping | 1.446047781078e12 | -2.6435 | -7.20362 | -1.87829 |
| user_003 | Jumping | 1.446047781143e12 | -2.60518 | -7.28026 | -1.64837 |
| user_003 | Jumping | 1.446047781208e12 | -1.72382 | -6.51385 | -2.0699 |
| user_003 | Jumping | 1.446047781272e12 | -0.689167 | -5.55585 | -2.37646 |
| user_003 | Jumping | 1.446047781337e12 | -3.67815 | -8.00835 | -1.99325 |
| user_003 | Jumping | 1.446047781402e12 | -5.63249 | -13.64143 | -3.44943 |
| user_003 | Jumping | 1.446047781467e12 | -5.97737 | -18.16323 | -5.59536 |
| user_003 | Jumping | 1.446047781531e12 | -2.03038 | -19.2362 | -5.25048 |
| user_003 | Jumping | 1.446047781596e12 | -2.14534 | -8.88971 | -1.18853 |
| user_003 | Jumping | 1.44604778166e12 | -3.06503 | 2.52974 | -0.767005 |
| user_003 | Jumping | 1.446047781725e12 | 0.230521 | 0.268841 | -0.345482 |
| user_003 | Jumping | 1.44604778179e12 | 0.15388 | -0.535886 | -0.690364 |
| user_003 | Jumping | 1.446047781855e12 | -0.420925 | -2.18366 | -2.68302 |
| user_003 | Jumping | 1.446047781919e12 | -3.63983 | -17.47346 | -4.44576 |
| user_003 | Jumping | 1.446047781985e12 | -2.91174 | -18.35483 | -4.714 |
| user_003 | Jumping | 1.446047782049e12 | -0.919089 | -10.88237 | -2.2615 |
| user_003 | Jumping | 1.446047782114e12 | -3.02671 | -6.93538 | -2.68302 |
| user_003 | Jumping | 1.446047782178e12 | -2.75846 | -7.3569 | -2.10822 |
| user_003 | Jumping | 1.446047782244e12 | -1.45557 | -7.85507 | -1.22685 |
| user_003 | Jumping | 1.446047782309e12 | -1.8771 | -6.32225 | -1.99325 |
| user_003 | Jumping | 1.446047782373e12 | -1.64717 | -5.90073 | -2.33814 |
| user_003 | Jumping | 1.446047782438e12 | -2.49022 | -8.08499 | -1.80165 |
| user_003 | Jumping | 1.446047782503e12 | -4.40624 | -11.26557 | -2.37646 |
| user_003 | Jumping | 1.446047782568e12 | -5.13432 | -17.1669 | -4.67568 |
| user_003 | Jumping | 1.446047782632e12 | -3.40991 | -19.54276 | -5.71033 |
| user_003 | Jumping | 1.446047782697e12 | -1.99206 | -13.48815 | -4.06255 |
| user_003 | Jumping | 1.446047782762e12 | -2.22198 | -1.68549 | 0.267643 |
| user_003 | Jumping | 1.446047782827e12 | -1.91542 | 1.80165 | -1.68669 |
| user_003 | Jumping | 1.446047782891e12 | 0.575403 | -0.152682 | 0.459245 |
| user_003 | Jumping | 1.446047782956e12 | -0.842448 | -0.804128 | -1.03525 |
| user_003 | Jumping | 1.446047783021e12 | -1.76214 | -12.22358 | -5.67201 |
| user_003 | Jumping | 1.446047783086e12 | -2.33694 | -19.19788 | -4.02423 |
| user_003 | Jumping | 1.446047783151e12 | -1.14901 | -14.98264 | -4.13919 |
| user_003 | Jumping | 1.446047783215e12 | -2.22198 | -9.04299 | -2.91294 |
| user_003 | Jumping | 1.44604778328e12 | -2.41358 | -5.59417 | -0.613724 |
| user_003 | Jumping | 1.446047783344e12 | -2.60518 | -5.78577 | -2.33814 |
| user_003 | Jumping | 1.446047783409e12 | -3.67815 | -4.44456 | -2.4531 |
| user_003 | Jumping | 1.446047783474e12 | -2.8351 | -4.63616 | -3.75599 |
| user_003 | Jumping | 1.446047783539e12 | -1.64717 | -16.4005 | -0.843646 |
| user_003 | Jumping | 1.446047783604e12 | -6.62882 | -19.58108 | -8.58435 |
| user_003 | Jumping | 1.446047783669e12 | -1.72382 | -14.67608 | -3.52607 |
| user_003 | Jumping | 1.446047783733e12 | -1.57053 | 1.34181 | 0.612526 |
| user_003 | Jumping | 1.446047783798e12 | -1.18733 | 1.22685 | -1.38013 |
| user_003 | Jumping | 1.446047783863e12 | -0.612526 | -0.612526 | -0.268841 |
| user_003 | Jumping | 1.446047783928e12 | -1.22565 | 0.345482 | -0.805325 |
| user_003 | Jumping | 1.446047783992e12 | -1.11069 | -2.2603 | -2.56806 |
| user_003 | Jumping | 1.446047784057e12 | -2.8351 | -17.62674 | -5.02056 |
| user_003 | Jumping | 1.446047784122e12 | -3.90807 | -19.19788 | -5.59536 |
| user_003 | Jumping | 1.446047784187e12 | -1.72382 | -12.6451 | -3.64103 |
| user_003 | Jumping | 1.446047784252e12 | -1.49389 | -6.7821 | -2.0699 |
| user_003 | Jumping | 1.446047784316e12 | -2.14534 | -4.67448 | -1.15021 |
| user_003 | Jumping | 1.446047784381e12 | -2.91174 | -6.93538 | -2.6447 |
| user_003 | Jumping | 1.446047784446e12 | -3.71647 | -5.86241 | -2.56806 |
| user_003 | Jumping | 1.44604778451e12 | -0.344284 | -6.51385 | -2.8363 |
| user_003 | Jumping | 1.446047784575e12 | -4.02303 | -14.98264 | -2.2615 |
| user_003 | Jumping | 1.446047784639e12 | -5.36425 | -19.58108 | -8.89091 |
| user_003 | Jumping | 1.446047784704e12 | -0.191003 | -14.906 | -3.94759 |
| user_003 | Jumping | 1.446047784769e12 | -2.4519 | 7.7239e-2 | 1.11069 |
| user_003 | Jumping | 1.446047784834e12 | -2.4519 | 1.22685 | -1.41845 |
| user_003 | Jumping | 1.446047784899e12 | -0.344284 | 0.345482 | -0.15388 |
| user_003 | Jumping | 1.446047784964e12 | -0.842448 | 0.422122 | -0.996927 |
| user_003 | Jumping | 1.446047785029e12 | -2.29862 | -4.3296 | -4.63736 |
| user_003 | Jumping | 1.446047785093e12 | -0.765807 | -18.92964 | -3.98591 |
| user_003 | Jumping | 1.446047785158e12 | -4.3296 | -19.38948 | -5.17384 |
| user_003 | Jumping | 1.446047785222e12 | -1.11069 | -12.6451 | -1.22685 |
| user_003 | Jumping | 1.446047785287e12 | -3.44823 | -7.24194 | -2.18486 |
| user_003 | Jumping | 1.446047785352e12 | -2.49022 | -6.51385 | -2.95126 |
| user_003 | Jumping | 1.446047785417e12 | -2.10702 | -6.85874 | -1.22685 |
| user_003 | Jumping | 1.446047785482e12 | -2.79678 | -7.05034 | -2.14654 |
| user_003 | Jumping | 1.446047785546e12 | -2.56686 | -5.01936 | -2.52974 |
| user_003 | Jumping | 1.446047785611e12 | -2.87342 | -6.62882 | -2.56806 |
| user_003 | Jumping | 1.446047785676e12 | -4.63616 | -14.44616 | -2.52974 |
| user_003 | Jumping | 1.446047785741e12 | -5.67081 | -19.58108 | -6.59169 |
| user_003 | Jumping | 1.446047785805e12 | -3.67815 | -17.66507 | -6.63001 |
| user_003 | Jumping | 1.44604778587e12 | -0.919089 | -4.82776 | -0.268841 |
| user_003 | Jumping | 1.446047785934e12 | -2.91174 | 2.87462 | -2.33814 |
| user_003 | Jumping | 1.446047785999e12 | -0.420925 | -0.420925 | 0.919089 |
| user_003 | Jumping | 1.446047786065e12 | -3.7722e-2 | 0.230521 | 0.382604 |
| user_003 | Jumping | 1.446047786129e12 | -0.842448 | -0.497565 | -3.0279 |
| user_003 | Jumping | 1.446047786194e12 | -0.995729 | -15.48081 | -4.33079 |
| user_003 | Jumping | 1.446047786259e12 | -3.98471 | -19.50444 | -6.89825 |
| user_003 | Jumping | 1.446047786324e12 | -0.574206 | -13.29655 | -3.94759 |
| user_003 | Jumping | 1.446047786388e12 | -1.41725 | -8.85139 | -1.38013 |
| user_003 | Jumping | 1.446047786453e12 | -3.94639 | -5.59417 | -3.14286 |
| user_003 | Jumping | 1.446047786518e12 | -2.18366 | -8.04667 | -3.06622 |
| user_003 | Jumping | 1.446047786582e12 | -1.60885 | -8.92803 | -1.15021 |
| user_003 | Jumping | 1.446047786647e12 | -2.72014 | -5.13432 | -2.33814 |
| user_003 | Jumping | 1.446047786712e12 | -2.03038 | -4.25296 | -3.18118 |
| user_003 | Jumping | 1.446047786777e12 | -3.48655 | -9.84772 | -1.45677 |
| user_003 | Jumping | 1.446047786841e12 | -6.01569 | -18.96796 | -6.78329 |
| user_003 | Jumping | 1.446047786906e12 | -4.44456 | -19.4278 | -8.62267 |
| user_003 | Jumping | 1.44604778697e12 | -0.267643 | -8.92803 | -1.57173 |
| user_003 | Jumping | 1.446047787035e12 | -2.91174 | 3.41111 | -1.53341 |
| user_003 | Jumping | 1.4460477871e12 | 0.268841 | -0.765807 | 0.689167 |
| user_003 | Jumping | 1.446047787165e12 | -0.650847 | 0.383802 | -0.728685 |
| user_003 | Jumping | 1.44604778723e12 | -0.880768 | 0.422122 | -1.03525 |
| user_003 | Jumping | 1.446047787294e12 | -1.60885 | -11.11229 | -5.4804 |
| user_003 | Jumping | 1.446047787358e12 | -4.59784 | -19.35116 | -5.94025 |
| user_003 | Jumping | 1.446047787424e12 | -2.41358 | -15.71073 | -5.74865 |
| user_003 | Jumping | 1.446047787489e12 | -1.26397 | -9.34956 | -3.06622 |
| user_003 | Jumping | 1.446047787553e12 | -3.44823 | -6.47553 | -2.75966 |
| user_003 | Jumping | 1.446047787618e12 | -2.4519 | -7.97003 | -2.2615 |
| user_003 | Jumping | 1.446047787683e12 | -1.95374 | -7.81675 | -1.83997 |
| user_003 | Jumping | 1.446047787747e12 | -2.75846 | -4.67448 | -3.06622 |
| user_003 | Jumping | 1.446047787812e12 | -2.22198 | -5.24928 | -2.0699 |
| user_003 | Jumping | 1.446047787877e12 | -3.29495 | -12.41518 | -1.49509 |
| user_003 | Jumping | 1.446047787942e12 | -5.44089 | -19.46612 | -6.20849 |
| user_003 | Jumping | 1.446047788007e12 | -3.79311 | -18.69972 | -7.70298 |
| user_003 | Jumping | 1.446047788071e12 | -0.382604 | -7.1653 | -1.83997 |
| user_003 | Jumping | 1.446047788136e12 | -3.14167 | 2.72134 | -0.575403 |
| user_003 | Jumping | 1.446047788201e12 | 0.460442 | -0.152682 | 0.114362 |
| user_003 | Jumping | 1.446047788265e12 | -0.842448 | 0.652044 | -0.230521 |
| user_003 | Jumping | 1.446047788331e12 | -2.2603 | -0.229323 | -1.45677 |
| user_003 | Jumping | 1.446047788395e12 | -1.45557 | -13.71807 | -6.63001 |
| user_003 | Jumping | 1.44604778846e12 | -3.14167 | -19.54276 | -6.32345 |
| user_003 | Jumping | 1.446047788524e12 | -1.83878 | -13.06663 | -4.40743 |
| user_003 | Jumping | 1.44604778859e12 | -1.83878 | -9.77108 | -3.25783 |
| user_003 | Jumping | 1.446047788654e12 | -3.14167 | -6.55217 | -2.6447 |
| user_003 | Jumping | 1.446047788719e12 | -2.10702 | -7.31858 | -2.10822 |
| user_003 | Jumping | 1.446047788784e12 | -2.14534 | -5.90073 | -2.0699 |
| user_003 | Jumping | 1.446047788848e12 | -1.57053 | -4.9044 | -2.75966 |
| user_003 | Jumping | 1.446047788913e12 | -2.75846 | -7.5485 | -1.76333 |
| user_003 | Jumping | 1.446047788978e12 | -4.98104 | -15.97897 | -4.67568 |
| user_003 | Jumping | 1.446047789043e12 | -3.10335 | -19.58108 | -8.16283 |
| user_003 | Jumping | 1.446047789107e12 | -1.68549 | -16.36218 | -5.25048 |
| user_003 | Jumping | 1.446047789172e12 | -2.52854 | -2.0687 | -0.345482 |
| user_003 | Jumping | 1.446047789236e12 | -1.72382 | 2.41478 | -2.37646 |
| user_003 | Jumping | 1.446047789301e12 | 0.843646 | -0.382604 | 1.11069 |
| user_003 | Jumping | 1.446047789366e12 | -0.689167 | 0.230521 | -0.728685 |
| user_003 | Jumping | 1.446047789431e12 | -1.14901 | -1.95374 | -2.72134 |
| user_003 | Jumping | 1.446047789495e12 | -3.90807 | -17.74171 | -7.58802 |
| user_003 | Jumping | 1.44604778956e12 | -3.29495 | -18.96796 | -5.05888 |
| user_003 | Jumping | 1.446047789625e12 | -2.22198 | -12.0703 | -3.98591 |
| user_003 | Jumping | 1.44604778969e12 | -2.79678 | -7.7401 | -2.37646 |
| user_003 | Jumping | 1.446047789755e12 | -1.95374 | -5.74745 | -2.22318 |
| user_003 | Jumping | 1.446047789818e12 | -2.03038 | -6.85874 | -1.68669 |
| user_003 | Jumping | 1.446047789883e12 | -2.91174 | -5.67081 | -2.52974 |
| user_003 | Jumping | 1.446047789949e12 | -1.49389 | -4.94272 | -2.75966 |
| user_003 | Jumping | 1.446047790013e12 | -3.37159 | -10.88237 | -1.57173 |
| user_003 | Jumping | 1.446047790078e12 | -5.51753 | -19.58108 | -7.24314 |
| user_003 | Jumping | 1.446047790143e12 | -4.48288 | -17.5501 | -6.9749 |
| user_003 | Jumping | 1.446047790207e12 | -0.612526 | -2.68182 | -0.613724 |
| user_003 | Jumping | 1.446047790272e12 | -1.8771 | 2.68302 | -2.03158 |
| user_003 | Jumping | 1.446047790337e12 | 1.15021 | -0.919089 | 1.64717 |
| user_003 | Jumping | 1.446047790402e12 | -2.03038 | 0.307161 | -1.22685 |
| user_003 | Jumping | 1.446047790467e12 | -1.34061 | -0.191003 | -1.61005 |
| user_003 | Jumping | 1.446047790531e12 | -3.02671 | -13.64143 | -5.17384 |
| user_003 | Jumping | 1.446047790596e12 | -4.75112 | -19.58108 | -7.62634 |
| user_003 | Jumping | 1.44604779066e12 | -2.60518 | -15.05928 | -6.01689 |
| user_003 | Jumping | 1.446047790726e12 | -1.26397 | -8.12331 | -3.10454 |
| user_003 | Jumping | 1.44604779079e12 | -3.25663 | -5.97737 | -3.0279 |
| user_003 | Jumping | 1.446047790855e12 | -0.420925 | -6.24561 | -2.2615 |
| user_003 | Jumping | 1.446047790919e12 | -1.83878 | -3.40991 | -3.60271 |
| user_003 | Jumping | 1.446047790984e12 | -3.94639 | -5.36425 | -3.75599 |
| user_003 | Jumping | 1.446047791049e12 | -2.2603 | -13.98632 | -3.67935 |
| user_003 | Jumping | 1.446047791114e12 | -7.70178 | -19.58108 | -7.5497 |
| user_003 | Jumping | 1.446047791179e12 | -2.52854 | -15.36585 | -6.66833 |
| user_003 | Jumping | 1.446047791243e12 | -0.574206 | -0.497565 | -0.230521 |
| user_003 | Jumping | 1.446047791308e12 | -2.0687 | 0.958607 | -0.575403 |
| user_003 | Jumping | 1.446047791373e12 | 0.11556 | -0.497565 | 0.344284 |
| user_003 | Jumping | 1.446047791438e12 | -1.30229 | 1.34181 | -1.11189 |
| user_003 | Jumping | 1.446047791503e12 | -0.382604 | -0.880768 | -2.22318 |
| user_003 | Jumping | 1.446047791568e12 | -2.87342 | -15.97897 | -6.28513 |
| user_003 | Jumping | 1.446047791632e12 | -2.95007 | -19.38948 | -7.51138 |
| user_003 | Jumping | 1.446047791697e12 | -4.59784 | -13.37319 | -5.02056 |
| user_003 | Jumping | 1.446047791762e12 | -2.14534 | -9.84772 | -3.2195 |
| user_003 | Jumping | 1.446047791827e12 | -2.29862 | -6.55217 | -2.49142 |
| user_003 | Jumping | 1.446047791891e12 | -1.68549 | -7.47186 | -2.29982 |
| user_003 | Jumping | 1.446047791956e12 | -1.22565 | -8.16163 | -2.56806 |
| user_003 | Jumping | 1.446047792021e12 | -2.95007 | -5.096 | -2.52974 |
| user_003 | Jumping | 1.446047792085e12 | -4.02303 | -4.06135 | -7.7413 |
| user_003 | Jumping | 1.446047792151e12 | -2.60518 | -14.40784 | 0.727487 |
| user_003 | Jumping | 1.446047792215e12 | -6.24561 | -19.58108 | -8.89091 |
| user_003 | Jumping | 1.44604779228e12 | -1.60885 | -12.56846 | -3.2195 |
| user_003 | Jumping | 1.446047792344e12 | -3.10335 | 3.14286 | -1.26517 |
| user_003 | Jumping | 1.44604779241e12 | -3.7722e-2 | 5.99e-4 | -0.11556 |
| user_003 | Jumping | 1.446047792474e12 | 0.268841 | -0.650847 | 0.114362 |
| user_003 | Jumping | 1.446047792539e12 | -2.68182 | 0.996927 | -1.99325 |
| user_003 | Jumping | 1.446047792603e12 | -1.64717 | -4.67448 | -5.51872 |
| user_003 | Jumping | 1.446047792669e12 | -2.87342 | -18.96796 | -5.86361 |
| user_003 | Jumping | 1.446047792733e12 | -3.29495 | -18.66139 | -4.44576 |
| user_003 | Jumping | 1.446047792798e12 | -4.02303 | -13.21991 | -4.79064 |
| user_003 | Jumping | 1.446047792863e12 | -2.95007 | -7.97003 | -2.79798 |
| user_003 | Jumping | 1.446047792928e12 | -3.17999 | -6.20729 | -2.29982 |
| user_003 | Jumping | 1.446047792992e12 | -3.25663 | -5.40257 | -2.37646 |
| user_003 | Jumping | 1.446047793057e12 | -3.40991 | -4.5212 | -3.18118 |
| user_003 | Jumping | 1.446047793122e12 | -3.29495 | -3.90807 | -3.94759 |
| user_003 | Jumping | 1.446047793186e12 | -4.7128 | -14.7144 | -2.75966 |
| user_003 | Jumping | 1.446047793251e12 | -4.55952 | -19.58108 | -7.77962 |
| user_003 | Jumping | 1.446047793316e12 | -2.95007 | -16.32385 | -6.93658 |
| user_003 | Jumping | 1.44604779338e12 | -1.26397 | -2.14534 | -1.38013 |
| user_003 | Jumping | 1.446047793445e12 | -2.10702 | 2.33814 | -1.64837 |
| user_003 | Jumping | 1.44604779351e12 | -0.420925 | -0.267643 | 1.07237 |
| user_003 | Jumping | 1.446047793575e12 | -0.995729 | 0.383802 | -0.537083 |
| user_003 | Jumping | 1.44604779364e12 | -1.14901 | -1.76214 | -2.29982 |
| user_003 | Jumping | 1.446047793704e12 | -2.56686 | -14.82936 | -6.28513 |
| user_003 | Jumping | 1.446047793768e12 | -3.40991 | -19.31284 | -7.3581 |
| user_003 | Jumping | 1.446047793834e12 | -3.52487 | -13.87135 | -5.63368 |
| user_003 | Jumping | 1.446047793899e12 | -2.68182 | -10.84405 | -3.10454 |
| user_003 | Jumping | 1.446047793963e12 | -2.72014 | -4.78944 | -2.72134 |
| user_003 | Jumping | 1.446047794028e12 | -1.03405 | -4.94272 | -1.68669 |
| user_003 | Jumping | 1.446047794093e12 | -2.10702 | -5.93905 | -2.14654 |
| user_003 | Jumping | 1.446047794158e12 | -1.57053 | -5.90073 | -3.2195 |
| user_003 | Jumping | 1.446047794222e12 | -4.3296 | -8.08499 | -1.15021 |
| user_003 | Jumping | 1.446047794287e12 | -4.48288 | -19.08292 | -6.36177 |
| user_003 | Jumping | 1.446047794352e12 | -4.67448 | -19.58108 | -8.27779 |
| user_003 | Jumping | 1.446047794416e12 | 5.99e-4 | -10.76741 | -2.33814 |
| user_003 | Jumping | 1.446047794481e12 | -1.95374 | 3.41111 | -1.07357 |
| user_003 | Jumping | 1.446047794546e12 | -0.152682 | -0.689167 | 0.229323 |
| user_003 | Jumping | 1.446047794611e12 | -0.957409 | 0.422122 | -0.460442 |
| user_003 | Jumping | 1.446047794676e12 | -0.957409 | 0.996927 | -0.881966 |
| user_003 | Jumping | 1.44604779474e12 | -1.57053 | -3.60151 | -4.75232 |
| user_003 | Jumping | 1.446047794805e12 | -1.99206 | -18.35483 | -5.78697 |
| user_003 | Jumping | 1.44604779487e12 | -3.71647 | -19.2362 | -6.47673 |
| user_003 | Jumping | 1.446047794935e12 | -0.842448 | -14.94432 | -4.17751 |
| user_002 | Standing | 1.446309439342e12 | -2.49022 | -9.19628 | -1.11189 |
| user_002 | Standing | 1.446309439349e12 | -2.41358 | -9.15796 | -1.22685 |
| user_002 | Standing | 1.446309439358e12 | -2.56686 | -9.15796 | -1.30349 |
| user_002 | Standing | 1.446309439365e12 | -2.75846 | -9.08132 | -1.11189 |
| user_002 | Standing | 1.446309439373e12 | -2.75846 | -9.08132 | -1.15021 |
| user_002 | Standing | 1.446309439382e12 | -2.8351 | -9.15796 | -0.920286 |
| user_002 | Standing | 1.44630943939e12 | -2.8357 | -9.15855 | -0.919687 |
| user_002 | Standing | 1.446309439398e12 | -2.95007 | -9.2346 | -0.881966 |
| user_002 | Standing | 1.446309439406e12 | -2.8351 | -9.11964 | -0.958607 |
| user_002 | Standing | 1.446309439414e12 | -2.8351 | -8.96635 | -1.15021 |
| user_002 | Standing | 1.446309439422e12 | -2.95007 | -9.08132 | -0.920286 |
| user_002 | Standing | 1.44630943943e12 | -2.95007 | -8.88971 | -0.996927 |
| user_002 | Standing | 1.446309439438e12 | -2.91174 | -9.00468 | -1.15021 |
| user_002 | Standing | 1.446309439447e12 | -3.02671 | -8.96635 | -0.996927 |
| user_002 | Standing | 1.446309439454e12 | -2.79678 | -8.96635 | -1.15021 |
| user_002 | Standing | 1.446309439463e12 | -2.87342 | -9.08132 | -1.34181 |
| user_002 | Standing | 1.446309439471e12 | -2.98839 | -9.19628 | -1.22685 |
| user_002 | Standing | 1.446309439479e12 | -2.95007 | -9.00468 | -1.30349 |
| user_002 | Standing | 1.446309439487e12 | -2.98839 | -9.00468 | -1.15021 |
| user_002 | Standing | 1.446309439495e12 | -3.06503 | -9.00468 | -0.958607 |
| user_002 | Standing | 1.446309439503e12 | -3.02671 | -9.04299 | -0.881966 |
| user_002 | Standing | 1.446309439511e12 | -2.91174 | -9.00468 | -0.920286 |
| user_002 | Standing | 1.446309439519e12 | -2.95007 | -9.08132 | -0.767005 |
| user_002 | Standing | 1.446309439527e12 | -2.8351 | -9.00468 | -0.958607 |
| user_002 | Standing | 1.446309439536e12 | -2.95007 | -9.00468 | -0.996927 |
| user_002 | Standing | 1.446309439543e12 | -2.75846 | -9.04299 | -0.690364 |
| user_002 | Standing | 1.446309439552e12 | -2.6435 | -9.00468 | -1.03525 |
| user_002 | Standing | 1.44630943956e12 | -2.72014 | -9.00468 | -1.15021 |
| user_002 | Standing | 1.446309439568e12 | -2.68182 | -9.04299 | -1.11189 |
| user_002 | Standing | 1.446309439576e12 | -2.60518 | -9.00468 | -1.11189 |
| user_002 | Standing | 1.446309439584e12 | -2.52854 | -8.88971 | -1.15021 |
| user_002 | Standing | 1.446309439593e12 | -2.6435 | -8.92803 | -1.03525 |
| user_002 | Standing | 1.446309439601e12 | -2.60518 | -8.96635 | -0.996927 |
| user_002 | Standing | 1.446309439609e12 | -2.6435 | -9.04299 | -0.996927 |
| user_002 | Standing | 1.446309439616e12 | -2.75846 | -9.04299 | -0.920286 |
| user_002 | Standing | 1.446309439624e12 | -2.6435 | -9.19628 | -0.805325 |
| user_002 | Standing | 1.446309439633e12 | -2.79678 | -8.96635 | -0.767005 |
| user_002 | Standing | 1.446309439641e12 | -2.87342 | -8.92803 | -0.958607 |
| user_002 | Standing | 1.446309439649e12 | -2.60518 | -9.11964 | -0.843646 |
| user_002 | Standing | 1.446309439657e12 | -2.8351 | -9.08132 | -0.843646 |
| user_002 | Standing | 1.446309439665e12 | -2.8351 | -9.2346 | -0.920286 |
| user_002 | Standing | 1.446309439674e12 | -2.72014 | -9.11964 | -0.958607 |
| user_002 | Standing | 1.446309439682e12 | -2.87342 | -9.15796 | -1.18853 |
| user_002 | Standing | 1.44630943969e12 | -3.06503 | -9.2346 | -1.18853 |
| user_002 | Standing | 1.446309439698e12 | -2.8351 | -9.08132 | -0.996927 |
| user_002 | Standing | 1.446309439705e12 | -2.98839 | -9.04299 | -1.03525 |
| user_002 | Standing | 1.446309439713e12 | -3.17999 | -8.96635 | -1.18853 |
| user_002 | Standing | 1.446309439722e12 | -3.21831 | -8.88971 | -1.18853 |
| user_002 | Standing | 1.446309439729e12 | -3.17999 | -8.92803 | -1.15021 |
| user_002 | Standing | 1.446309439738e12 | -3.17999 | -8.77475 | -1.22685 |
| user_002 | Standing | 1.446309439746e12 | -3.33327 | -8.65979 | -1.07357 |
| user_002 | Standing | 1.446309439754e12 | -3.37159 | -8.58315 | -1.34181 |
| user_002 | Standing | 1.446309439762e12 | -3.33327 | -8.77475 | -1.18853 |
| user_002 | Standing | 1.446309439771e12 | -3.33327 | -8.58315 | -1.07357 |
| user_002 | Standing | 1.446309439779e12 | -3.14167 | -8.92803 | -1.03525 |
| user_002 | Standing | 1.446309439787e12 | -3.25663 | -8.85139 | -0.881966 |
| user_002 | Standing | 1.446309439795e12 | -3.10335 | -8.85139 | -0.920286 |
| user_002 | Standing | 1.446309439803e12 | -2.87342 | -8.85139 | -0.843646 |
| user_002 | Standing | 1.446309439811e12 | -2.91174 | -8.92803 | -0.767005 |
| user_002 | Standing | 1.446309439819e12 | -2.87342 | -8.88971 | -0.805325 |
| user_002 | Standing | 1.446309439827e12 | -2.98839 | -8.96635 | -0.767005 |
| user_002 | Standing | 1.446309439835e12 | -2.79678 | -9.11964 | -1.07357 |
| user_002 | Standing | 1.446309439843e12 | -2.87342 | -9.08132 | -0.920286 |
| user_002 | Standing | 1.446309439851e12 | -2.95007 | -9.15796 | -0.805325 |
| user_002 | Standing | 1.446309439859e12 | -3.02671 | -8.85139 | -0.728685 |
| user_002 | Standing | 1.446309439868e12 | -2.98839 | -8.92803 | -0.690364 |
| user_002 | Standing | 1.446309439875e12 | -2.98839 | -8.96635 | -0.690364 |
| user_002 | Standing | 1.446309439883e12 | -2.91174 | -8.96635 | -0.881966 |
| user_002 | Standing | 1.446309439892e12 | -3.02671 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.4463094399e12 | -3.06503 | -9.04299 | -0.767005 |
| user_002 | Standing | 1.446309439908e12 | -2.87342 | -8.73643 | -0.652044 |
| user_002 | Standing | 1.446309439916e12 | -2.95007 | -8.88971 | -0.728685 |
| user_002 | Standing | 1.446309439924e12 | -3.10335 | -8.77475 | -0.690364 |
| user_002 | Standing | 1.446309439932e12 | -2.91174 | -8.77475 | -0.652044 |
| user_002 | Standing | 1.44630943994e12 | -2.91174 | -9.04299 | -0.805325 |
| user_002 | Standing | 1.446309439948e12 | -2.91174 | -9.04299 | -0.767005 |
| user_002 | Standing | 1.446309439957e12 | -3.02671 | -9.04299 | -0.652044 |
| user_002 | Standing | 1.446309439965e12 | -3.06503 | -9.27292 | -0.805325 |
| user_002 | Standing | 1.446309439973e12 | -3.14167 | -9.00468 | -0.767005 |
| user_002 | Standing | 1.44630943998e12 | -2.95007 | -8.96635 | -0.843646 |
| user_002 | Standing | 1.446309439989e12 | -3.10335 | -9.00468 | -1.15021 |
| user_002 | Standing | 1.446309439997e12 | -2.98839 | -9.19628 | -1.03525 |
| user_002 | Standing | 1.446309440004e12 | -3.10335 | -9.19628 | -1.15021 |
| user_002 | Standing | 1.446309440013e12 | -2.91174 | -9.11964 | -1.18853 |
| user_002 | Standing | 1.446309440021e12 | -2.91174 | -9.08132 | -1.22685 |
| user_002 | Standing | 1.446309440029e12 | -2.95007 | -9.00468 | -1.26517 |
| user_002 | Standing | 1.446309440038e12 | -3.06503 | -9.08132 | -1.15021 |
| user_002 | Standing | 1.446309440045e12 | -3.06503 | -9.00468 | -1.03525 |
| user_002 | Standing | 1.446309440054e12 | -3.10335 | -9.11964 | -1.07357 |
| user_002 | Standing | 1.446309440062e12 | -3.10335 | -8.96635 | -0.996927 |
| user_002 | Standing | 1.446309440069e12 | -3.10335 | -8.96635 | -1.03525 |
| user_002 | Standing | 1.446309440078e12 | -3.17999 | -9.00468 | -1.11189 |
| user_002 | Standing | 1.446309440086e12 | -3.14167 | -8.85139 | -0.996927 |
| user_002 | Standing | 1.446309440094e12 | -3.17999 | -9.00468 | -0.728685 |
| user_002 | Standing | 1.446309440102e12 | -3.17999 | -8.96635 | -0.958607 |
| user_002 | Standing | 1.446309440111e12 | -3.10335 | -9.00468 | -0.881966 |
| user_002 | Standing | 1.446309440119e12 | -3.17999 | -9.00468 | -1.03525 |
| user_002 | Standing | 1.446309440126e12 | -3.02671 | -9.00468 | -0.881966 |
| user_002 | Standing | 1.446309440135e12 | -3.06503 | -9.00468 | -0.728685 |
| user_002 | Standing | 1.446309440142e12 | -3.21831 | -8.92803 | -1.03525 |
| user_002 | Standing | 1.446309440151e12 | -3.33327 | -8.85139 | -0.767005 |
| user_002 | Standing | 1.446309440158e12 | -3.21831 | -9.04299 | -0.652044 |
| user_002 | Standing | 1.446309440167e12 | -3.33327 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309440175e12 | -3.37159 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309440183e12 | -3.37159 | -9.00468 | -0.652044 |
| user_002 | Standing | 1.446309440192e12 | -3.33327 | -8.81307 | -0.460442 |
| user_002 | Standing | 1.446309440199e12 | -3.21831 | -9.08132 | -0.537083 |
| user_002 | Standing | 1.446309440208e12 | -3.21831 | -8.92803 | -0.690364 |
| user_002 | Standing | 1.446309440215e12 | -3.14167 | -8.96635 | -0.575403 |
| user_002 | Standing | 1.446309440224e12 | -3.17999 | -8.92803 | -0.690364 |
| user_002 | Standing | 1.446309440232e12 | -3.17999 | -8.77475 | -0.690364 |
| user_002 | Standing | 1.446309440239e12 | -3.14167 | -8.88971 | -0.805325 |
| user_002 | Standing | 1.446309440248e12 | -3.10335 | -9.04299 | -0.843646 |
| user_002 | Standing | 1.446309440255e12 | -3.14167 | -8.88971 | -0.767005 |
| user_002 | Standing | 1.446309440264e12 | -3.06503 | -9.11964 | -0.805325 |
| user_002 | Standing | 1.446309440272e12 | -3.14167 | -8.96635 | -0.690364 |
| user_002 | Standing | 1.44630944028e12 | -3.29495 | -9.08132 | -0.843646 |
| user_002 | Standing | 1.446309440288e12 | -3.33327 | -8.81307 | -0.575403 |
| user_002 | Standing | 1.446309440296e12 | -3.21831 | -8.88971 | -0.575403 |
| user_002 | Standing | 1.446309440305e12 | -3.21831 | -8.96635 | -0.613724 |
| user_002 | Standing | 1.446309440312e12 | -3.14167 | -8.77475 | -0.537083 |
| user_002 | Standing | 1.44630944032e12 | -3.10335 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309440329e12 | -3.21831 | -9.00468 | -0.613724 |
| user_002 | Standing | 1.446309440337e12 | -3.14167 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309440345e12 | -3.21831 | -9.04299 | -0.690364 |
| user_002 | Standing | 1.446309440353e12 | -3.06503 | -8.92803 | -0.575403 |
| user_002 | Standing | 1.446309440361e12 | -3.25663 | -9.11964 | -0.728685 |
| user_002 | Standing | 1.446309440369e12 | -3.21831 | -8.92803 | -0.652044 |
| user_002 | Standing | 1.446309440377e12 | -3.06503 | -8.92803 | -0.843646 |
| user_002 | Standing | 1.446309440385e12 | -3.06503 | -9.04299 | -0.690364 |
| user_002 | Standing | 1.446309440394e12 | -3.21831 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309440402e12 | -3.02671 | -8.92803 | -0.652044 |
| user_002 | Standing | 1.44630944041e12 | -3.21831 | -8.96635 | -0.652044 |
| user_002 | Standing | 1.446309440417e12 | -3.25663 | -8.96635 | -0.767005 |
| user_002 | Standing | 1.446309440426e12 | -3.21831 | -8.88971 | -0.728685 |
| user_002 | Standing | 1.446309440434e12 | -3.37159 | -8.85139 | -0.767005 |
| user_002 | Standing | 1.446309440443e12 | -3.21831 | -8.85139 | -0.537083 |
| user_002 | Standing | 1.44630944045e12 | -3.10335 | -9.11964 | -0.767005 |
| user_002 | Standing | 1.446309440458e12 | -3.10335 | -9.19628 | -0.920286 |
| user_002 | Standing | 1.446309440467e12 | -3.10335 | -9.00468 | -0.767005 |
| user_002 | Standing | 1.446309440474e12 | -3.02671 | -8.96635 | -0.767005 |
| user_002 | Standing | 1.446309440482e12 | -3.10335 | -8.92803 | -0.805325 |
| user_002 | Standing | 1.446309440491e12 | -3.17999 | -8.92803 | -0.843646 |
| user_002 | Standing | 1.446309440498e12 | -3.33327 | -8.96635 | -0.805325 |
| user_002 | Standing | 1.446309440506e12 | -3.29495 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.446309440515e12 | -3.21831 | -8.88971 | -0.728685 |
| user_002 | Standing | 1.446309440523e12 | -3.29495 | -8.92803 | -0.652044 |
| user_002 | Standing | 1.446309440531e12 | -3.37159 | -9.04299 | -0.575403 |
| user_002 | Standing | 1.446309440539e12 | -3.33327 | -8.92803 | -0.613724 |
| user_002 | Standing | 1.446309440547e12 | -3.33327 | -8.88971 | -0.498763 |
| user_002 | Standing | 1.446309440555e12 | -3.21831 | -9.08132 | -0.690364 |
| user_002 | Standing | 1.446309440564e12 | -3.06503 | -8.88971 | -0.652044 |
| user_002 | Standing | 1.446309440571e12 | -3.17999 | -8.88971 | -0.537083 |
| user_002 | Standing | 1.44630944058e12 | -3.06503 | -8.88971 | -0.498763 |
| user_002 | Standing | 1.446309440587e12 | -3.10335 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.446309440596e12 | -3.06503 | -9.08132 | -0.613724 |
| user_002 | Standing | 1.446309440604e12 | -2.87342 | -8.85139 | -0.652044 |
| user_002 | Standing | 1.446309440612e12 | -2.95007 | -9.08132 | -0.728685 |
| user_002 | Standing | 1.44630944062e12 | -3.14167 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309440628e12 | -3.02671 | -8.92803 | -0.537083 |
| user_002 | Standing | 1.446309440636e12 | -3.14167 | -9.00468 | -0.422122 |
| user_002 | Standing | 1.446309440645e12 | -3.10335 | -9.08132 | -0.383802 |
| user_002 | Standing | 1.446309440653e12 | -3.14167 | -8.81307 | -0.307161 |
| user_002 | Standing | 1.44630944066e12 | -3.17999 | -8.85139 | -0.422122 |
| user_002 | Standing | 1.446309440669e12 | -3.21831 | -8.85139 | -0.1922 |
| user_002 | Standing | 1.446309440676e12 | -3.17999 | -9.04299 | -0.498763 |
| user_002 | Standing | 1.446309440685e12 | -3.02671 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309440693e12 | -3.21831 | -8.85139 | -0.498763 |
| user_002 | Standing | 1.446309440701e12 | -3.10335 | -9.08132 | -0.498763 |
| user_002 | Standing | 1.446309440709e12 | -3.29495 | -8.96635 | -0.537083 |
| user_002 | Standing | 1.446309440717e12 | -3.06503 | -8.88971 | -0.383802 |
| user_002 | Standing | 1.446309440725e12 | -3.10335 | -9.15796 | -0.498763 |
| user_002 | Standing | 1.446309440734e12 | -2.91174 | -9.00468 | -0.345482 |
| user_002 | Standing | 1.446309440741e12 | -3.17999 | -8.88971 | -0.498763 |
| user_002 | Standing | 1.446309440749e12 | -3.21831 | -9.08132 | -0.537083 |
| user_002 | Standing | 1.446309440757e12 | -3.06503 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309440766e12 | -3.06503 | -9.00468 | -0.613724 |
| user_002 | Standing | 1.446309440774e12 | -3.10335 | -8.92803 | -0.498763 |
| user_002 | Standing | 1.446309440782e12 | -3.25663 | -8.92803 | -0.613724 |
| user_002 | Standing | 1.44630944079e12 | -3.17999 | -8.92803 | -0.728685 |
| user_002 | Standing | 1.446309440798e12 | -3.29495 | -9.04299 | -0.422122 |
| user_002 | Standing | 1.446309440806e12 | -3.17999 | -9.08132 | -0.422122 |
| user_002 | Standing | 1.446309440814e12 | -3.21831 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309440822e12 | -3.06503 | -8.96635 | -0.460442 |
| user_002 | Standing | 1.446309440831e12 | -3.17999 | -9.04299 | -0.537083 |
| user_002 | Standing | 1.446309440839e12 | -3.14167 | -9.04299 | -0.498763 |
| user_002 | Standing | 1.446309440847e12 | -3.17999 | -9.08132 | -0.345482 |
| user_002 | Standing | 1.446309440855e12 | -3.21831 | -9.08132 | -0.613724 |
| user_002 | Standing | 1.446309440863e12 | -3.33327 | -9.15796 | -0.460442 |
| user_002 | Standing | 1.446309440871e12 | -3.37159 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309440879e12 | -3.17999 | -8.92803 | -0.537083 |
| user_002 | Standing | 1.446309440887e12 | -3.17999 | -8.96635 | -0.345482 |
| user_002 | Standing | 1.446309440896e12 | -3.17999 | -9.04299 | -0.383802 |
| user_002 | Standing | 1.446309440904e12 | -3.37159 | -9.11964 | -0.345482 |
| user_002 | Standing | 1.446309440912e12 | -3.33327 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.44630944092e12 | -3.44823 | -8.96635 | -0.460442 |
| user_002 | Standing | 1.446309440927e12 | -3.25663 | -8.81307 | -0.345482 |
| user_002 | Standing | 1.446309440936e12 | -3.25663 | -8.92803 | -0.460442 |
| user_002 | Standing | 1.446309440944e12 | -3.40991 | -8.85139 | -0.652044 |
| user_002 | Standing | 1.446309440951e12 | -3.21831 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.44630944096e12 | -3.33327 | -8.85139 | -0.498763 |
| user_002 | Standing | 1.446309440968e12 | -3.37159 | -8.85139 | -0.460442 |
| user_002 | Standing | 1.446309440976e12 | -3.17999 | -8.81307 | -0.728685 |
| user_002 | Standing | 1.446309440985e12 | -2.98839 | -8.88971 | -0.575403 |
| user_002 | Standing | 1.446309440992e12 | -3.06503 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309441001e12 | -2.87342 | -8.88971 | -0.652044 |
| user_002 | Standing | 1.446309441009e12 | -3.06503 | -9.11964 | -0.805325 |
| user_002 | Standing | 1.446309441017e12 | -2.91174 | -9.08132 | -0.652044 |
| user_002 | Standing | 1.446309441025e12 | -2.87342 | -9.00468 | -0.498763 |
| user_002 | Standing | 1.446309441033e12 | -2.91174 | -9.15796 | -0.422122 |
| user_002 | Standing | 1.446309441041e12 | -2.91174 | -8.96635 | -0.383802 |
| user_002 | Standing | 1.446309441049e12 | -2.87342 | -9.04299 | -0.575403 |
| user_002 | Standing | 1.446309441057e12 | -3.02671 | -9.19628 | -0.422122 |
| user_002 | Standing | 1.446309441065e12 | -3.02671 | -9.04299 | -0.498763 |
| user_002 | Standing | 1.446309441073e12 | -3.06503 | -9.00468 | -0.422122 |
| user_002 | Standing | 1.446309441081e12 | -2.95007 | -9.04299 | -0.422122 |
| user_002 | Standing | 1.44630944109e12 | -3.06503 | -9.04299 | -0.613724 |
| user_002 | Standing | 1.446309441098e12 | -2.95007 | -9.11964 | -0.498763 |
| user_002 | Standing | 1.446309441106e12 | -2.98839 | -8.96635 | -0.498763 |
| user_002 | Standing | 1.446309441113e12 | -2.98839 | -9.11964 | -0.498763 |
| user_002 | Standing | 1.446309441121e12 | -2.98839 | -9.15796 | -0.613724 |
| user_002 | Standing | 1.44630944113e12 | -3.14167 | -9.08132 | -0.575403 |
| user_002 | Standing | 1.446309441138e12 | -3.10335 | -9.15796 | -0.613724 |
| user_002 | Standing | 1.446309441146e12 | -2.98839 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309441154e12 | -3.17999 | -9.00468 | -0.537083 |
| user_002 | Standing | 1.446309441162e12 | -3.06503 | -8.85139 | -0.537083 |
| user_002 | Standing | 1.44630944117e12 | -3.25663 | -9.11964 | -0.460442 |
| user_002 | Standing | 1.446309441179e12 | -3.29495 | -8.96635 | -0.498763 |
| user_002 | Standing | 1.446309441186e12 | -3.33327 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309441195e12 | -3.17999 | -8.96635 | -0.575403 |
| user_002 | Standing | 1.446309441202e12 | -3.21831 | -8.81307 | -0.498763 |
| user_002 | Standing | 1.446309441211e12 | -3.52487 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309441219e12 | -3.40991 | -9.04299 | -0.422122 |
| user_002 | Standing | 1.446309441226e12 | -3.37159 | -8.77475 | -0.498763 |
| user_002 | Standing | 1.446309441235e12 | -3.48655 | -8.81307 | -0.498763 |
| user_002 | Standing | 1.446309441243e12 | -3.29495 | -8.96635 | -0.652044 |
| user_002 | Standing | 1.446309441252e12 | -3.25663 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.44630944126e12 | -3.37159 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309441268e12 | -3.14167 | -8.85139 | -0.690364 |
| user_002 | Standing | 1.446309441276e12 | -3.29495 | -8.81307 | -0.498763 |
| user_002 | Standing | 1.446309441284e12 | -3.37159 | -9.11964 | -0.575403 |
| user_002 | Standing | 1.446309441292e12 | -3.33327 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.4463094413e12 | -3.25663 | -8.77475 | -0.460442 |
| user_002 | Standing | 1.446309441308e12 | -3.40991 | -8.65979 | -0.345482 |
| user_002 | Standing | 1.446309441316e12 | -3.17999 | -9.04299 | -0.422122 |
| user_002 | Standing | 1.446309441324e12 | -3.44823 | -8.88971 | -0.383802 |
| user_002 | Standing | 1.446309441332e12 | -3.25663 | -8.73643 | -0.383802 |
| user_002 | Standing | 1.446309441341e12 | -3.37159 | -9.08132 | -0.460442 |
| user_002 | Standing | 1.446309441349e12 | -3.44823 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.446309441357e12 | -3.25663 | -8.92803 | -0.230521 |
| user_002 | Standing | 1.446309441365e12 | -3.25663 | -8.81307 | -0.383802 |
| user_002 | Standing | 1.446309441373e12 | -3.44823 | -8.96635 | -0.268841 |
| user_002 | Standing | 1.446309441381e12 | -3.40991 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441389e12 | -3.33327 | -8.92803 | -0.345482 |
| user_002 | Standing | 1.446309441397e12 | -3.52487 | -8.73643 | -0.307161 |
| user_002 | Standing | 1.446309441405e12 | -3.40991 | -8.81307 | -0.1922 |
| user_002 | Standing | 1.446309441413e12 | -3.29495 | -9.00468 | -0.307161 |
| user_002 | Standing | 1.446309441421e12 | -3.33327 | -9.04299 | -0.1922 |
| user_002 | Standing | 1.44630944143e12 | -3.29495 | -8.88971 | -0.230521 |
| user_002 | Standing | 1.446309441437e12 | -3.17999 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.446309441445e12 | -3.37159 | -8.85139 | -0.422122 |
| user_002 | Standing | 1.446309441453e12 | -3.29495 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441462e12 | -3.44823 | -8.85139 | -0.383802 |
| user_002 | Standing | 1.44630944147e12 | -3.25663 | -8.85139 | -0.460442 |
| user_002 | Standing | 1.446309441478e12 | -3.37159 | -8.88971 | -0.383802 |
| user_002 | Standing | 1.446309441486e12 | -3.29495 | -8.88971 | -0.345482 |
| user_002 | Standing | 1.446309441494e12 | -3.17999 | -8.92803 | -0.345482 |
| user_002 | Standing | 1.446309441501e12 | -3.29495 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.44630944151e12 | -3.14167 | -8.96635 | -0.498763 |
| user_002 | Standing | 1.446309441518e12 | -3.17999 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309441527e12 | -3.06503 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309441535e12 | -3.33327 | -8.81307 | -0.613724 |
| user_002 | Standing | 1.446309441543e12 | -3.10335 | -8.92803 | -0.383802 |
| user_002 | Standing | 1.446309441551e12 | -3.29495 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.446309441558e12 | -3.21831 | -8.85139 | -0.460442 |
| user_002 | Standing | 1.446309441567e12 | -3.25663 | -8.92803 | -0.460442 |
| user_002 | Standing | 1.446309441575e12 | -3.17999 | -8.85139 | -0.460442 |
| user_002 | Standing | 1.446309441583e12 | -3.21831 | -8.77475 | -0.422122 |
| user_002 | Standing | 1.446309441591e12 | -3.40991 | -8.73643 | -0.230521 |
| user_002 | Standing | 1.446309441599e12 | -3.44823 | -8.92803 | -0.498763 |
| user_002 | Standing | 1.446309441607e12 | -3.25663 | -8.81307 | -0.383802 |
| user_002 | Standing | 1.446309441615e12 | -3.40991 | -8.77475 | -0.345482 |
| user_002 | Standing | 1.446309441623e12 | -3.29495 | -8.69811 | -0.307161 |
| user_002 | Standing | 1.446309441632e12 | -3.25663 | -8.65979 | -0.460442 |
| user_002 | Standing | 1.446309441639e12 | -3.21831 | -8.77475 | -0.422122 |
| user_002 | Standing | 1.446309441648e12 | -3.17999 | -9.00468 | -0.460442 |
| user_002 | Standing | 1.446309441655e12 | -3.06503 | -8.96635 | -0.383802 |
| user_002 | Standing | 1.446309441664e12 | -3.17999 | -9.2346 | -0.460442 |
| user_002 | Standing | 1.446309441672e12 | -3.10335 | -9.11964 | -0.422122 |
| user_002 | Standing | 1.44630944168e12 | -3.10335 | -9.19628 | -0.345482 |
| user_002 | Standing | 1.446309441688e12 | -3.29495 | -9.19628 | -0.422122 |
| user_002 | Standing | 1.446309441696e12 | -3.21831 | -8.96635 | -0.307161 |
| user_002 | Standing | 1.446309441705e12 | -3.14167 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309441713e12 | -3.25663 | -9.00468 | -0.422122 |
| user_002 | Standing | 1.446309441721e12 | -3.25663 | -9.00468 | -0.575403 |
| user_002 | Standing | 1.446309441729e12 | -3.29495 | -9.04299 | -0.345482 |
| user_002 | Standing | 1.446309441737e12 | -3.25663 | -9.00468 | -0.575403 |
| user_002 | Standing | 1.446309441745e12 | -3.17999 | -8.96635 | -0.537083 |
| user_002 | Standing | 1.446309441752e12 | -3.25663 | -9.11964 | -0.422122 |
| user_002 | Standing | 1.446309441761e12 | -3.21831 | -9.11964 | -0.307161 |
| user_002 | Standing | 1.446309441769e12 | -3.10335 | -8.92803 | -0.460442 |
| user_002 | Standing | 1.446309441777e12 | -3.10335 | -9.04299 | -0.460442 |
| user_002 | Standing | 1.446309441785e12 | -3.21831 | -9.11964 | -0.345482 |
| user_002 | Standing | 1.446309441793e12 | -3.17999 | -9.2346 | -0.307161 |
| user_002 | Standing | 1.446309441802e12 | -3.17999 | -9.08132 | -0.383802 |
| user_002 | Standing | 1.446309441809e12 | -3.17999 | -9.04299 | -0.498763 |
| user_002 | Standing | 1.446309441818e12 | -3.06503 | -9.04299 | -0.383802 |
| user_002 | Standing | 1.446309441825e12 | -3.14167 | -8.96635 | -0.383802 |
| user_002 | Standing | 1.446309441833e12 | -3.25663 | -8.88971 | -0.422122 |
| user_002 | Standing | 1.446309441842e12 | -3.17999 | -8.88971 | -0.537083 |
| user_002 | Standing | 1.44630944185e12 | -3.21831 | -8.96635 | -0.422122 |
| user_002 | Standing | 1.446309441858e12 | -3.02671 | -8.81307 | -0.460442 |
| user_002 | Standing | 1.446309441867e12 | -3.17999 | -8.92803 | -0.460442 |
| user_002 | Standing | 1.446309441875e12 | -3.06503 | -8.85139 | -0.268841 |
| user_002 | Standing | 1.446309441882e12 | -3.29495 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441891e12 | -3.33327 | -8.88971 | -0.268841 |
| user_002 | Standing | 1.446309441899e12 | -3.33327 | -8.81307 | -0.1922 |
| user_002 | Standing | 1.446309441907e12 | -3.17999 | -8.77475 | -0.230521 |
| user_002 | Standing | 1.446309441915e12 | -3.17999 | -9.08132 | -3.8919e-2 |
| user_002 | Standing | 1.446309441923e12 | -3.14167 | -9.19628 | -0.15388 |
| user_002 | Standing | 1.446309441931e12 | -3.21831 | -9.08132 | -0.1922 |
| user_002 | Standing | 1.446309441939e12 | -3.06503 | -9.00468 | -0.268841 |
| user_002 | Standing | 1.446309441947e12 | -3.29495 | -8.92803 | -0.11556 |
| user_002 | Standing | 1.446309441956e12 | -3.21831 | -9.00468 | -0.307161 |
| user_002 | Standing | 1.446309441964e12 | -3.06503 | -8.88971 | -0.268841 |
| user_002 | Standing | 1.446309441971e12 | -3.25663 | -9.00468 | -0.15388 |
| user_002 | Standing | 1.446309441979e12 | -3.25663 | -9.08132 | -0.268841 |
| user_002 | Standing | 1.446309441987e12 | -3.29495 | -8.92803 | -0.1922 |
| user_002 | Standing | 1.446309441995e12 | -3.33327 | -8.96635 | -0.268841 |
| user_002 | Standing | 1.446309442004e12 | -3.21831 | -8.88971 | -0.11556 |
| user_002 | Standing | 1.446309442012e12 | -3.25663 | -9.11964 | -5.99e-4 |
| user_002 | Standing | 1.44630944202e12 | -3.06503 | -9.00468 | -5.99e-4 |
| user_002 | Standing | 1.446309442028e12 | -3.06503 | -9.11964 | -3.8919e-2 |
| user_002 | Standing | 1.446309442036e12 | -3.02671 | -9.2346 | -3.8919e-2 |
| user_002 | Standing | 1.446309442045e12 | -2.95007 | -9.19628 | -0.15388 |
| user_002 | Standing | 1.446309442052e12 | -2.91174 | -9.04299 | -0.1922 |
| user_002 | Standing | 1.446309442061e12 | -2.91174 | -9.19628 | -5.99e-4 |
| user_002 | Standing | 1.446309442068e12 | -2.95007 | -9.08132 | -5.99e-4 |
| user_002 | Standing | 1.446309442077e12 | -3.10335 | -9.08132 | -7.7239e-2 |
| user_002 | Standing | 1.446309442085e12 | -2.98839 | -9.00468 | -5.99e-4 |
| user_002 | Standing | 1.446309442092e12 | -3.10335 | -9.15796 | -0.268841 |
| user_002 | Standing | 1.446309442157e12 | -3.02671 | -8.92803 | -7.7239e-2 |
| user_002 | Standing | 1.446309442223e12 | -3.14167 | -8.92803 | -3.8919e-2 |
| user_002 | Standing | 1.446309442287e12 | -2.87342 | -9.08132 | -0.268841 |
| user_002 | Standing | 1.446309442352e12 | -2.72014 | -9.11964 | -7.7239e-2 |
| user_002 | Standing | 1.446309442417e12 | -2.56686 | -9.19628 | -3.8919e-2 |
| user_002 | Standing | 1.446309442481e12 | -2.6435 | -9.11964 | 3.7722e-2 |
| user_002 | Standing | 1.446309442547e12 | -2.56686 | -9.11964 | -7.7239e-2 |
| user_002 | Standing | 1.446309442611e12 | -2.60518 | -9.11964 | -5.99e-4 |
| user_002 | Standing | 1.446309442675e12 | -2.4519 | -9.2346 | -5.99e-4 |
| user_002 | Standing | 1.44630944274e12 | -2.41358 | -9.11964 | -5.99e-4 |
| user_002 | Standing | 1.446309442805e12 | -2.41358 | -9.08132 | -7.7239e-2 |
| user_002 | Standing | 1.44630944287e12 | -2.33694 | -9.15796 | -7.7239e-2 |
| user_002 | Standing | 1.446309442935e12 | -2.37526 | -9.15796 | -5.99e-4 |
| user_002 | Standing | 1.446309443e12 | -2.29862 | -9.19628 | 7.6042e-2 |
| user_002 | Standing | 1.446309443064e12 | -2.2603 | -9.2346 | -0.11556 |
| user_002 | Standing | 1.446309443129e12 | -2.22198 | -9.2346 | -0.11556 |
| user_002 | Standing | 1.446309443194e12 | -2.10702 | -9.27292 | -3.8919e-2 |
| user_002 | Standing | 1.446309443258e12 | -2.18366 | -9.15796 | 3.7722e-2 |
| user_002 | Standing | 1.446309443323e12 | -2.2603 | -9.19628 | -0.11556 |
| user_002 | Standing | 1.446309443388e12 | -2.2603 | -9.15796 | -5.99e-4 |
| user_002 | Standing | 1.446309443453e12 | -2.10702 | -9.2346 | -5.99e-4 |
| user_002 | Standing | 1.446309443517e12 | -2.03038 | -9.31124 | -3.8919e-2 |
| user_002 | Standing | 1.446309443582e12 | -2.14534 | -9.2346 | -3.8919e-2 |
| user_002 | Standing | 1.446309443647e12 | -2.29862 | -9.19628 | -5.99e-4 |
| user_002 | Standing | 1.446309443711e12 | -2.29862 | -9.19628 | 3.7722e-2 |
| user_002 | Standing | 1.446309443776e12 | -2.18366 | -9.2346 | -3.8919e-2 |
| user_002 | Standing | 1.446309443841e12 | -2.10702 | -9.19628 | 3.7722e-2 |
| user_002 | Standing | 1.446309443906e12 | -2.10702 | -9.15796 | 3.7722e-2 |
| user_002 | Standing | 1.446309443971e12 | -2.29862 | -9.15796 | 0.114362 |
| user_002 | Standing | 1.446309444036e12 | -2.03038 | -9.27292 | -5.99e-4 |
| user_002 | Standing | 1.4463094441e12 | -2.10702 | -9.19628 | 7.6042e-2 |
| user_002 | Standing | 1.446309444165e12 | -2.14534 | -9.2346 | 3.7722e-2 |
| user_002 | Standing | 1.44630944423e12 | -2.22198 | -9.19628 | 7.6042e-2 |
| user_002 | Standing | 1.446309444295e12 | -2.33694 | -9.19628 | 0.191003 |
| user_002 | Standing | 1.446309444359e12 | -2.10702 | -9.31124 | 0.114362 |
| user_002 | Standing | 1.446309444424e12 | -2.0687 | -9.2346 | -5.99e-4 |
| user_002 | Standing | 1.446309444489e12 | -2.03038 | -9.2346 | 0.114362 |
| user_002 | Standing | 1.446309444554e12 | -2.41358 | -9.00468 | 0.191003 |
| user_002 | Standing | 1.446309444618e12 | -2.37526 | -9.19628 | 0.267643 |
| user_002 | Standing | 1.446309444683e12 | -2.14534 | -9.2346 | 7.6042e-2 |
| user_002 | Standing | 1.446309444748e12 | -2.0687 | -9.27292 | 7.6042e-2 |
| user_002 | Standing | 1.446309444812e12 | -2.33694 | -9.2346 | 0.229323 |
| user_002 | Standing | 1.446309444877e12 | -2.2603 | -9.19628 | 0.229323 |
| user_002 | Standing | 1.446309444941e12 | -2.18366 | -9.2346 | 0.152682 |
| user_002 | Standing | 1.446309445007e12 | -2.22198 | -9.2346 | 0.229323 |
| user_002 | Standing | 1.446309445072e12 | -2.22198 | -9.15796 | 0.191003 |
| user_002 | Standing | 1.446309445137e12 | -2.10702 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309445202e12 | -2.10702 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446309445265e12 | -1.91542 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309445331e12 | -1.83878 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309445395e12 | -1.91542 | -9.08132 | 0.305964 |
| user_002 | Standing | 1.446309445459e12 | -1.95374 | -9.19628 | 0.152682 |
| user_002 | Standing | 1.446309445525e12 | -1.83878 | -9.34956 | 7.6042e-2 |
| user_002 | Standing | 1.44630944559e12 | -1.80046 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309445654e12 | -1.68549 | -9.38788 | 0.267643 |
| user_002 | Standing | 1.446309445719e12 | -1.80046 | -9.19628 | 0.267643 |
| user_002 | Standing | 1.446309445784e12 | -1.76214 | -9.34956 | -5.99e-4 |
| user_002 | Standing | 1.446309445849e12 | -1.91542 | -9.2346 | 0.152682 |
| user_002 | Standing | 1.446309445913e12 | -1.80046 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309445978e12 | -1.76214 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309446043e12 | -1.83878 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309446108e12 | -1.83878 | -9.2346 | 0.152682 |
| user_002 | Standing | 1.446309446173e12 | -1.8771 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309446237e12 | -1.8771 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309446302e12 | -1.8771 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309446367e12 | -1.8771 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446309446432e12 | -1.8771 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309446496e12 | -1.91542 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309446561e12 | -1.8771 | -9.2346 | 0.229323 |
| user_002 | Standing | 1.446309446625e12 | -1.83878 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309446691e12 | -1.91542 | -9.27292 | 7.6042e-2 |
| user_002 | Standing | 1.446309446755e12 | -1.8771 | -9.31124 | 0.267643 |
| user_002 | Standing | 1.44630944682e12 | -1.80046 | -9.27292 | 0.267643 |
| user_002 | Standing | 1.446309446884e12 | -1.80046 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.44630944695e12 | -1.72382 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309447014e12 | -1.68549 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309447079e12 | -1.83878 | -9.31124 | 0.267643 |
| user_002 | Standing | 1.446309447144e12 | -1.8771 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309447209e12 | -1.76214 | -9.19628 | 0.152682 |
| user_002 | Standing | 1.446309447273e12 | -1.72382 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309447338e12 | -1.83878 | -9.27292 | 0.267643 |
| user_002 | Standing | 1.446309447403e12 | -1.8771 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309447467e12 | -1.83878 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309447532e12 | -1.68549 | -9.34956 | 7.6042e-2 |
| user_002 | Standing | 1.446309447597e12 | -1.68549 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309447662e12 | -1.72382 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309447726e12 | -1.8771 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309447791e12 | -1.80046 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309447855e12 | -1.64717 | -9.31124 | 0.114362 |
| user_002 | Standing | 1.446309447921e12 | -1.60885 | -9.27292 | 0.114362 |
| user_002 | Standing | 1.446309447986e12 | -1.68549 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.44630944805e12 | -1.76214 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309448115e12 | -1.72382 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.44630944818e12 | -1.72382 | -9.27292 | 0.267643 |
| user_002 | Standing | 1.446309448245e12 | -1.64717 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309448309e12 | -1.64717 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309448374e12 | -1.72382 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309448439e12 | -1.72382 | -9.34956 | 0.267643 |
| user_002 | Standing | 1.446309448504e12 | -1.72382 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309448569e12 | -1.64717 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309448633e12 | -1.72382 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309448698e12 | -1.68549 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309448763e12 | -1.68549 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309448827e12 | -1.64717 | -9.31124 | 0.267643 |
| user_002 | Standing | 1.446309448892e12 | -1.64717 | -9.34956 | 0.229323 |
| user_002 | Standing | 1.446309448957e12 | -1.60885 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309449022e12 | -1.60885 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309449087e12 | -1.68549 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309449151e12 | -1.76214 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309449216e12 | -1.68549 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309449281e12 | -1.68549 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309449346e12 | -1.72382 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.44630944941e12 | -1.64717 | -9.34956 | 0.267643 |
| user_002 | Standing | 1.446309449475e12 | -1.76214 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.44630944954e12 | -1.64717 | -9.31124 | -5.99e-4 |
| user_002 | Standing | 1.446309449605e12 | -1.64717 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309449669e12 | -1.68549 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309449734e12 | -1.68549 | -9.31124 | 7.6042e-2 |
| user_002 | Standing | 1.446309449799e12 | -1.76214 | -9.31124 | 0.114362 |
| user_002 | Standing | 1.446309449864e12 | -1.76214 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309449928e12 | -1.72382 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309449993e12 | -1.72382 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309450057e12 | -1.72382 | -9.34956 | 0.114362 |
| user_002 | Standing | 1.446309450123e12 | -1.72382 | -9.31124 | 0.267643 |
| user_002 | Standing | 1.446309450188e12 | -1.68549 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309450252e12 | -1.83878 | -9.31124 | 0.114362 |
| user_002 | Standing | 1.446309450317e12 | -1.68549 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309450381e12 | -1.72382 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446309450446e12 | -1.68549 | -9.34956 | 3.7722e-2 |
| user_002 | Standing | 1.446309450511e12 | -1.68549 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309450576e12 | -1.76214 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309450641e12 | -1.72382 | -9.31124 | 0.152682 |
| user_002 | Standing | 1.446309450705e12 | -1.64717 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.44630945077e12 | -1.72382 | -9.34956 | 0.114362 |
| user_002 | Standing | 1.446309450835e12 | -1.83878 | -9.2346 | 0.191003 |
dataDF.count()
res5: Long = 13679
dataDF.select($"user_id").distinct().show()
+--------+
| user_id|
+--------+
|user_002|
|user_006|
|user_005|
|user_001|
|user_007|
|user_003|
|user_004|
+--------+
dataDF.select($"activity").distinct().show()
+--------+
|activity|
+--------+
| Sitting|
| Walking|
| Jumping|
|Standing|
| Jogging|
+--------+
val sDF = dataDF.sample(false,0.105, 12345L)
sDF.count
sDF: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [user_id: string, activity: string ... 4 more fields]
res8: Long = 1427
sDF.show(5)
+--------+--------+---------------+--------+---------+--------+
| user_id|activity|timeStampAsLong| x| y| z|
+--------+--------+---------------+--------+---------+--------+
|user_001| Jumping| 1446047227671|0.575403|-0.727487| 2.95007|
|user_001| Jumping| 1446047228253| 1.26517| -8.85139| 2.18366|
|user_001| Jumping| 1446047228836| 5.02056|-11.72542|-1.38013|
|user_001| Jumping| 1446047229354| 4.98224|-19.58108|0.995729|
|user_001| Jumping| 1446047229419| 5.17384| -19.2362|0.612526|
+--------+--------+---------------+--------+---------+--------+
only showing top 5 rows
sDF.show(5)
+--------+--------+---------------+--------+---------+--------+
| user_id|activity|timeStampAsLong| x| y| z|
+--------+--------+---------------+--------+---------+--------+
|user_001| Jumping| 1446047227671|0.575403|-0.727487| 2.95007|
|user_001| Jumping| 1446047228253| 1.26517| -8.85139| 2.18366|
|user_001| Jumping| 1446047228836| 5.02056|-11.72542|-1.38013|
|user_001| Jumping| 1446047229354| 4.98224|-19.58108|0.995729|
|user_001| Jumping| 1446047229419| 5.17384| -19.2362|0.612526|
+--------+--------+---------------+--------+---------+--------+
only showing top 5 rows
display(dataDF.sample(false,0.1))
| user_id | activity | timeStampAsLong | x | y | z |
|---|---|---|---|---|---|
| user_001 | Jumping | 1.446047227799e12 | 0.690364 | -3.7722e-2 | 1.72382 |
| user_001 | Jumping | 1.44604722793e12 | 1.87829 | -1.91542 | 0.880768 |
| user_001 | Jumping | 1.446047227995e12 | 1.57173 | -5.86241 | -3.75599 |
| user_001 | Jumping | 1.446047228383e12 | 0.230521 | 2.0699 | -1.41845 |
| user_001 | Jumping | 1.446047228512e12 | 1.53341 | -0.305964 | 1.41725 |
| user_001 | Jumping | 1.446047228707e12 | 7.43474 | -19.58108 | 2.95007 |
| user_001 | Jumping | 1.446047228966e12 | -1.30229 | 2.2615 | -1.26517 |
| user_001 | Jumping | 1.446047230195e12 | -2.41358 | 1.91661 | -5.99e-4 |
| user_001 | Jumping | 1.446047230584e12 | 8.00954 | -19.58108 | -0.1922 |
| user_001 | Jumping | 1.446047231296e12 | 11.61165 | -19.58108 | 4.25296 |
| user_001 | Jumping | 1.446047233562e12 | 0.996927 | -0.305964 | 0.995729 |
| user_001 | Jumping | 1.44604723421e12 | 2.79798 | -0.114362 | 0.919089 |
| user_001 | Jumping | 1.446047234468e12 | 6.89825 | -19.58108 | -4.25415 |
| user_001 | Jumping | 1.446047234663e12 | 0.805325 | 1.15021 | -0.15388 |
| user_001 | Jumping | 1.446047234986e12 | 5.36544 | -4.09967 | -0.537083 |
| user_001 | Jumping | 1.446047235115e12 | 6.93658 | -19.58108 | -2.75966 |
| user_001 | Jumping | 1.446047235633e12 | 1.18853 | -2.72014 | 1.41725 |
| user_001 | Jumping | 1.446047235763e12 | 6.55337 | -19.58108 | 0.995729 |
| user_001 | Jumping | 1.446047236281e12 | 3.56439 | -5.90073 | 0.727487 |
| user_001 | Jumping | 1.446047236346e12 | 7.05154 | -17.97163 | -1.61005 |
| user_001 | Jumping | 1.446047236669e12 | 0.11556 | -0.114362 | -1.11189 |
| user_001 | Jumping | 1.446047236798e12 | 1.80165 | 0.345482 | 0.765807 |
| user_001 | Jumping | 1.446047236928e12 | 3.48775 | -7.31858 | -0.728685 |
| user_001 | Jumping | 1.446047236992e12 | 5.32712 | -18.92964 | 0.114362 |
| user_001 | Jumping | 1.446047237057e12 | 10.34708 | -19.58108 | 2.37526 |
| user_001 | Jumping | 1.44604723764e12 | 4.98224 | -19.27452 | 1.57053 |
| user_001 | Jumping | 1.446047238287e12 | 6.32345 | -17.62674 | -0.728685 |
| user_001 | Jumping | 1.446047238676e12 | 1.95493 | 0.230521 | 1.60885 |
| user_001 | Jumping | 1.446047241849e12 | -0.919089 | 2.6447 | -3.14286 |
| user_001 | Jumping | 1.446047243725e12 | -1.07237 | 1.15021 | 0.152682 |
| user_001 | Jumping | 1.446047243984e12 | 2.87462 | -0.535886 | -0.268841 |
| user_001 | Jumping | 1.446047244243e12 | 14.86888 | -19.58108 | -1.41845 |
| user_003 | Jumping | 1.446047778293e12 | -3.63983 | -9.04299 | -2.6447 |
| user_003 | Jumping | 1.446047780171e12 | -3.37159 | -5.78577 | -3.87095 |
| user_003 | Jumping | 1.446047780365e12 | -3.86975 | -19.58108 | -5.40376 |
| user_003 | Jumping | 1.446047781725e12 | 0.230521 | 0.268841 | -0.345482 |
| user_003 | Jumping | 1.446047782244e12 | -1.45557 | -7.85507 | -1.22685 |
| user_003 | Jumping | 1.446047782373e12 | -1.64717 | -5.90073 | -2.33814 |
| user_003 | Jumping | 1.446047783151e12 | -1.14901 | -14.98264 | -4.13919 |
| user_003 | Jumping | 1.446047783409e12 | -3.67815 | -4.44456 | -2.4531 |
| user_003 | Jumping | 1.446047784057e12 | -2.8351 | -17.62674 | -5.02056 |
| user_003 | Jumping | 1.44604778451e12 | -0.344284 | -6.51385 | -2.8363 |
| user_003 | Jumping | 1.446047784575e12 | -4.02303 | -14.98264 | -2.2615 |
| user_003 | Jumping | 1.446047784769e12 | -2.4519 | 7.7239e-2 | 1.11069 |
| user_003 | Jumping | 1.446047785158e12 | -4.3296 | -19.38948 | -5.17384 |
| user_003 | Jumping | 1.446047785287e12 | -3.44823 | -7.24194 | -2.18486 |
| user_003 | Jumping | 1.44604778587e12 | -0.919089 | -4.82776 | -0.268841 |
| user_003 | Jumping | 1.446047786129e12 | -0.842448 | -0.497565 | -3.0279 |
| user_003 | Jumping | 1.446047786453e12 | -3.94639 | -5.59417 | -3.14286 |
| user_003 | Jumping | 1.44604778723e12 | -0.880768 | 0.422122 | -1.03525 |
| user_003 | Jumping | 1.446047787358e12 | -4.59784 | -19.35116 | -5.94025 |
| user_003 | Jumping | 1.446047787553e12 | -3.44823 | -6.47553 | -2.75966 |
| user_003 | Jumping | 1.446047788136e12 | -3.14167 | 2.72134 | -0.575403 |
| user_003 | Jumping | 1.446047788719e12 | -2.10702 | -7.31858 | -2.10822 |
| user_003 | Jumping | 1.446047789301e12 | 0.843646 | -0.382604 | 1.11069 |
| user_003 | Jumping | 1.446047789495e12 | -3.90807 | -17.74171 | -7.58802 |
| user_003 | Jumping | 1.446047789818e12 | -2.03038 | -6.85874 | -1.68669 |
| user_003 | Jumping | 1.446047790078e12 | -5.51753 | -19.58108 | -7.24314 |
| user_003 | Jumping | 1.446047790726e12 | -1.26397 | -8.12331 | -3.10454 |
| user_003 | Jumping | 1.446047790919e12 | -1.83878 | -3.40991 | -3.60271 |
| user_003 | Jumping | 1.446047791114e12 | -7.70178 | -19.58108 | -7.5497 |
| user_003 | Jumping | 1.446047791762e12 | -2.14534 | -9.84772 | -3.2195 |
| user_003 | Jumping | 1.446047792021e12 | -2.95007 | -5.096 | -2.52974 |
| user_003 | Jumping | 1.446047792669e12 | -2.87342 | -18.96796 | -5.86361 |
| user_003 | Jumping | 1.446047793834e12 | -3.52487 | -13.87135 | -5.63368 |
| user_003 | Jumping | 1.446047794158e12 | -1.57053 | -5.90073 | -3.2195 |
| user_002 | Standing | 1.446309439342e12 | -2.49022 | -9.19628 | -1.11189 |
| user_002 | Standing | 1.446309439373e12 | -2.75846 | -9.08132 | -1.15021 |
| user_002 | Standing | 1.446309439584e12 | -2.52854 | -8.88971 | -1.15021 |
| user_002 | Standing | 1.446309439665e12 | -2.8351 | -9.2346 | -0.920286 |
| user_002 | Standing | 1.446309439924e12 | -3.10335 | -8.77475 | -0.690364 |
| user_002 | Standing | 1.446309440021e12 | -2.91174 | -9.08132 | -1.22685 |
| user_002 | Standing | 1.446309440045e12 | -3.06503 | -9.00468 | -1.03525 |
| user_002 | Standing | 1.446309440111e12 | -3.10335 | -9.00468 | -0.881966 |
| user_002 | Standing | 1.446309440167e12 | -3.33327 | -8.85139 | -0.613724 |
| user_002 | Standing | 1.446309440239e12 | -3.14167 | -8.88971 | -0.805325 |
| user_002 | Standing | 1.446309440426e12 | -3.21831 | -8.88971 | -0.728685 |
| user_002 | Standing | 1.446309440498e12 | -3.33327 | -8.96635 | -0.805325 |
| user_002 | Standing | 1.446309440604e12 | -2.87342 | -8.85139 | -0.652044 |
| user_002 | Standing | 1.446309440717e12 | -3.06503 | -8.88971 | -0.383802 |
| user_002 | Standing | 1.446309440887e12 | -3.17999 | -8.96635 | -0.345482 |
| user_002 | Standing | 1.446309440951e12 | -3.21831 | -8.85139 | -0.575403 |
| user_002 | Standing | 1.446309440976e12 | -3.17999 | -8.81307 | -0.728685 |
| user_002 | Standing | 1.446309441017e12 | -2.91174 | -9.08132 | -0.652044 |
| user_002 | Standing | 1.446309441049e12 | -2.87342 | -9.04299 | -0.575403 |
| user_002 | Standing | 1.446309441276e12 | -3.29495 | -8.81307 | -0.498763 |
| user_002 | Standing | 1.446309441341e12 | -3.37159 | -9.08132 | -0.460442 |
| user_002 | Standing | 1.446309441349e12 | -3.44823 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.446309441453e12 | -3.29495 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441501e12 | -3.29495 | -9.00468 | -0.383802 |
| user_002 | Standing | 1.446309441591e12 | -3.40991 | -8.73643 | -0.230521 |
| user_002 | Standing | 1.446309441615e12 | -3.40991 | -8.77475 | -0.345482 |
| user_002 | Standing | 1.446309441664e12 | -3.17999 | -9.2346 | -0.460442 |
| user_002 | Standing | 1.446309441688e12 | -3.29495 | -9.19628 | -0.422122 |
| user_002 | Standing | 1.446309441793e12 | -3.17999 | -9.2346 | -0.307161 |
| user_002 | Standing | 1.446309441842e12 | -3.17999 | -8.88971 | -0.537083 |
| user_002 | Standing | 1.446309441882e12 | -3.29495 | -8.85139 | -0.345482 |
| user_002 | Standing | 1.446309441907e12 | -3.17999 | -8.77475 | -0.230521 |
| user_002 | Standing | 1.446309441915e12 | -3.17999 | -9.08132 | -3.8919e-2 |
| user_002 | Standing | 1.446309441979e12 | -3.25663 | -9.08132 | -0.268841 |
| user_002 | Standing | 1.446309442085e12 | -2.98839 | -9.00468 | -5.99e-4 |
| user_002 | Standing | 1.446309442287e12 | -2.87342 | -9.08132 | -0.268841 |
| user_002 | Standing | 1.446309442417e12 | -2.56686 | -9.19628 | -3.8919e-2 |
| user_002 | Standing | 1.446309443323e12 | -2.2603 | -9.19628 | -0.11556 |
| user_002 | Standing | 1.446309443776e12 | -2.18366 | -9.2346 | -3.8919e-2 |
| user_002 | Standing | 1.446309444036e12 | -2.03038 | -9.27292 | -5.99e-4 |
| user_002 | Standing | 1.446309444165e12 | -2.14534 | -9.2346 | 3.7722e-2 |
| user_002 | Standing | 1.446309444295e12 | -2.33694 | -9.19628 | 0.191003 |
| user_002 | Standing | 1.446309444424e12 | -2.0687 | -9.2346 | -5.99e-4 |
| user_002 | Standing | 1.44630944805e12 | -1.76214 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309448763e12 | -1.68549 | -9.27292 | 0.152682 |
| user_002 | Standing | 1.446309449151e12 | -1.76214 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309449346e12 | -1.72382 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309449993e12 | -1.72382 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446309450317e12 | -1.68549 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309450576e12 | -1.76214 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309450835e12 | -1.83878 | -9.2346 | 0.191003 |
| user_002 | Standing | 1.446309452195e12 | -1.76214 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446309452389e12 | -1.72382 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446309452907e12 | -1.72382 | -9.27292 | 0.229323 |
| user_002 | Standing | 1.446309453685e12 | -1.76214 | -9.19628 | 0.191003 |
| user_002 | Standing | 1.446309453749e12 | -1.68549 | -9.31124 | 0.229323 |
| user_002 | Standing | 1.446309454915e12 | -1.76214 | -9.27292 | 0.267643 |
| user_002 | Standing | 1.446309455109e12 | -1.80046 | -9.2346 | 0.152682 |
| user_002 | Standing | 1.446309455239e12 | -1.72382 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446309455562e12 | -1.68549 | -9.27292 | 0.114362 |
| user_005 | Standing | 1.446046599619e12 | -7.6042e-2 | -9.50284 | -0.498763 |
| user_005 | Standing | 1.446046601883e12 | 0.307161 | -9.4262 | 0.650847 |
| user_005 | Standing | 1.446046602014e12 | 0.307161 | -9.4262 | 0.612526 |
| user_005 | Standing | 1.446046602661e12 | 0.307161 | -9.34956 | 0.497565 |
| user_005 | Standing | 1.446046602855e12 | 0.345482 | -9.46452 | 0.459245 |
| user_005 | Standing | 1.446046603114e12 | 0.345482 | -9.38788 | 0.420925 |
| user_005 | Standing | 1.446046603373e12 | 0.460442 | -9.4262 | 0.497565 |
| user_005 | Standing | 1.44604660402e12 | 0.307161 | -9.34956 | 0.459245 |
| user_005 | Standing | 1.446046604473e12 | 0.307161 | -9.38788 | 0.382604 |
| user_005 | Standing | 1.446046605315e12 | 0.307161 | -9.38788 | 0.459245 |
| user_005 | Standing | 1.446046606867e12 | 0.268841 | -9.38788 | 0.459245 |
| user_005 | Standing | 1.446046607968e12 | 0.307161 | -9.46452 | 0.382604 |
| user_005 | Standing | 1.446046608033e12 | 0.307161 | -9.46452 | 0.420925 |
| user_005 | Standing | 1.446046608356e12 | 0.307161 | -9.4262 | 0.420925 |
| user_005 | Standing | 1.446046608745e12 | 0.345482 | -9.38788 | 0.420925 |
| user_005 | Standing | 1.446046609263e12 | 0.383802 | -9.38788 | 0.420925 |
| user_005 | Standing | 1.446046609586e12 | 0.307161 | -9.46452 | 0.459245 |
| user_005 | Standing | 1.446046610751e12 | 0.307161 | -9.4262 | 0.382604 |
| user_005 | Standing | 1.446046611463e12 | 0.307161 | -9.4262 | 0.344284 |
| user_005 | Standing | 1.44604661224e12 | 0.307161 | -9.4262 | 0.459245 |
| user_005 | Standing | 1.446046612823e12 | 0.345482 | -9.46452 | 0.420925 |
| user_005 | Standing | 1.446046614182e12 | 0.307161 | -9.4262 | 0.191003 |
| user_005 | Standing | 1.446046614895e12 | 0.15388 | -9.19628 | 0.382604 |
| user_005 | Standing | 1.446046615089e12 | 0.230521 | -9.4262 | 0.229323 |
| user_005 | Standing | 1.446046615736e12 | 0.383802 | -9.4262 | 0.191003 |
| user_002 | Sitting | 1.446034564845e12 | -0.842448 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034566335e12 | -0.650847 | -0.191003 | 9.4262 |
| user_002 | Sitting | 1.446034566464e12 | -0.727487 | -0.114362 | 9.38788 |
| user_002 | Sitting | 1.446034566528e12 | -0.727487 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.446034566918e12 | -0.804128 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034567047e12 | -0.765807 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034567695e12 | -0.765807 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034568082e12 | -0.689167 | -0.191003 | 9.4262 |
| user_002 | Sitting | 1.446034568601e12 | -0.765807 | -0.191003 | 9.34956 |
| user_002 | Sitting | 1.44603456873e12 | -0.727487 | -0.114362 | 9.50284 |
| user_002 | Sitting | 1.446034569701e12 | -0.804128 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034570089e12 | -0.765807 | -7.6042e-2 | 9.54116 |
| user_002 | Sitting | 1.446034570154e12 | -0.727487 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034571191e12 | -0.765807 | -0.152682 | 9.46452 |
| user_002 | Sitting | 1.446034571902e12 | -0.765807 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034572873e12 | -0.765807 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.446034574038e12 | -0.765807 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034574103e12 | -0.727487 | -0.191003 | 9.46452 |
| user_002 | Sitting | 1.446034574492e12 | -0.765807 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.446034574751e12 | -0.727487 | -0.229323 | 9.4262 |
| user_002 | Sitting | 1.446034574816e12 | -0.727487 | -0.191003 | 9.46452 |
| user_002 | Sitting | 1.446034575916e12 | -0.765807 | -0.114362 | 9.38788 |
| user_002 | Sitting | 1.446034576759e12 | -0.727487 | -0.229323 | 9.4262 |
| user_002 | Sitting | 1.446034577018e12 | -0.727487 | -0.114362 | 9.4262 |
| user_002 | Sitting | 1.446034578313e12 | -0.765807 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034578377e12 | -0.727487 | -0.191003 | 9.4262 |
| user_002 | Sitting | 1.446034578571e12 | -0.765807 | -0.191003 | 9.46452 |
| user_002 | Sitting | 1.446034578766e12 | -0.765807 | -0.191003 | 9.4262 |
| user_002 | Sitting | 1.446034580125e12 | -0.727487 | -0.152682 | 9.4262 |
| user_002 | Sitting | 1.446034581421e12 | -0.842448 | -0.152682 | 9.4262 |
| user_006 | Jogging | 1.446046855308e12 | -9.69444 | -10.49917 | -3.60271 |
| user_006 | Jogging | 1.446046856149e12 | -7.5485 | -9.19628 | -3.06622 |
| user_006 | Jogging | 1.446046857249e12 | -10.11596 | -10.15428 | -5.78697 |
| user_006 | Jogging | 1.446046857573e12 | -12.18526 | -10.76741 | -3.8919e-2 |
| user_006 | Jogging | 1.446046857962e12 | -8.12331 | -17.78002 | -3.25783 |
| user_006 | Jogging | 1.446046859581e12 | -4.55952 | -2.0687 | -2.60638 |
| user_006 | Jogging | 1.44604685971e12 | -6.51385 | -6.28393 | -7.58802 |
| user_006 | Jogging | 1.446046860164e12 | -9.08132 | -12.56846 | -6.24681 |
| user_006 | Jogging | 1.446046860293e12 | -5.70913 | -3.48655 | -4.06255 |
| user_006 | Jogging | 1.446046860423e12 | -2.8351 | -4.5212 | -4.13919 |
| user_006 | Jogging | 1.446046862495e12 | -4.36792 | -1.07237 | -1.72501 |
| user_006 | Jogging | 1.446046862948e12 | -3.37159 | -1.03405 | -1.26517 |
| user_006 | Jogging | 1.446046863789e12 | -6.09233 | -16.9753 | -2.03158 |
| user_006 | Jogging | 1.446046863854e12 | -7.39522 | -9.65612 | -5.63368 |
| user_006 | Jogging | 1.446046864372e12 | -3.75479 | -2.10702 | -2.68302 |
| user_006 | Jogging | 1.446046865149e12 | -2.41358 | -1.22565 | -0.15388 |
| user_006 | Jogging | 1.446046865472e12 | -2.18366 | 1.15021 | -1.95493 |
| user_006 | Jogging | 1.446046865861e12 | -2.68182 | -2.41358 | -2.33814 |
| user_006 | Jogging | 1.44604686612e12 | -6.82042 | -6.39889 | -3.79431 |
| user_006 | Jogging | 1.446046867867e12 | -12.37686 | -13.71807 | 7.6042e-2 |
| user_006 | Jogging | 1.446046868061e12 | -3.44823 | -6.28393 | -3.83263 |
| user_006 | Jogging | 1.446046868644e12 | -12.14694 | -10.30757 | 2.14534 |
| user_006 | Jogging | 1.446046868709e12 | -9.6178 | -10.38421 | -4.10087 |
| user_006 | Jogging | 1.446046870263e12 | -10.26924 | -8.65979 | -2.91294 |
| user_005 | Jogging | 1.446046533136e12 | -1.95374 | -10.34589 | 0.267643 |
| user_005 | Jogging | 1.446046533912e12 | 12.10982 | -19.58108 | -4.82896 |
| user_005 | Jogging | 1.446046534042e12 | 4.21583 | 11.30509 | 8.27659 |
| user_005 | Jogging | 1.446046535143e12 | -5.47921 | -0.765807 | -6.13185 |
| user_005 | Jogging | 1.446046536049e12 | 14.3324 | -19.58108 | -2.68302 |
| user_005 | Jogging | 1.446046536113e12 | -2.75846 | -16.24721 | -3.90927 |
| user_005 | Jogging | 1.446046536566e12 | 2.72134 | -6.85874 | -8.54603 |
| user_005 | Jogging | 1.446046537247e12 | 2.33814 | -11.8787 | -8.46939 |
| user_005 | Jogging | 1.446046537287e12 | -3.06503 | -0.574206 | -10.04052 |
| user_005 | Jogging | 1.446046537304e12 | -13.29655 | -1.26397 | -7.31978 |
| user_005 | Jogging | 1.446046537361e12 | -1.41725 | -7.6042e-2 | -7.58802 |
| user_005 | Jogging | 1.446046537385e12 | 0.537083 | -1.14901 | 0.957409 |
| user_005 | Jogging | 1.446046537555e12 | -2.87342 | -13.64143 | 1.64717 |
| user_005 | Jogging | 1.446046537927e12 | -3.83143 | -18.50811 | -14.17911 |
| user_005 | Jogging | 1.446046538154e12 | 3.75599 | -19.58108 | -1.11189 |
| user_005 | Jogging | 1.44604653821e12 | 11.30509 | -19.58108 | -7.85626 |
| user_005 | Jogging | 1.446046538218e12 | 7.31978 | -19.58108 | -10.04052 |
| user_005 | Jogging | 1.446046538267e12 | -2.60518 | -11.41886 | 1.83878 |
| user_005 | Jogging | 1.446046538275e12 | 0.11556 | -4.9044 | 1.64717 |
| user_005 | Jogging | 1.44604653838e12 | 0.996927 | 3.06622 | 2.2603 |
| user_005 | Jogging | 1.446046538388e12 | 7.7239e-2 | 1.45677 | 1.37893 |
| user_005 | Jogging | 1.446046538477e12 | 2.72134 | -10.80573 | 7.66346 |
| user_005 | Jogging | 1.446046538502e12 | 16.55497 | -17.01362 | 12.03198 |
| user_005 | Jogging | 1.446046538696e12 | 8.31611 | -6.74378 | -7.08986 |
| user_005 | Jogging | 1.446046538898e12 | 19.00747 | -19.58108 | -5.25048 |
| user_005 | Jogging | 1.446046538971e12 | -7.43354 | -18.58475 | 0.114362 |
| user_005 | Jogging | 1.446046539084e12 | 1.49509 | 2.95126 | 3.90807 |
| user_005 | Jogging | 1.446046539165e12 | 3.44943 | -14.3312 | -7.70298 |
| user_005 | Jogging | 1.446046539214e12 | 19.50564 | -15.0976 | -9.619 |
| user_005 | Jogging | 1.446046539229e12 | 7.89458 | -15.02096 | 10.92069 |
| user_005 | Jogging | 1.446046539238e12 | 2.98958 | -12.10862 | 10.92069 |
| user_005 | Jogging | 1.446046539521e12 | 5.99e-4 | -3.98471 | -0.690364 |
| user_005 | Jogging | 1.446046539659e12 | -4.86608 | -19.58108 | -6.13185 |
| user_005 | Jogging | 1.446046539708e12 | 2.8363 | 12.4547 | 5.44089 |
| user_005 | Jogging | 1.446046539958e12 | -0.880768 | -9.04299 | 11.6871 |
| user_005 | Jogging | 1.446046540023e12 | -7.66346 | -19.58108 | -3.60271 |
| user_005 | Jogging | 1.446046540031e12 | -4.21464 | -19.58108 | -10.27044 |
| user_005 | Jogging | 1.44604654008e12 | 11.42005 | -7.66346 | -4.67568 |
| user_005 | Jogging | 1.446046540153e12 | -12.98999 | -3.79311 | -1.91661 |
| user_005 | Jogging | 1.446046540169e12 | -6.51385 | -2.75846 | -10.50036 |
| user_005 | Jogging | 1.446046540201e12 | 3.8919e-2 | -3.37159 | -0.11556 |
| user_005 | Jogging | 1.446046540355e12 | -6.85874 | -19.58108 | -4.63736 |
| user_005 | Jogging | 1.446046540387e12 | -0.765807 | 8.50771 | 7.39522 |
| user_005 | Jogging | 1.446046540395e12 | 1.87829 | 12.60798 | 8.42987 |
| user_005 | Jogging | 1.446046540587e12 | 12.03318 | -8.85139 | 14.17792 |
| user_005 | Jogging | 1.446046540595e12 | 17.1681 | -10.84405 | 12.53014 |
| user_005 | Jogging | 1.446046540636e12 | -2.87342 | -9.77108 | 12.72175 |
| user_005 | Jogging | 1.446046540659e12 | 0.345482 | -9.84772 | 8.27659 |
| user_005 | Jogging | 1.446046540766e12 | 9.54236 | -8.81307 | -9.12083 |
| user_005 | Jogging | 1.446046540772e12 | 9.84892 | -3.83143 | -8.16283 |
| user_005 | Jogging | 1.446046540813e12 | -18.00995 | -3.79311 | -9.15915 |
| user_005 | Jogging | 1.446046540838e12 | -10.72909 | -3.25663 | -3.2195 |
| user_005 | Jogging | 1.446046540976e12 | 3.2195 | -19.58108 | -6.9749 |
| user_005 | Jogging | 1.446046541105e12 | 0.690364 | 15.78857 | 11.91702 |
| user_005 | Jogging | 1.446046541332e12 | -2.10702 | -6.82042 | 11.84038 |
| user_005 | Jogging | 1.446046541348e12 | -1.22565 | -12.4535 | 10.69077 |
| user_005 | Jogging | 1.446046541445e12 | 15.40536 | -4.55952 | -2.8363 |
| user_005 | Jogging | 1.446046541477e12 | -9.92436 | -2.8351 | -14.29407 |
| user_005 | Jogging | 1.44604654155e12 | -0.497565 | -1.80046 | -0.920286 |
| user_005 | Jogging | 1.446046541638e12 | 6.43841 | -19.58108 | -5.78697 |
| user_005 | Jogging | 1.446046541841e12 | 1.83997 | 1.61005 | 0.612526 |
| user_005 | Jogging | 1.446046541906e12 | 8.69931 | -19.58108 | -17.62794 |
| user_005 | Jogging | 1.446046542011e12 | -1.95374 | -5.74745 | 11.57214 |
| user_005 | Jogging | 1.446046542116e12 | 10.0022 | -9.04299 | -7.28146 |
| user_005 | Jogging | 1.446046542189e12 | -14.1396 | -4.7128 | -4.82896 |
| user_005 | Jogging | 1.446046542262e12 | -1.45557 | -4.02303 | -2.56806 |
| user_005 | Jogging | 1.446046542295e12 | -5.67081 | -10.001 | 5.01936 |
| user_005 | Jogging | 1.446046542441e12 | -0.995729 | -3.17999 | 10.26924 |
| user_005 | Jogging | 1.446046542651e12 | 1.53341 | -16.82202 | 19.08292 |
| user_005 | Jogging | 1.446046542659e12 | 0.690364 | -9.2346 | 11.34221 |
| user_005 | Jogging | 1.446046542699e12 | -0.382604 | -7.24194 | 6.59049 |
| user_005 | Jogging | 1.446046542715e12 | 1.91661 | -16.70706 | 2.72014 |
| user_005 | Jogging | 1.446046542739e12 | 1.07357 | -19.58108 | -14.63896 |
| user_005 | Jogging | 1.446046542812e12 | -0.420925 | -7.31858 | -8.85259 |
| user_005 | Jogging | 1.44604654282e12 | 0.805325 | -5.74745 | -9.15915 |
| user_005 | Jogging | 1.446046542853e12 | -3.21831 | 1.95493 | -1.26517 |
| user_005 | Jogging | 1.446046543063e12 | 6.01689 | -19.54276 | -6.05521 |
| user_005 | Jogging | 1.446046543071e12 | 2.60638 | -17.93331 | -3.0279 |
| user_005 | Jogging | 1.446046543136e12 | 0.805325 | -0.305964 | 7.66346 |
| user_005 | Jogging | 1.446046543209e12 | 2.72134 | 9.12083 | 1.30229 |
| user_005 | Jogging | 1.446046543233e12 | 4.48408 | 1.22685 | -3.37279 |
| user_005 | Jogging | 1.44604654333e12 | 3.44943 | -19.58108 | 16.4005 |
| user_005 | Jogging | 1.446046543379e12 | -1.57053 | -19.58108 | 2.29862 |
| user_005 | Jogging | 1.446046543444e12 | 8.69931 | -5.59417 | -9.84892 |
| user_005 | Jogging | 1.446046543573e12 | -2.10702 | -1.22565 | -0.613724 |
| user_005 | Jogging | 1.446046543621e12 | 0.11556 | -8.12331 | 4.25296 |
| user_005 | Jogging | 1.446046543826e12 | 11.49669 | -3.02671 | -6.66833 |
| user_005 | Jogging | 1.446046543859e12 | 8.31611 | 1.41845 | -10.0022 |
| user_005 | Jogging | 1.446046544045e12 | -1.14901 | -16.20889 | -5.74865 |
| user_005 | Jogging | 1.446046544118e12 | -1.37893 | -15.32753 | -6.9749 |
| user_005 | Jogging | 1.446046544126e12 | -0.612526 | -14.75272 | -7.39642 |
| user_005 | Jogging | 1.446046544295e12 | 1.07357 | -7.3569 | -1.53341 |
| user_005 | Jogging | 1.44604654432e12 | -0.689167 | -13.29655 | -1.30349 |
| user_005 | Jogging | 1.446046544352e12 | -5.70913 | -19.58108 | -6.09353 |
| user_005 | Jogging | 1.446046544465e12 | 2.6447 | 0.422122 | 3.52487 |
| user_005 | Jogging | 1.446046544498e12 | 4.86728 | 2.91294 | 3.63983 |
| user_005 | Jogging | 1.446046544554e12 | 2.72134 | 0.268841 | 0.765807 |
| user_005 | Jogging | 1.44604654466e12 | -2.10702 | -9.54116 | 10.11596 |
| user_005 | Jogging | 1.446046544668e12 | 1.18853 | -14.86768 | 12.14694 |
| user_005 | Jogging | 1.446046544684e12 | 4.56072 | -19.58108 | 2.6435 |
| user_005 | Jogging | 1.446046544692e12 | 4.86728 | -19.58108 | 1.8771 |
| user_005 | Jogging | 1.446046544757e12 | -6.13065 | -19.58108 | 4.9044 |
| user_005 | Jogging | 1.446046544838e12 | 1.64837 | -12.53014 | -11.80326 |
| user_005 | Jogging | 1.446046544894e12 | -4.78944 | -2.0687 | -8.85259 |
| user_005 | Jogging | 1.446046545121e12 | -2.18366 | -11.15061 | -3.8919e-2 |
| user_005 | Jogging | 1.446046545202e12 | 3.0279 | 5.40376 | 4.5212 |
| user_005 | Jogging | 1.446046545267e12 | -0.344284 | 0.920286 | -1.11189 |
| user_005 | Jogging | 1.44604654538e12 | 2.91294 | -12.03198 | 4.75112 |
| user_005 | Jogging | 1.446046545485e12 | 1.61005 | -18.66139 | -12.2631 |
| user_005 | Jogging | 1.446046545639e12 | 3.8919e-2 | -4.02303 | -6.66833 |
| user_005 | Jogging | 1.446046545704e12 | 2.03158 | -19.12124 | 2.10702 |
| user_005 | Jogging | 1.446046545833e12 | -1.18733 | -4.55952 | 4.09967 |
| user_005 | Jogging | 1.446046545841e12 | 1.07357 | 1.03525 | 4.138 |
| user_005 | Jogging | 1.446046545866e12 | 5.36544 | 15.17544 | 7.39522 |
| user_005 | Jogging | 1.446046546052e12 | 7.43474 | -7.70178 | 6.82042 |
| user_005 | Jogging | 1.446046546068e12 | -0.689167 | -9.46452 | 11.07397 |
| user_005 | Jogging | 1.446046546229e12 | 6.32345 | -4.78944 | -6.13185 |
| user_005 | Jogging | 1.446046546368e12 | 0.881966 | -6.32225 | 1.49389 |
| user_005 | Jogging | 1.446046546392e12 | -0.574206 | -13.44983 | 3.75479 |
| user_005 | Jogging | 1.4460465464e12 | 1.11189 | -17.74171 | 1.83878 |
| user_005 | Jogging | 1.446046546424e12 | 14.29407 | -19.58108 | -5.74865 |
| user_005 | Jogging | 1.446046546457e12 | 16.05681 | -19.58108 | -2.60638 |
| user_005 | Jogging | 1.446046546554e12 | 3.79431 | 7.7413 | 8.12331 |
| user_005 | Jogging | 1.446046546683e12 | -2.79678 | -15.94065 | -19.54396 |
| user_005 | Jogging | 1.44604654682e12 | -0.191003 | -19.50444 | 6.09233 |
| user_005 | Jogging | 1.446046546861e12 | -3.44823 | -19.58108 | -6.70665 |
| user_005 | Jogging | 1.446046546877e12 | 1.22685 | -19.58108 | -15.2904 |
| user_005 | Jogging | 1.446046546893e12 | 1.03525 | -15.74905 | -9.54236 |
| user_005 | Jogging | 1.44604654695e12 | 1.49509 | 1.64837 | -8.62267 |
| user_005 | Jogging | 1.446046547015e12 | -5.93905 | 0.728685 | -11.68829 |
| user_005 | Jogging | 1.446046548148e12 | 7.5497 | -12.30022 | 3.67815 |
| user_005 | Jogging | 1.446046549119e12 | -7.66346 | -0.229323 | -6.59169 |
| user_005 | Jogging | 1.446046549378e12 | -2.52854 | -6.9737 | 2.10702 |
| user_005 | Jogging | 1.446046549572e12 | 8.35443 | -16.13225 | 1.64717 |
| user_005 | Jogging | 1.446046549702e12 | -2.49022 | -19.35116 | 1.30229 |
| user_006 | Standing | 1.446046930244e12 | -4.06135 | -8.62147 | -0.996927 |
| user_006 | Standing | 1.446046930349e12 | -4.25296 | -8.69811 | -0.920286 |
| user_006 | Standing | 1.446046930381e12 | -4.09967 | -8.50651 | -0.958607 |
| user_006 | Standing | 1.446046930421e12 | -4.29128 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046930591e12 | -4.17632 | -8.65979 | -1.03525 |
| user_006 | Standing | 1.446046930633e12 | -3.98471 | -8.62147 | -1.18853 |
| user_006 | Standing | 1.446046930649e12 | -4.17632 | -8.46819 | -0.920286 |
| user_006 | Standing | 1.446046930746e12 | -4.3296 | -8.58315 | -0.920286 |
| user_006 | Standing | 1.446046930883e12 | -4.17632 | -8.42987 | -1.07357 |
| user_006 | Standing | 1.446046931005e12 | -4.25296 | -8.65979 | -1.11189 |
| user_006 | Standing | 1.446046931045e12 | -4.02303 | -8.65979 | -0.920286 |
| user_006 | Standing | 1.446046931053e12 | -4.09967 | -8.65979 | -0.920286 |
| user_006 | Standing | 1.446046931078e12 | -3.98471 | -8.58315 | -1.07357 |
| user_006 | Standing | 1.446046931401e12 | -4.25296 | -8.62147 | -1.03525 |
| user_006 | Standing | 1.446046931482e12 | -4.36792 | -8.54483 | -1.11189 |
| user_006 | Standing | 1.446046931499e12 | -4.25296 | -8.65979 | -0.881966 |
| user_006 | Standing | 1.446046931563e12 | -4.138 | -8.58315 | -0.843646 |
| user_006 | Standing | 1.446046931595e12 | -4.36792 | -8.58315 | -1.03525 |
| user_006 | Standing | 1.446046931628e12 | -4.06135 | -8.50651 | -0.881966 |
| user_006 | Standing | 1.446046931733e12 | -4.17632 | -8.58315 | -0.958607 |
| user_006 | Standing | 1.446046931855e12 | -4.25296 | -8.69811 | -0.958607 |
| user_006 | Standing | 1.446046931927e12 | -4.17632 | -8.62147 | -0.767005 |
| user_006 | Standing | 1.446046932178e12 | -4.138 | -8.69811 | -0.996927 |
| user_006 | Standing | 1.446046932186e12 | -4.25296 | -8.69811 | -0.996927 |
| user_006 | Standing | 1.446046932235e12 | -4.138 | -8.46819 | -0.920286 |
| user_006 | Standing | 1.446046932242e12 | -4.09967 | -8.69811 | -0.958607 |
| user_006 | Standing | 1.446046932364e12 | -4.09967 | -8.69811 | -0.920286 |
| user_006 | Standing | 1.446046932478e12 | -4.17632 | -8.65979 | -0.881966 |
| user_006 | Standing | 1.446046932575e12 | -4.29128 | -8.73643 | -0.920286 |
| user_006 | Standing | 1.44604693264e12 | -4.17632 | -8.77475 | -0.843646 |
| user_006 | Standing | 1.446046932818e12 | -4.25296 | -8.58315 | -0.881966 |
| user_006 | Standing | 1.446046932826e12 | -4.25296 | -8.54483 | -0.996927 |
| user_006 | Standing | 1.446046932988e12 | -4.09967 | -8.50651 | -1.07357 |
| user_006 | Standing | 1.446046933012e12 | -4.138 | -8.69811 | -1.03525 |
| user_006 | Standing | 1.44604693302e12 | -4.09967 | -8.62147 | -0.958607 |
| user_006 | Standing | 1.446046933109e12 | -4.3296 | -8.62147 | -0.958607 |
| user_006 | Standing | 1.446046933133e12 | -4.29128 | -8.69811 | -0.958607 |
| user_006 | Standing | 1.446046933287e12 | -4.25296 | -8.73643 | -0.920286 |
| user_006 | Standing | 1.446046933303e12 | -4.25296 | -8.58315 | -0.843646 |
| user_006 | Standing | 1.446046935092e12 | -4.138 | -8.58315 | -0.996927 |
| user_006 | Standing | 1.446046935156e12 | -4.21464 | -8.69811 | -0.996927 |
| user_006 | Standing | 1.446046935286e12 | -4.21464 | -8.65979 | -1.03525 |
| user_006 | Standing | 1.446046936452e12 | -4.17632 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046936581e12 | -4.17632 | -8.65979 | -0.881966 |
| user_006 | Standing | 1.446046936776e12 | -4.17632 | -8.69811 | -0.920286 |
| user_006 | Standing | 1.446046937359e12 | -4.17632 | -8.62147 | -0.996927 |
| user_006 | Standing | 1.446046937941e12 | -4.21464 | -8.65979 | -0.958607 |
| user_006 | Standing | 1.446046938459e12 | -4.29128 | -8.62147 | -1.07357 |
| user_006 | Standing | 1.446046938586e12 | -4.5212 | -8.77475 | -1.07357 |
| user_006 | Standing | 1.446046938708e12 | -4.25296 | -8.54483 | -0.996927 |
| user_006 | Standing | 1.446046938829e12 | -4.21464 | -8.58315 | -1.26517 |
| user_006 | Standing | 1.446046938861e12 | -4.17632 | -8.54483 | -1.03525 |
| user_006 | Standing | 1.446046938886e12 | -4.29128 | -8.58315 | -1.03525 |
| user_006 | Standing | 1.446046938893e12 | -4.17632 | -8.85139 | -0.881966 |
| user_006 | Standing | 1.446046939355e12 | -4.3296 | -8.69811 | -1.15021 |
| user_006 | Standing | 1.446046939363e12 | -4.25296 | -8.46819 | -0.881966 |
| user_006 | Standing | 1.446046939395e12 | -4.21464 | -8.65979 | -0.958607 |
| user_006 | Standing | 1.446046939412e12 | -4.29128 | -8.54483 | -1.03525 |
| user_006 | Standing | 1.446046939428e12 | -4.17632 | -8.62147 | -0.958607 |
| user_006 | Standing | 1.446046939468e12 | -4.138 | -8.62147 | -0.920286 |
| user_006 | Standing | 1.446046939533e12 | -4.21464 | -8.58315 | -1.11189 |
| user_006 | Standing | 1.446046939557e12 | -4.36792 | -8.54483 | -0.805325 |
| user_006 | Standing | 1.446046939679e12 | -4.138 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046939719e12 | -4.21464 | -8.65979 | -1.18853 |
| user_006 | Standing | 1.446046939897e12 | -4.21464 | -8.69811 | -1.03525 |
| user_006 | Standing | 1.446046940018e12 | -4.21464 | -8.65979 | -1.11189 |
| user_006 | Standing | 1.446046940051e12 | -4.138 | -8.77475 | -0.996927 |
| user_006 | Standing | 1.446046940075e12 | -4.17632 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046940172e12 | -4.138 | -8.69811 | -1.03525 |
| user_006 | Standing | 1.446046940205e12 | -4.21464 | -8.69811 | -1.07357 |
| user_006 | Standing | 1.446046940221e12 | -4.09967 | -8.62147 | -1.03525 |
| user_006 | Standing | 1.446046940277e12 | -4.17632 | -8.65979 | -1.03525 |
| user_006 | Standing | 1.446046940447e12 | -4.138 | -8.69811 | -1.07357 |
| user_006 | Standing | 1.446046940471e12 | -4.25296 | -8.62147 | -0.958607 |
| user_006 | Standing | 1.446046940479e12 | -4.21464 | -8.65979 | -1.03525 |
| user_006 | Standing | 1.446046940585e12 | -4.3296 | -8.54483 | -1.03525 |
| user_006 | Standing | 1.44604694073e12 | -4.21464 | -8.50651 | -0.958607 |
| user_006 | Standing | 1.446046940851e12 | -4.21464 | -8.62147 | -1.03525 |
| user_006 | Standing | 1.446046940868e12 | -4.09967 | -8.77475 | -1.07357 |
| user_006 | Standing | 1.446046940932e12 | -4.21464 | -8.73643 | -0.958607 |
| user_006 | Standing | 1.446046940981e12 | -4.17632 | -8.65979 | -1.15021 |
| user_006 | Standing | 1.446046941005e12 | -4.40624 | -8.77475 | -1.07357 |
| user_006 | Standing | 1.446046941102e12 | -4.40624 | -8.54483 | -1.22685 |
| user_006 | Standing | 1.446046941135e12 | -4.138 | -8.42987 | -1.03525 |
| user_006 | Standing | 1.446046941159e12 | -4.09967 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046941191e12 | -4.06135 | -8.65979 | -1.07357 |
| user_006 | Standing | 1.446046941224e12 | -4.138 | -8.88971 | -1.11189 |
| user_006 | Standing | 1.446046941264e12 | -4.25296 | -8.73643 | -1.03525 |
| user_006 | Standing | 1.446046941305e12 | -4.17632 | -8.73643 | -1.11189 |
| user_006 | Standing | 1.446046941329e12 | -4.25296 | -8.58315 | -1.18853 |
| user_006 | Standing | 1.446046941337e12 | -4.3296 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.446046941426e12 | -4.21464 | -8.58315 | -0.920286 |
| user_006 | Standing | 1.446046941458e12 | -4.138 | -8.77475 | -1.15021 |
| user_006 | Standing | 1.446046941482e12 | -4.25296 | -8.65979 | -1.03525 |
| user_006 | Standing | 1.446046941547e12 | -4.3296 | -8.65979 | -1.18853 |
| user_006 | Standing | 1.446046941636e12 | -4.21464 | -8.58315 | -0.996927 |
| user_006 | Standing | 1.44604694175e12 | -4.36792 | -8.58315 | -1.22685 |
| user_006 | Standing | 1.446046942081e12 | -4.17632 | -8.58315 | -0.996927 |
| user_006 | Standing | 1.446046942534e12 | -4.17632 | -8.62147 | -1.03525 |
| user_006 | Standing | 1.446046942923e12 | -4.25296 | -8.62147 | -0.996927 |
| user_006 | Standing | 1.446046943118e12 | -4.17632 | -8.62147 | -0.996927 |
| user_006 | Standing | 1.446046944606e12 | -4.138 | -8.65979 | -0.996927 |
| user_006 | Standing | 1.44604694467e12 | -4.138 | -8.73643 | -0.958607 |
| user_002 | Standing | 1.446034733566e12 | -1.49389 | -9.31124 | 0.191003 |
| user_002 | Standing | 1.446034734085e12 | -1.37893 | -9.38788 | 0.229323 |
| user_002 | Standing | 1.446034734797e12 | -1.53221 | -9.34956 | 0.229323 |
| user_002 | Standing | 1.446034735056e12 | -1.37893 | -9.38788 | 3.7722e-2 |
| user_002 | Standing | 1.446034735508e12 | -1.37893 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446034735703e12 | -1.45557 | -9.4262 | 0.267643 |
| user_002 | Standing | 1.446034736285e12 | -1.37893 | -9.34956 | 0.191003 |
| user_002 | Standing | 1.446034737257e12 | -1.49389 | -9.38788 | 0.267643 |
| user_002 | Standing | 1.446034738228e12 | -1.37893 | -9.4262 | 0.152682 |
| user_002 | Standing | 1.446034738876e12 | -1.37893 | -9.50284 | 0.229323 |
| user_002 | Standing | 1.446034739004e12 | -1.45557 | -9.38788 | 0.191003 |
| user_002 | Standing | 1.446034739134e12 | -1.30229 | -9.4262 | 0.191003 |
| user_002 | Standing | 1.446034739264e12 | -1.41725 | -9.4262 | 0.191003 |
| user_002 | Standing | 1.446034739523e12 | -1.34061 | -9.54116 | 0.229323 |
| user_002 | Standing | 1.44603474043e12 | -1.34061 | -9.50284 | 0.191003 |
| user_002 | Standing | 1.446034741335e12 | -1.34061 | -9.46452 | 0.114362 |
| user_002 | Standing | 1.446034742123e12 | -1.37893 | -9.46452 | 3.7722e-2 |
| user_002 | Standing | 1.446034742237e12 | -1.37893 | -9.54116 | 0.191003 |
| user_002 | Standing | 1.44603474231e12 | -1.49389 | -9.38788 | 0.191003 |
| user_002 | Standing | 1.446034742334e12 | -1.45557 | -9.27292 | 0.191003 |
| user_002 | Standing | 1.446034742383e12 | -1.34061 | -9.4262 | 7.6042e-2 |
| user_002 | Standing | 1.446034742424e12 | -1.49389 | -9.54116 | 0.191003 |
| user_002 | Standing | 1.446034742488e12 | -1.37893 | -9.38788 | 0.152682 |
| user_002 | Standing | 1.446034742697e12 | -1.41725 | -9.54116 | 0.229323 |
| user_002 | Standing | 1.446034742916e12 | -1.26397 | -9.4262 | 0.114362 |
| user_002 | Standing | 1.446034742932e12 | -0.995729 | -9.34956 | 0.344284 |
| user_002 | Standing | 1.446034743369e12 | -1.37893 | -9.57948 | 0.114362 |
| user_002 | Standing | 1.446034743386e12 | -1.26397 | -9.38788 | 0.344284 |
| user_002 | Standing | 1.446034743434e12 | -1.18733 | -9.46452 | 0.344284 |
| user_002 | Standing | 1.446034743499e12 | -1.41725 | -9.31124 | 0.344284 |
| user_002 | Standing | 1.44603474371e12 | -1.26397 | -9.34956 | 0.267643 |
| user_002 | Standing | 1.44603474375e12 | -1.22565 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446034743791e12 | -1.22565 | -9.46452 | 0.229323 |
| user_002 | Standing | 1.446034743798e12 | -1.18733 | -9.34956 | 7.6042e-2 |
| user_002 | Standing | 1.446034743823e12 | -1.45557 | -9.27292 | 3.7722e-2 |
| user_002 | Standing | 1.446034743863e12 | -1.26397 | -9.4262 | 0.191003 |
| user_002 | Standing | 1.446034744001e12 | -1.30229 | -9.57948 | 0.267643 |
| user_002 | Standing | 1.446034744025e12 | -1.18733 | -9.4262 | 0.305964 |
| user_002 | Standing | 1.446034744074e12 | -1.22565 | -9.46452 | 0.305964 |
| user_002 | Standing | 1.446034744098e12 | -1.22565 | -9.46452 | 0.305964 |
| user_002 | Standing | 1.446034744171e12 | -1.18733 | -9.54116 | 0.305964 |
| user_002 | Standing | 1.446034744203e12 | -1.18733 | -9.27292 | 0.267643 |
| user_002 | Standing | 1.446034744211e12 | -1.22565 | -9.38788 | 0.229323 |
| user_002 | Standing | 1.446034744236e12 | -1.22565 | -9.27292 | 0.344284 |
| user_002 | Standing | 1.446034744325e12 | -1.34061 | -9.4262 | 0.305964 |
| user_002 | Standing | 1.446034744365e12 | -1.22565 | -9.38788 | 0.229323 |
| user_002 | Standing | 1.446034744551e12 | -1.41725 | -9.34956 | 0.152682 |
| user_002 | Standing | 1.446034744559e12 | -1.41725 | -9.38788 | 0.459245 |
| user_002 | Standing | 1.446034744632e12 | -1.30229 | -9.46452 | 0.420925 |
| user_002 | Standing | 1.446034744665e12 | -1.11069 | -9.34956 | 0.420925 |
| user_002 | Standing | 1.446034744738e12 | -1.30229 | -9.4262 | 0.382604 |
| user_002 | Standing | 1.446034744964e12 | -1.26397 | -9.4262 | 0.229323 |
| user_002 | Standing | 1.446034744981e12 | -1.30229 | -9.38788 | -3.8919e-2 |
| user_002 | Standing | 1.446034745045e12 | -1.22565 | -9.6178 | 0.305964 |
| user_002 | Standing | 1.446034745191e12 | -0.880768 | -9.46452 | 0.344284 |
| user_002 | Standing | 1.446034745215e12 | -1.26397 | -9.57948 | 0.114362 |
| user_002 | Standing | 1.446034745345e12 | -1.37893 | -9.46452 | 0.420925 |
| user_002 | Standing | 1.446034745369e12 | -1.14901 | -9.46452 | 0.305964 |
| user_002 | Standing | 1.446034745385e12 | -1.30229 | -9.46452 | 0.152682 |
| user_002 | Standing | 1.44603474545e12 | -1.18733 | -9.38788 | 0.152682 |
| user_002 | Standing | 1.446034745482e12 | -1.41725 | -9.54116 | 0.229323 |
| user_002 | Standing | 1.44603474549e12 | -1.26397 | -9.65612 | 0.420925 |
| user_002 | Standing | 1.446034745507e12 | -1.41725 | -9.46452 | 0.114362 |
| user_002 | Standing | 1.446034745636e12 | -1.30229 | -9.38788 | 0.267643 |
| user_002 | Standing | 1.446034745685e12 | -1.45557 | -9.4262 | 0.191003 |
| user_002 | Standing | 1.446034745741e12 | -1.18733 | -9.46452 | 0.305964 |
| user_002 | Standing | 1.446034745863e12 | -1.26397 | -9.57948 | 0.152682 |
| user_002 | Standing | 1.446034746082e12 | -1.30229 | -9.38788 | 0.305964 |
| user_002 | Standing | 1.446034746858e12 | -1.34061 | -9.4262 | 0.305964 |
| user_002 | Standing | 1.446034747311e12 | -1.30229 | -9.4262 | 0.191003 |
| user_002 | Standing | 1.446034748736e12 | -1.18733 | -9.46452 | 0.267643 |
| user_002 | Standing | 1.446034749383e12 | -1.18733 | -9.46452 | 0.267643 |
| user_002 | Standing | 1.446146630001e12 | -4.02303 | -8.73643 | -0.920286 |
| user_002 | Standing | 1.446146631036e12 | -3.83143 | -8.69811 | -1.11189 |
| user_002 | Standing | 1.446146631166e12 | -4.09967 | -8.62147 | -1.03525 |
| user_002 | Standing | 1.446146632655e12 | -4.59784 | -7.70178 | -2.22318 |
| user_002 | Standing | 1.446146633757e12 | -0.267643 | -10.65245 | 1.07237 |
| user_002 | Standing | 1.446146634987e12 | -0.267643 | -9.92436 | 0.689167 |
| user_002 | Standing | 1.446146635375e12 | -1.60885 | -8.50651 | 1.57053 |
| user_002 | Standing | 1.446146635505e12 | -1.49389 | -9.11964 | 2.41358 |
| user_002 | Standing | 1.446146635635e12 | -1.99206 | -9.11964 | 1.14901 |
| user_002 | Standing | 1.446146635828e12 | -1.26397 | -9.19628 | 1.49389 |
| user_002 | Standing | 1.446146637577e12 | -1.45557 | -9.2346 | 1.57053 |
| user_002 | Standing | 1.44614663803e12 | -0.382604 | -10.11596 | 1.57053 |
| user_002 | Standing | 1.446146640036e12 | -0.229323 | -9.08132 | 2.03038 |
| user_002 | Standing | 1.446146641072e12 | -0.114362 | -9.00468 | 2.10702 |
| user_002 | Standing | 1.446146641267e12 | 5.99e-4 | -9.15796 | 2.14534 |
| user_002 | Standing | 1.446146642043e12 | -7.6042e-2 | -9.15796 | 2.22198 |
| user_002 | Standing | 1.446146644825e12 | 3.37279 | -8.62147 | 1.45557 |
| user_002 | Standing | 1.446146645409e12 | 3.83263 | -8.54483 | 1.30229 |
| user_002 | Walking | 1.446034820068e12 | -3.98471 | -8.81307 | 2.49022 |
| user_002 | Walking | 1.446034821104e12 | -4.138 | -7.85507 | 1.72382 |
| user_002 | Walking | 1.446034821558e12 | -3.29495 | -9.34956 | 2.75846 |
| user_002 | Walking | 1.446034822592e12 | -4.21464 | -8.27659 | 2.8351 |
| user_002 | Walking | 1.446034822787e12 | -4.94272 | -8.65979 | 1.22565 |
| user_002 | Walking | 1.446034823953e12 | -5.36425 | -9.50284 | 2.14534 |
| user_002 | Walking | 1.446034824277e12 | -3.98471 | -7.05034 | 2.10702 |
| user_002 | Walking | 1.446034824406e12 | -3.44823 | -6.28393 | -3.8919e-2 |
| user_002 | Walking | 1.446034824665e12 | -5.55585 | -8.85139 | 1.45557 |
| user_002 | Walking | 1.446034826609e12 | -4.55952 | -11.18893 | 1.18733 |
| user_002 | Walking | 1.446034827386e12 | -3.98471 | -7.89339 | 2.18366 |
| user_002 | Walking | 1.446034827451e12 | -3.75479 | -8.50651 | 1.91542 |
| user_002 | Walking | 1.44603482758e12 | -5.74745 | -7.58682 | 2.79678 |
| user_002 | Walking | 1.446034828098e12 | -4.44456 | -6.20729 | -1.76333 |
| user_002 | Walking | 1.446034828746e12 | -4.98104 | -7.39522 | 2.0687 |
| user_002 | Walking | 1.446034828811e12 | -5.90073 | -7.7401 | 2.33694 |
| user_002 | Walking | 1.446034829458e12 | -7.77842 | -11.53382 | -3.10454 |
| user_002 | Walking | 1.446034829523e12 | -6.36057 | -9.27292 | 0.114362 |
| user_002 | Walking | 1.44603483043e12 | -3.14167 | -6.39889 | 0.344284 |
| user_002 | Walking | 1.44603483056e12 | -3.29495 | -6.28393 | -1.03525 |
| user_002 | Walking | 1.446034832438e12 | -5.32592 | -7.3569 | 1.83878 |
| user_002 | Walking | 1.446034834769e12 | -4.29128 | -8.00835 | 1.57053 |
| user_002 | Walking | 1.446034836e12 | -4.48288 | -7.70178 | 1.60885 |
| user_005 | Walking | 1.446046499672e12 | -4.78944 | -11.30389 | -6.28513 |
| user_005 | Walking | 1.446046499736e12 | -1.91542 | -13.948 | -0.230521 |
| user_005 | Walking | 1.446046500255e12 | -5.36425 | -12.49182 | -1.45677 |
| user_005 | Walking | 1.446046501161e12 | -2.4519 | -8.39155 | 0.804128 |
| user_005 | Walking | 1.446046501679e12 | -3.02671 | -5.40257 | 0.612526 |
| user_005 | Walking | 1.446046501873e12 | 1.22685 | -15.25089 | -12.30142 |
| user_005 | Walking | 1.446046502197e12 | -0.842448 | -7.24194 | -0.15388 |
| user_005 | Walking | 1.446046502261e12 | -0.919089 | -8.16163 | 0.880768 |
| user_005 | Walking | 1.446046502455e12 | -4.7128 | -11.18893 | -0.652044 |
| user_005 | Walking | 1.446046502908e12 | 0.767005 | -11.45717 | -10.65365 |
| user_005 | Walking | 1.446046503103e12 | 3.8919e-2 | -5.86241 | 3.60151 |
| user_005 | Walking | 1.446046503362e12 | -1.53221 | -9.00468 | -1.83997 |
| user_005 | Walking | 1.446046504528e12 | -3.56319 | -11.18893 | -0.958607 |
| user_005 | Walking | 1.446046505304e12 | -3.60151 | -10.92069 | 2.8351 |
| user_005 | Walking | 1.44604650757e12 | -3.94639 | -12.14694 | 7.6042e-2 |
| user_005 | Walking | 1.446046507635e12 | 0.996927 | -7.47186 | -1.41845 |
| user_005 | Walking | 1.446046508023e12 | 0.422122 | -11.99366 | 0.459245 |
| user_005 | Walking | 1.446046508476e12 | 2.87462 | -10.15428 | -8.1245 |
| user_005 | Walking | 1.44604650867e12 | -3.17999 | -11.07397 | 0.727487 |
| user_005 | Walking | 1.446046508864e12 | -1.18733 | -8.35323 | -1.03525 |
| user_005 | Walking | 1.446046508928e12 | -2.29862 | -9.65612 | -0.805325 |
| user_005 | Walking | 1.446046509123e12 | 1.34181 | -11.15061 | 0.344284 |
| user_005 | Walking | 1.446046509512e12 | 1.45677 | -8.50651 | -6.85994 |
| user_005 | Walking | 1.446046510224e12 | -7.6042e-2 | -11.45717 | -7.7239e-2 |
| user_005 | Walking | 1.446046510288e12 | 1.22685 | -11.64878 | 1.22565 |
| user_005 | Walking | 1.446046510676e12 | 2.68302 | -11.4955 | -9.46572 |
| user_005 | Walking | 1.446046511648e12 | -2.03038 | -6.28393 | -0.307161 |
| user_005 | Walking | 1.446046511971e12 | 5.21216 | -13.44983 | -0.690364 |
| user_005 | Walking | 1.446046512618e12 | 2.68302 | -13.10495 | 0.689167 |
| user_005 | Walking | 1.446046513653e12 | -0.191003 | -11.26557 | 0.574206 |
| user_005 | Walking | 1.446046514171e12 | 4.5224 | -8.77475 | -7.70298 |
| user_005 | Walking | 1.446046514883e12 | 2.2615 | -12.4535 | 0.574206 |
| user_005 | Walking | 1.446046515336e12 | 3.64103 | -8.73643 | -6.20849 |
| user_005 | Walking | 1.446046515789e12 | -2.03038 | -10.84405 | -0.958607 |
| user_005 | Walking | 1.446046515919e12 | 0.767005 | -11.15061 | 0.191003 |
| user_005 | Walking | 1.446046516049e12 | 3.48775 | -14.21624 | 0.880768 |
| user_005 | Walking | 1.446046516437e12 | 5.17384 | -9.57948 | -8.66099 |
| user_005 | Walking | 1.446046516501e12 | 6.05521 | -12.22358 | -2.18486 |
| user_002 | Jogging | 1.446034899761e12 | -19.27452 | -8.12331 | -6.93658 |
| user_002 | Jogging | 1.446034900473e12 | -16.05561 | -9.34956 | -4.10087 |
| user_002 | Jogging | 1.446034901249e12 | -16.66874 | -11.99366 | -6.9749 |
| user_002 | Jogging | 1.446034901357e12 | -4.06135 | -2.98839 | 2.14534 |
| user_002 | Jogging | 1.446034901365e12 | -2.22198 | -2.60518 | 1.91542 |
| user_002 | Jogging | 1.446034901438e12 | 4.5224 | -0.382604 | -2.14654 |
| user_002 | Jogging | 1.446034901495e12 | -0.535886 | -4.06135 | 0.305964 |
| user_002 | Jogging | 1.446034901576e12 | -17.12858 | -14.906 | 4.86608 |
| user_002 | Jogging | 1.446034901624e12 | -13.06663 | -16.7837 | -0.920286 |
| user_002 | Jogging | 1.446034901883e12 | -3.06503 | 7.7239e-2 | -0.11556 |
| user_002 | Jogging | 1.446034901908e12 | -0.459245 | -1.8771 | 5.01936 |
| user_002 | Jogging | 1.44603490194e12 | -4.82776 | -13.60311 | -5.59536 |
| user_002 | Jogging | 1.446034901996e12 | -18.31651 | -13.29655 | -8.89091 |
| user_002 | Jogging | 1.446034902125e12 | -6.16897 | -1.99206 | -0.422122 |
| user_002 | Jogging | 1.446034902167e12 | -0.995729 | 3.48775 | -0.996927 |
| user_002 | Jogging | 1.446034902191e12 | 1.83997 | 3.14286 | -1.41845 |
| user_002 | Jogging | 1.446034902247e12 | -3.98471 | -2.22198 | -5.99e-4 |
| user_002 | Jogging | 1.446034902256e12 | -3.83143 | -3.06503 | 0.765807 |
| user_002 | Jogging | 1.446034902563e12 | -6.13065 | 2.56806 | -0.843646 |
| user_002 | Jogging | 1.446034902741e12 | -13.37319 | -12.18526 | -5.25048 |
| user_002 | Jogging | 1.446034902766e12 | -15.25089 | -14.1396 | -7.01322 |
| user_002 | Jogging | 1.446034903016e12 | 1.72501 | -3.75479 | -1.61005 |
| user_002 | Jogging | 1.446034903219e12 | -13.06663 | -9.92436 | -0.498763 |
| user_002 | Jogging | 1.446034903421e12 | 0.460442 | -3.37159 | 1.91542 |
| user_002 | Jogging | 1.446034903445e12 | -2.49022 | -3.79311 | 0.497565 |
| user_002 | Jogging | 1.446034903461e12 | -6.36057 | -10.92069 | -1.26517 |
| user_002 | Jogging | 1.446034903518e12 | -15.02096 | -18.31651 | -6.63001 |
| user_002 | Jogging | 1.446034903534e12 | -17.62674 | -16.55378 | -8.16283 |
| user_002 | Jogging | 1.446034903542e12 | -18.92964 | -14.59944 | -8.73763 |
| user_002 | Jogging | 1.446034903639e12 | -5.86241 | -2.87342 | 0.191003 |
| user_002 | Jogging | 1.446034903712e12 | 2.8363 | 2.2615 | -3.2195 |
| user_002 | Jogging | 1.446034903769e12 | -3.7722e-2 | -2.4519 | -4.67568 |
| user_002 | Jogging | 1.446034903785e12 | -8.16163 | -1.14901 | -1.64837 |
| user_002 | Jogging | 1.446034903793e12 | -8.00835 | -2.87342 | -1.18853 |
| user_002 | Jogging | 1.446034903809e12 | -10.1926 | -4.17632 | -3.0279 |
| user_002 | Jogging | 1.446034903915e12 | -18.46979 | -11.11229 | -5.63368 |
| user_002 | Jogging | 1.446034904077e12 | -2.98839 | 2.03158 | -0.613724 |
| user_002 | Jogging | 1.446034904085e12 | -2.49022 | 3.52607 | -1.11189 |
| user_002 | Jogging | 1.446034904384e12 | -9.6178 | -4.59784 | -5.05888 |
| user_002 | Jogging | 1.446034904497e12 | 4.75232 | 0.498763 | -3.18118 |
| user_002 | Jogging | 1.446034904554e12 | -3.60151 | -0.612526 | -0.996927 |
| user_002 | Jogging | 1.44603490457e12 | -5.63249 | -2.18366 | 0.497565 |
| user_002 | Jogging | 1.446034904797e12 | -9.08132 | -7.51018 | -1.76333 |
| user_002 | Jogging | 1.446034904813e12 | -5.82409 | -7.24194 | -0.690364 |
| user_002 | Jogging | 1.446034904935e12 | -0.727487 | -3.02671 | -0.537083 |
| user_002 | Jogging | 1.446034905144e12 | -11.11229 | -0.919089 | -4.40743 |
| user_002 | Jogging | 1.446034905266e12 | 6.17017 | -2.49022 | -0.460442 |
| user_002 | Jogging | 1.446034905331e12 | -5.97737 | -3.83143 | 3.7722e-2 |
| user_002 | Jogging | 1.446034905469e12 | -15.55745 | -16.05561 | 1.76214 |
| user_002 | Jogging | 1.446034905606e12 | -4.17632 | 0.11556 | -2.52974 |
| user_002 | Jogging | 1.446034905631e12 | -1.34061 | 4.40743 | -4.13919 |
| user_002 | Jogging | 1.446034905736e12 | 0.498763 | -10.23092 | 0.267643 |
| user_002 | Jogging | 1.446034905971e12 | 3.25783 | 1.53341 | 1.30229 |
| user_002 | Jogging | 1.446034906003e12 | 6.17017 | 0.11556 | -1.61005 |
| user_002 | Jogging | 1.446034906319e12 | -3.56319 | -4.98104 | -1.22685 |
| user_002 | Jogging | 1.446034906327e12 | -4.21464 | -4.59784 | -1.91661 |
| user_002 | Jogging | 1.446034906383e12 | -3.29495 | 3.44943 | -5.55704 |
| user_002 | Jogging | 1.446034906408e12 | -3.37159 | -2.6435 | -0.537083 |
| user_002 | Jogging | 1.446034906578e12 | -17.32018 | -10.57581 | -11.76493 |
| user_002 | Jogging | 1.446034906667e12 | -8.81307 | -0.842448 | -3.29615 |
| user_002 | Jogging | 1.446034906683e12 | -5.74745 | -0.191003 | -1.38013 |
| user_002 | Jogging | 1.446034906699e12 | -1.60885 | -0.459245 | 0.114362 |
| user_002 | Jogging | 1.446034906707e12 | 0.268841 | -0.535886 | 0.382604 |
| user_002 | Jogging | 1.446034906731e12 | 3.44943 | 1.22685 | -1.80165 |
| user_002 | Jogging | 1.44603490682e12 | -6.13065 | -4.36792 | -0.11556 |
| user_002 | Jogging | 1.446034906989e12 | -18.31651 | -13.06663 | 0.650847 |
| user_002 | Jogging | 1.446034907063e12 | -8.19995 | -4.44456 | -2.10822 |
| user_002 | Jogging | 1.446034907071e12 | -7.01202 | -3.33327 | -1.45677 |
| user_002 | Jogging | 1.446034907088e12 | -4.25296 | -3.14167 | -1.26517 |
| user_002 | Jogging | 1.446034907161e12 | -3.33327 | 4.67568 | -4.29247 |
| user_002 | Jogging | 1.446034907217e12 | -2.60518 | -3.44823 | -0.1922 |
| user_002 | Jogging | 1.446034907233e12 | -2.87342 | -2.18366 | 2.03038 |
| user_002 | Jogging | 1.44603490729e12 | -14.5228 | -14.98264 | -4.714 |
| user_002 | Jogging | 1.446034907314e12 | -18.69972 | -13.60311 | -8.77595 |
| user_002 | Jogging | 1.446034907395e12 | -17.39682 | -3.48655 | -7.31978 |
| user_002 | Jogging | 1.446034907426e12 | -10.15428 | -2.03038 | -4.21583 |
| user_002 | Jogging | 1.446034907451e12 | -7.47186 | -1.8771 | -2.18486 |
| user_002 | Jogging | 1.446034907694e12 | -18.54643 | -8.50651 | 2.72014 |
| user_002 | Jogging | 1.446034907759e12 | -15.86401 | -13.71807 | 0.919089 |
| user_002 | Jogging | 1.446034907767e12 | -16.55378 | -13.948 | 0.804128 |
| user_002 | Jogging | 1.446034908074e12 | -9.69444 | -11.8787 | -0.805325 |
| user_002 | Jogging | 1.446034908721e12 | -3.86975 | 2.87462 | -4.36911 |
| user_002 | Jogging | 1.4460349106e12 | -13.44983 | -4.44456 | -6.55337 |
| user_002 | Jogging | 1.446034911117e12 | -4.138 | 1.57173 | -2.87462 |
| user_002 | Jogging | 1.446034911571e12 | 4.13919 | -1.03405 | -1.87829 |
| user_002 | Jogging | 1.446034912607e12 | -7.43354 | -7.62514 | -2.56806 |
| user_002 | Jogging | 1.446034913189e12 | -7.77842 | -5.78577 | -1.83997 |
| user_002 | Jogging | 1.446034914678e12 | -3.67815 | 0.345482 | 2.2603 |
| user_002 | Jogging | 1.446034915714e12 | -12.68342 | -12.56846 | 1.37893 |
| user_005 | Jumping | 1.446046630501e12 | -4.29128 | 1.07357 | -2.41478 |
| user_005 | Jumping | 1.446046630824e12 | -0.267643 | -3.79311 | 7.6042e-2 |
| user_005 | Jumping | 1.446046631278e12 | 0.383802 | 1.53341 | -1.03525 |
| user_005 | Jumping | 1.446046631407e12 | -1.8771 | -1.8771 | 0.612526 |
| user_005 | Jumping | 1.446046632184e12 | -0.344284 | -8.27659 | -5.32712 |
| user_005 | Jumping | 1.446046632766e12 | -2.75846 | -3.60151 | 1.37893 |
| user_005 | Jumping | 1.446046632832e12 | 0.537083 | -6.93538 | -4.5224 |
| user_005 | Jumping | 1.446046634321e12 | -1.45557 | -15.28921 | -1.15021 |
| user_005 | Jumping | 1.446046634515e12 | 1.87829 | 2.03158 | 3.21831 |
| user_005 | Jumping | 1.446046634839e12 | 4.94392 | -18.92964 | -10.99853 |
| user_005 | Jumping | 1.446046635615e12 | 1.99325 | -16.01729 | -1.11189 |
| user_005 | Jumping | 1.446046636069e12 | 0.537083 | -2.95007 | 3.7722e-2 |
| user_005 | Jumping | 1.446046637039e12 | -1.37893 | -0.689167 | -7.58802 |
| user_005 | Jumping | 1.446046637493e12 | 9.69564 | -19.58108 | -10.61533 |
| user_005 | Jumping | 1.446046638271e12 | 4.82896 | -17.28186 | -16.82322 |
| user_005 | Jumping | 1.446046638465e12 | 0.15388 | -1.8771 | 2.0687 |
| user_005 | Jumping | 1.446046639695e12 | 7.85626 | -19.54276 | -7.12818 |
| user_005 | Jumping | 1.44604663976e12 | -0.880768 | -7.1653 | -0.11556 |
| user_005 | Jumping | 1.446046640018e12 | -1.57053 | 1.45677 | -1.57173 |
| user_005 | Jumping | 1.446046640277e12 | 3.25783 | -19.58108 | -10.76861 |
| user_005 | Jumping | 1.446046640342e12 | 6.78329 | -19.35116 | -4.63736 |
| user_005 | Jumping | 1.446046640471e12 | -1.03405 | -0.420925 | -3.67935 |
| user_005 | Jumping | 1.446046640601e12 | -0.114362 | 0.345482 | -0.958607 |
| user_005 | Jumping | 1.44604664112e12 | -1.8771 | 0.460442 | -3.94759 |
| user_005 | Jumping | 1.44604664209e12 | 3.0279 | -10.53749 | -5.90193 |
| user_005 | Jumping | 1.44604664235e12 | -2.68182 | -3.7722e-2 | -3.60271 |
| user_005 | Jumping | 1.446046642674e12 | -2.03038 | -2.98839 | 1.37893 |
| user_005 | Jumping | 1.446046642803e12 | 5.17384 | -19.58108 | -7.05154 |
| user_005 | Jumping | 1.446046644551e12 | 1.30349 | -2.22198 | 2.29862 |
| user_005 | Jumping | 1.44604664494e12 | 6.78329 | -14.79104 | -10.3854 |
| user_005 | Jumping | 1.446046645522e12 | -0.765807 | -1.37893 | 1.45557 |
| user_001 | Walking | 1.446047180025e12 | 3.48775 | -7.70178 | 0.420925 |
| user_001 | Walking | 1.446047180219e12 | 1.83997 | -6.09233 | -0.15388 |
| user_001 | Walking | 1.446047180672e12 | 4.21583 | -5.93905 | 7.6042e-2 |
| user_001 | Walking | 1.446047180737e12 | 2.75966 | -6.36057 | 0.344284 |
| user_001 | Walking | 1.446047181838e12 | 4.25415 | -6.89706 | 1.18733 |
| user_001 | Walking | 1.446047183131e12 | 2.14654 | -7.01202 | -0.1922 |
| user_001 | Walking | 1.446047183391e12 | 6.13185 | -8.42987 | 0.880768 |
| user_001 | Walking | 1.446047186239e12 | 3.10454 | -11.84038 | 2.0687 |
| user_001 | Walking | 1.446047186563e12 | 2.87462 | -7.51018 | 0.191003 |
| user_001 | Walking | 1.446047187275e12 | 11.99486 | -10.46085 | -0.498763 |
| user_001 | Walking | 1.446047187792e12 | 4.56072 | -8.81307 | 0.382604 |
| user_001 | Walking | 1.446047188634e12 | -2.14534 | -11.64878 | 1.99206 |
| user_001 | Walking | 1.446047188699e12 | 0.958607 | -8.96635 | 1.91542 |
| user_001 | Walking | 1.446047189022e12 | 4.98224 | -11.26557 | 2.29862 |
| user_001 | Walking | 1.446047190317e12 | 3.56439 | -9.38788 | 3.06503 |
| user_001 | Walking | 1.446047190835e12 | 4.86728 | -12.91335 | 4.25296 |
| user_001 | Walking | 1.446047191093e12 | 0.575403 | -7.62514 | 2.0687 |
| user_001 | Walking | 1.446047191677e12 | 0.537083 | -8.50651 | -0.307161 |
| user_001 | Walking | 1.446047191742e12 | -0.344284 | -5.82409 | 0.382604 |
| user_001 | Walking | 1.446047191871e12 | -0.765807 | -7.08866 | 0.842448 |
| user_001 | Walking | 1.446047192195e12 | -0.765807 | -10.34589 | 1.8771 |
| user_001 | Walking | 1.446047192518e12 | 3.41111 | -8.85139 | 3.60151 |
| user_001 | Walking | 1.446047192713e12 | 3.48775 | -8.46819 | 2.72014 |
| user_001 | Walking | 1.446047192907e12 | 7.7239e-2 | -8.16163 | 0.420925 |
| user_001 | Walking | 1.446047193166e12 | 6.66833 | -9.57948 | 0.344284 |
| user_001 | Walking | 1.44604719336e12 | 2.37646 | -8.73643 | -0.537083 |
| user_001 | Walking | 1.446047194461e12 | 7.01322 | -12.37686 | 0.305964 |
| user_001 | Walking | 1.446047194979e12 | 5.71033 | -10.57581 | 0.880768 |
| user_001 | Walking | 1.446047195367e12 | -0.689167 | -5.70913 | 1.41725 |
| user_001 | Walking | 1.446047196144e12 | 5.94025 | -9.96268 | 1.07237 |
| user_002 | Jumping | 1.446035034445e12 | -1.76214 | -0.267643 | -0.996927 |
| user_002 | Jumping | 1.446035035351e12 | -14.75272 | -19.46612 | 0.995729 |
| user_002 | Jumping | 1.44603503574e12 | -1.68549 | -2.10702 | -1.95493 |
| user_002 | Jumping | 1.446035036129e12 | -0.114362 | 0.996927 | -3.8919e-2 |
| user_002 | Jumping | 1.446035036582e12 | -10.80573 | -14.94432 | 1.22565 |
| user_002 | Jumping | 1.446035036647e12 | -0.459245 | -2.14534 | -0.767005 |
| user_002 | Jumping | 1.44603503736e12 | 0.268841 | -0.995729 | -0.958607 |
| user_002 | Jumping | 1.446035038783e12 | -9.88604 | -13.48815 | 0.574206 |
| user_002 | Jumping | 1.446035040338e12 | 5.99e-4 | -1.07237 | -0.1922 |
| user_002 | Jumping | 1.446035040531e12 | -9.65612 | -12.60678 | -1.15021 |
| user_002 | Jumping | 1.446035043056e12 | -12.41518 | -19.08292 | -2.2615 |
| user_002 | Jumping | 1.446035043121e12 | -4.3296 | -6.74378 | 0.114362 |
| user_002 | Jumping | 1.446035043962e12 | -1.76214 | -0.919089 | -1.38013 |
| user_002 | Jumping | 1.446035044415e12 | 0.268841 | 1.68669 | -1.57173 |
| user_002 | Jumping | 1.446035044609e12 | -1.41725 | -1.45557 | -1.38013 |
| user_002 | Jumping | 1.446035044868e12 | -11.57214 | -16.5921 | -0.498763 |
| user_002 | Jumping | 1.446035046228e12 | -7.6042e-2 | 0.307161 | -7.7239e-2 |
| user_002 | Jumping | 1.446035046486e12 | -13.21991 | -15.71073 | 0.305964 |
| user_002 | Jumping | 1.446035047263e12 | -4.06135 | -5.90073 | -3.8919e-2 |
| user_002 | Jumping | 1.446035048234e12 | -12.83671 | -15.0976 | 7.6042e-2 |
| user_002 | Jumping | 1.446035049399e12 | -13.52647 | -17.3585 | -0.958607 |
| user_003 | Sitting | 1.446047526718e12 | 0.422122 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047527172e12 | 0.498763 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047528661e12 | 0.460442 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047529656e12 | 0.460442 | -0.727487 | 9.4262 |
| user_003 | Sitting | 1.446047529705e12 | 0.422122 | -0.650847 | 9.69444 |
| user_003 | Sitting | 1.446047529786e12 | 0.345482 | -0.727487 | 9.34956 |
| user_003 | Sitting | 1.446047529817e12 | 0.575403 | -0.612526 | 9.34956 |
| user_003 | Sitting | 1.446047529842e12 | 0.498763 | -0.650847 | 9.31124 |
| user_003 | Sitting | 1.44604752985e12 | 0.460442 | -0.727487 | 9.46452 |
| user_003 | Sitting | 1.446047529988e12 | 0.307161 | -0.804128 | 9.38788 |
| user_003 | Sitting | 1.446047530045e12 | 0.460442 | -0.804128 | 9.4262 |
| user_003 | Sitting | 1.446047530101e12 | 0.345482 | -0.574206 | 9.4262 |
| user_003 | Sitting | 1.446047530133e12 | 0.345482 | -0.727487 | 9.50284 |
| user_003 | Sitting | 1.446047530215e12 | 0.537083 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047530231e12 | 0.460442 | -0.612526 | 9.54116 |
| user_003 | Sitting | 1.446047530255e12 | 0.383802 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047530263e12 | 0.537083 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047530425e12 | 0.575403 | -0.574206 | 9.50284 |
| user_003 | Sitting | 1.446047530562e12 | 0.575403 | -0.765807 | 9.46452 |
| user_003 | Sitting | 1.446047530595e12 | 0.345482 | -0.689167 | 9.31124 |
| user_003 | Sitting | 1.44604753061e12 | 0.575403 | -0.612526 | 9.4262 |
| user_003 | Sitting | 1.4460475307e12 | 0.575403 | -0.612526 | 9.4262 |
| user_003 | Sitting | 1.446047530707e12 | 0.613724 | -0.727487 | 9.38788 |
| user_003 | Sitting | 1.446047530749e12 | 0.422122 | -0.765807 | 9.4262 |
| user_003 | Sitting | 1.446047530846e12 | 0.383802 | -0.727487 | 9.4262 |
| user_003 | Sitting | 1.446047530871e12 | 0.575403 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.446047530886e12 | 0.537083 | -0.650847 | 9.34956 |
| user_003 | Sitting | 1.446047530927e12 | 0.460442 | -0.727487 | 9.50284 |
| user_003 | Sitting | 1.446047531113e12 | 0.422122 | -0.765807 | 9.50284 |
| user_003 | Sitting | 1.44604753121e12 | 0.383802 | -0.497565 | 9.34956 |
| user_003 | Sitting | 1.446047531217e12 | 0.383802 | -0.612526 | 9.38788 |
| user_003 | Sitting | 1.446047531234e12 | 0.422122 | -0.612526 | 9.38788 |
| user_003 | Sitting | 1.446047531493e12 | 0.537083 | -0.612526 | 9.4262 |
| user_003 | Sitting | 1.446047531526e12 | 0.307161 | -0.574206 | 9.46452 |
| user_003 | Sitting | 1.446047531656e12 | 0.498763 | -0.650847 | 9.38788 |
| user_003 | Sitting | 1.446047531728e12 | 0.230521 | -0.689167 | 9.50284 |
| user_003 | Sitting | 1.446047531736e12 | 0.307161 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047531768e12 | 0.460442 | -0.765807 | 9.50284 |
| user_003 | Sitting | 1.446047531833e12 | 0.537083 | -0.880768 | 9.38788 |
| user_003 | Sitting | 1.446047531881e12 | 0.498763 | -0.612526 | 9.38788 |
| user_003 | Sitting | 1.446047531889e12 | 0.498763 | -0.765807 | 9.4262 |
| user_003 | Sitting | 1.446047531922e12 | 0.383802 | -0.765807 | 9.38788 |
| user_003 | Sitting | 1.44604753193e12 | 0.460442 | -0.880768 | 9.46452 |
| user_003 | Sitting | 1.446047531939e12 | 0.460442 | -0.612526 | 9.54116 |
| user_003 | Sitting | 1.446047532027e12 | 0.422122 | -0.842448 | 9.38788 |
| user_003 | Sitting | 1.446047532043e12 | 0.460442 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047532108e12 | 0.575403 | -0.612526 | 9.4262 |
| user_003 | Sitting | 1.446047532237e12 | 0.422122 | -0.574206 | 9.50284 |
| user_003 | Sitting | 1.446047532724e12 | 0.460442 | -0.727487 | 9.4262 |
| user_003 | Sitting | 1.44604753278e12 | 0.460442 | -0.535886 | 9.34956 |
| user_003 | Sitting | 1.446047533443e12 | 0.498763 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047533702e12 | 0.422122 | -0.650847 | 9.50284 |
| user_003 | Sitting | 1.446047535127e12 | 0.422122 | -0.689167 | 9.50284 |
| user_003 | Sitting | 1.446047535386e12 | 0.498763 | -0.689167 | 9.38788 |
| user_003 | Sitting | 1.446047535904e12 | 0.422122 | -0.689167 | 9.46452 |
| user_003 | Sitting | 1.446047536033e12 | 0.460442 | -0.650847 | 9.50284 |
| user_003 | Sitting | 1.446047536227e12 | 0.422122 | -0.650847 | 9.38788 |
| user_003 | Sitting | 1.446047536745e12 | 0.460442 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047537781e12 | 0.460442 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047537963e12 | 2.91294 | -4.06135 | -17.7429 |
| user_003 | Sitting | 1.44604753806e12 | 0.460442 | -0.727487 | 9.34956 |
| user_003 | Sitting | 1.446047538237e12 | 0.537083 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.446047538246e12 | 0.690364 | -0.727487 | 9.46452 |
| user_003 | Sitting | 1.44604753827e12 | 0.498763 | -0.689167 | 9.54116 |
| user_003 | Sitting | 1.446047538287e12 | 0.460442 | -0.842448 | 9.54116 |
| user_003 | Sitting | 1.446047538295e12 | 0.537083 | -0.727487 | 9.57948 |
| user_003 | Sitting | 1.446047538343e12 | 0.537083 | -0.574206 | 9.38788 |
| user_003 | Sitting | 1.446047538448e12 | 0.345482 | -0.727487 | 9.50284 |
| user_003 | Sitting | 1.446047538529e12 | 0.460442 | -0.650847 | 9.34956 |
| user_003 | Sitting | 1.446047538577e12 | 0.383802 | -0.804128 | 9.34956 |
| user_003 | Sitting | 1.44604753861e12 | 0.383802 | -0.574206 | 9.46452 |
| user_003 | Sitting | 1.446047538626e12 | 0.498763 | -0.727487 | 9.38788 |
| user_003 | Sitting | 1.446047538804e12 | 0.613724 | -0.765807 | 9.2346 |
| user_003 | Sitting | 1.446047538846e12 | 0.498763 | -0.612526 | 9.31124 |
| user_003 | Sitting | 1.446047539161e12 | 0.537083 | -0.574206 | 9.6178 |
| user_003 | Sitting | 1.44604753929e12 | 0.613724 | -0.650847 | 9.38788 |
| user_003 | Sitting | 1.446047539298e12 | 0.307161 | -0.689167 | 9.38788 |
| user_003 | Sitting | 1.446047539557e12 | 0.498763 | -0.727487 | 9.50284 |
| user_003 | Sitting | 1.446047539824e12 | 0.383802 | -0.689167 | 9.50284 |
| user_003 | Sitting | 1.446047540084e12 | 0.460442 | -0.650847 | 9.38788 |
| user_003 | Sitting | 1.446047540148e12 | 0.422122 | -0.650847 | 9.4262 |
| user_003 | Sitting | 1.446047540261e12 | 0.307161 | -0.612526 | 9.46452 |
| user_003 | Sitting | 1.446047540504e12 | 0.460442 | -0.689167 | 9.4262 |
| user_003 | Sitting | 1.446047540803e12 | 0.498763 | -0.765807 | 9.6178 |
| user_003 | Sitting | 1.446047540836e12 | 0.422122 | -0.727487 | 9.2346 |
| user_003 | Sitting | 1.446047540852e12 | 0.537083 | -0.650847 | 9.31124 |
| user_003 | Sitting | 1.446047540884e12 | 0.498763 | -0.650847 | 9.46452 |
| user_003 | Sitting | 1.446047540941e12 | 0.498763 | -0.957409 | 9.54116 |
| user_003 | Sitting | 1.446047541046e12 | 0.345482 | -0.689167 | 9.4262 |
| user_003 | Sitting | 1.44604754111e12 | 0.575403 | -0.535886 | 9.50284 |
| user_003 | Sitting | 1.446047541135e12 | 0.460442 | -0.650847 | 9.38788 |
| user_003 | Sitting | 1.4460475412e12 | 0.498763 | -0.689167 | 9.27292 |
| user_003 | Sitting | 1.446047541223e12 | 0.307161 | -0.689167 | 9.46452 |
| user_003 | Sitting | 1.44604754209e12 | 0.460442 | -0.650847 | 9.46452 |
| user_006 | Sitting | 1.446035991462e12 | -0.229323 | -1.26397 | 9.31124 |
| user_006 | Sitting | 1.44603599224e12 | -0.267643 | -1.30229 | 9.34956 |
| user_006 | Sitting | 1.446035992757e12 | -0.191003 | -1.14901 | 9.38788 |
| user_006 | Sitting | 1.446035993534e12 | -0.305964 | -1.14901 | 9.38788 |
| user_006 | Sitting | 1.446035993664e12 | -0.305964 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446035994765e12 | -0.229323 | -1.22565 | 9.4262 |
| user_006 | Sitting | 1.446035995413e12 | -0.229323 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.44603599606e12 | -0.229323 | -1.22565 | 9.4262 |
| user_006 | Sitting | 1.446035996578e12 | -0.229323 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446035997161e12 | -0.191003 | -1.22565 | 9.4262 |
| user_006 | Sitting | 1.446035998003e12 | -0.267643 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446035998391e12 | -0.229323 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.446035999492e12 | -0.229323 | -1.18733 | 9.34956 |
| user_006 | Sitting | 1.446036000334e12 | -0.191003 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.446036000593e12 | -0.229323 | -1.22565 | 9.34956 |
| user_006 | Sitting | 1.446036001046e12 | -0.229323 | -1.14901 | 9.38788 |
| user_006 | Sitting | 1.446036001176e12 | -0.229323 | -1.18733 | 9.4262 |
| user_006 | Sitting | 1.446036001241e12 | -0.267643 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446036001694e12 | -0.152682 | -1.18733 | 9.4262 |
| user_006 | Sitting | 1.44603600273e12 | -0.229323 | -1.26397 | 9.38788 |
| user_006 | Sitting | 1.446036003054e12 | -0.229323 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.446036003184e12 | -0.191003 | -1.18733 | 9.4262 |
| user_006 | Sitting | 1.446036003378e12 | -0.229323 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446036003443e12 | -0.305964 | -1.14901 | 9.38788 |
| user_006 | Sitting | 1.446036003572e12 | -0.191003 | -1.26397 | 9.38788 |
| user_006 | Sitting | 1.446036003702e12 | -0.305964 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.446036004091e12 | -0.229323 | -1.22565 | 9.34956 |
| user_006 | Sitting | 1.446036004285e12 | -0.229323 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446036004738e12 | -0.229323 | -1.11069 | 9.38788 |
| user_006 | Sitting | 1.446036004997e12 | -0.229323 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446036005774e12 | -0.305964 | -1.18733 | 9.38788 |
| user_006 | Sitting | 1.446036006875e12 | -0.152682 | -1.18733 | 9.34956 |
| user_006 | Sitting | 1.446036006939e12 | -0.267643 | -1.30229 | 9.31124 |
| user_006 | Sitting | 1.446036007653e12 | -0.267643 | -1.22565 | 9.38788 |
| user_006 | Sitting | 1.446036007976e12 | -0.229323 | -1.22565 | 9.34956 |
| user_007 | Jogging | 1.446309659174e12 | -12.60678 | -8.85139 | 2.22198 |
| user_007 | Jogging | 1.446309659693e12 | 3.83263 | -1.26397 | 1.83878 |
| user_007 | Jogging | 1.446309661117e12 | -15.90233 | -10.03932 | -2.68302 |
| user_007 | Jogging | 1.446309661377e12 | -7.51018 | -6.74378 | -1.83997 |
| user_007 | Jogging | 1.446309661442e12 | -15.0976 | -12.30022 | 1.45557 |
| user_007 | Jogging | 1.446309661635e12 | -0.344284 | -2.10702 | -0.460442 |
| user_007 | Jogging | 1.446309664809e12 | -3.56319 | -3.98471 | 1.03405 |
| user_007 | Jogging | 1.446309665263e12 | -16.17057 | -11.22725 | -2.91294 |
| user_007 | Jogging | 1.446309665651e12 | -2.98839 | -6.39889 | -0.11556 |
| user_007 | Jogging | 1.446309666687e12 | -3.94639 | 0.613724 | 0.229323 |
| user_007 | Jogging | 1.446309667399e12 | -14.86768 | -3.71647 | -5.82529 |
| user_007 | Jogging | 1.446309671028e12 | -5.13432 | -3.29495 | 0.382604 |
| user_007 | Jogging | 1.446309673683e12 | -7.12698 | -11.64878 | -2.98958 |
| user_007 | Jogging | 1.446309673943e12 | -1.64717 | 0.843646 | -1.87829 |
| user_007 | Jogging | 1.446309674397e12 | -1.60885 | -0.919089 | -1.18853 |
| user_007 | Jogging | 1.446309674979e12 | -13.90968 | -7.81675 | -0.843646 |
| user_007 | Jogging | 1.446309675691e12 | -11.57214 | -12.0703 | -2.49142 |
| user_007 | Jogging | 1.446309675886e12 | -3.79311 | -7.51018 | -1.49509 |
| user_004 | Walking | 1.44604636711e12 | 2.2615 | -7.05034 | -0.728685 |
| user_004 | Walking | 1.446046367952e12 | 3.2195 | -10.23092 | -0.422122 |
| user_004 | Walking | 1.446046368146e12 | -6.82042 | -14.59944 | 0.574206 |
| user_004 | Walking | 1.446046368276e12 | 0.958607 | -6.7821 | 1.03405 |
| user_004 | Walking | 1.44604636957e12 | 3.2195 | -12.4535 | -7.7413 |
| user_004 | Walking | 1.446046369635e12 | 0.843646 | -10.42253 | -7.58802 |
| user_004 | Walking | 1.446046370153e12 | 4.10087 | -10.1926 | -0.307161 |
| user_004 | Walking | 1.446046370347e12 | -7.43354 | -15.97897 | 0.152682 |
| user_004 | Walking | 1.446046371124e12 | 0.230521 | -9.08132 | 7.6042e-2 |
| user_004 | Walking | 1.446046371189e12 | -0.727487 | -8.96635 | 1.14901 |
| user_004 | Walking | 1.446046372095e12 | -1.53221 | -13.18159 | 5.51753 |
| user_004 | Walking | 1.446046373391e12 | -1.68549 | -8.16163 | 1.72382 |
| user_004 | Walking | 1.446046374038e12 | -1.34061 | -1.99206 | -2.95126 |
| user_004 | Walking | 1.446046376822e12 | 0.15388 | -9.54116 | -7.7239e-2 |
| user_004 | Walking | 1.446046377082e12 | 1.41845 | -13.25823 | 0.842448 |
| user_004 | Walking | 1.44604637883e12 | -3.44823 | -2.2603 | 1.22565 |
| user_004 | Walking | 1.446046379219e12 | 2.49142 | -10.72909 | -1.07357 |
| user_004 | Walking | 1.446046379801e12 | 1.80165 | -10.1926 | -7.43474 |
| user_004 | Walking | 1.446046379995e12 | -4.98104 | -6.59049 | 3.21831 |
| user_004 | Walking | 1.446046380349e12 | 0.881966 | -11.34221 | -1.30349 |
| user_004 | Walking | 1.446046380357e12 | -0.229323 | -11.03565 | -1.91661 |
| user_004 | Walking | 1.446046380616e12 | 2.60638 | -8.65979 | -1.18853 |
| user_004 | Walking | 1.446046380632e12 | 1.91661 | -9.04299 | -1.95493 |
| user_004 | Walking | 1.446046380802e12 | -0.152682 | -3.71647 | -4.86728 |
| user_004 | Walking | 1.446046380818e12 | 0.422122 | -5.01936 | -6.47673 |
| user_004 | Walking | 1.446046380826e12 | 0.920286 | -5.67081 | -7.12818 |
| user_004 | Walking | 1.446046380891e12 | 3.18118 | -18.96796 | -9.92556 |
| user_004 | Walking | 1.446046380899e12 | 2.22318 | -16.28553 | -8.58435 |
| user_004 | Walking | 1.446046381053e12 | -3.52487 | -1.76214 | 0.650847 |
| user_004 | Walking | 1.446046381109e12 | -2.6435 | -10.26924 | 2.10702 |
| user_004 | Walking | 1.446046381166e12 | 4.63736 | -9.92436 | -3.29615 |
| user_004 | Walking | 1.446046381377e12 | -1.26397 | -8.96635 | -0.307161 |
| user_004 | Walking | 1.446046381393e12 | -0.497565 | -9.27292 | -1.68669 |
| user_004 | Walking | 1.4460463814e12 | 0.307161 | -9.34956 | -1.38013 |
| user_004 | Walking | 1.446046381539e12 | 0.767005 | -13.10495 | 1.45557 |
| user_004 | Walking | 1.446046381708e12 | 3.41111 | -8.62147 | -2.49142 |
| user_004 | Walking | 1.446046381757e12 | 0.958607 | -7.58682 | -0.307161 |
| user_004 | Walking | 1.446046381773e12 | -0.152682 | -6.51385 | 7.6042e-2 |
| user_004 | Walking | 1.446046381797e12 | -1.72382 | -4.7128 | 1.22565 |
| user_004 | Walking | 1.446046381822e12 | -3.06503 | -2.98839 | 0.727487 |
| user_004 | Walking | 1.446046381854e12 | -3.29495 | -2.03038 | -1.41845 |
| user_004 | Walking | 1.446046381886e12 | -1.14901 | -2.22198 | -4.02423 |
| user_004 | Walking | 1.446046381919e12 | 0.958607 | -6.16897 | -5.40376 |
| user_004 | Walking | 1.446046381959e12 | 3.71767 | -15.05928 | -7.70298 |
| user_004 | Walking | 1.446046382089e12 | -3.71647 | -10.72909 | -2.14654 |
| user_004 | Walking | 1.446046382129e12 | -11.76374 | -19.50444 | -0.843646 |
| user_004 | Walking | 1.44604638221e12 | -4.09967 | -3.40991 | 5.096 |
| user_004 | Walking | 1.446046382259e12 | 0.652044 | -19.58108 | 4.63616 |
| user_004 | Walking | 1.446046382316e12 | -0.229323 | -4.82776 | -0.575403 |
| user_004 | Walking | 1.446046382332e12 | -3.86975 | -4.48288 | 2.14534 |
| user_004 | Walking | 1.446046382526e12 | 1.49509 | -9.77108 | -0.537083 |
| user_004 | Walking | 1.446046382566e12 | 3.2195 | -10.95901 | -7.7239e-2 |
| user_004 | Walking | 1.446046382647e12 | -0.152682 | -13.79471 | 0.574206 |
| user_004 | Walking | 1.44604638285e12 | 2.60638 | -7.77842 | -2.33814 |
| user_004 | Walking | 1.446046383157e12 | -0.344284 | -11.61046 | -6.82161 |
| user_004 | Walking | 1.446046383174e12 | -1.22565 | -8.08499 | -7.08986 |
| user_004 | Jumping | 1.446046440495e12 | -0.612526 | -0.995729 | -0.728685 |
| user_004 | Jumping | 1.446046440559e12 | 1.41845 | -0.880768 | -7.7239e-2 |
| user_004 | Jumping | 1.44604644069e12 | 8.35443 | -7.66346 | -0.460442 |
| user_004 | Jumping | 1.446046441337e12 | 14.02583 | -14.7144 | -0.958607 |
| user_004 | Jumping | 1.446046442243e12 | 0.575403 | 1.15021 | -0.690364 |
| user_004 | Jumping | 1.446046442502e12 | 6.89825 | -11.11229 | -1.57173 |
| user_004 | Jumping | 1.446046443085e12 | 3.94759 | -0.305964 | 1.30229 |
| user_004 | Jumping | 1.446046444705e12 | -1.72382 | 1.26517 | -1.45677 |
| user_004 | Jumping | 1.446046444899e12 | 0.498763 | 1.30349 | 1.18733 |
| user_004 | Jumping | 1.446046445028e12 | 19.19908 | -19.08292 | -3.79431 |
| user_004 | Jumping | 1.446046445093e12 | 3.18118 | -8.46819 | 1.30229 |
| user_004 | Jumping | 1.446046445352e12 | 1.61005 | -0.689167 | -0.613724 |
| user_004 | Jumping | 1.446046445482e12 | 12.03318 | -8.88971 | -0.920286 |
| user_004 | Jumping | 1.446046446387e12 | 1.30349 | -1.57053 | -1.76333 |
| user_004 | Jumping | 1.446046446777e12 | 1.15021 | 6.40009 | -2.41478 |
| user_004 | Jumping | 1.446046446842e12 | -1.41725 | 0.230521 | -1.30349 |
| user_004 | Jumping | 1.446046447036e12 | 0.498763 | 0.690364 | -1.34181 |
| user_004 | Jumping | 1.44604644723e12 | -1.26397 | -3.60151 | 4.40624 |
| user_004 | Jumping | 1.446046448784e12 | 16.36337 | -16.86034 | -1.72501 |
| user_004 | Jumping | 1.446046448849e12 | -0.535886 | -3.83143 | 4.63616 |
| user_004 | Jumping | 1.446046449172e12 | 1.38013 | 2.56806 | 0.191003 |
| user_004 | Jumping | 1.446046449949e12 | -2.0687 | 0.575403 | 1.07237 |
| user_004 | Jumping | 1.446046450079e12 | -1.76214 | -0.459245 | -0.728685 |
| user_004 | Jumping | 1.446046450273e12 | 1.03525 | 0.996927 | -2.33814 |
| user_004 | Jumping | 1.446046450726e12 | 0.805325 | -3.7722e-2 | -0.767005 |
| user_004 | Jumping | 1.446046451115e12 | 0.268841 | 4.40743 | -3.06622 |
| user_004 | Jumping | 1.446046451503e12 | 17.62794 | -14.63776 | -3.41111 |
| user_004 | Jumping | 1.446046451697e12 | -1.80046 | 1.76333 | -2.10822 |
| user_004 | Jumping | 1.446046452086e12 | 3.37279 | -10.34589 | 3.14167 |
| user_004 | Jumping | 1.446046452409e12 | 0.690364 | 1.49509 | -1.68669 |
| user_004 | Jumping | 1.446046453575e12 | -0.305964 | 0.690364 | -1.07357 |
| user_004 | Jumping | 1.446046455194e12 | 0.881966 | 1.87829 | -0.575403 |
Feature Selection on Running Windows
- ETL Using SparkSQL Windows
A Markov Process Assumption
This is sensible since the subjects are not instantaneously changing between the activities of interest: sitting, walking, jogging, etc. Thus it makes sense to try and use the most recent accelerometer readings (from the immediate past) to predict the current activity.
See the following for a crash introduction to windows:
// Import the window functions.
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
// Create a window specification
val windowSize = 10
val wSpec1 = Window.partitionBy("user_id","activity").orderBy("timeStampAsLong").rowsBetween(-windowSize, 0)
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
windowSize: Int = 10
wSpec1: org.apache.spark.sql.expressions.WindowSpec = org.apache.spark.sql.expressions.WindowSpec@ade1233
// Calculate the moving window statistics from data
val dataFeatDF = dataDF
.withColumn( "meanX", mean($"x").over(wSpec1) )
.withColumn( "meanY", mean($"y").over(wSpec1) )
.withColumn( "meanZ", mean($"z").over(wSpec1) )
//resultant = 1/n * ∑ √(x² + y² + z²)
.withColumn( "SqX", pow($"x",2.0) )
.withColumn( "SqY", pow($"y",2.0) )
.withColumn( "SqZ", pow($"z",2.0) )
.withColumn( "resultant", pow( $"SqX"+$"SqY"+$"SqZ",0.50 ) )
.withColumn( "meanResultant", mean("resultant").over(wSpec1) )
// (1 / n ) * ∑ |b - mean_b|, for b in {x,y,z}
.withColumn( "absDevFromMeanX", abs($"x" - $"meanX") )
.withColumn( "absDevFromMeanY", abs($"y" - $"meanY") )
.withColumn( "absDevFromMeanZ", abs($"z" - $"meanZ") )
.withColumn( "meanAbsDevFromMeanX", mean("absDevFromMeanX").over(wSpec1) )
.withColumn( "meanAbsDevFromMeanY", mean("absDevFromMeanY").over(wSpec1) )
.withColumn( "meanAbsDevFromMeanZ", mean("absDevFromMeanZ").over(wSpec1) )
//standard deviation = √ variance = √ 1/n * ∑ (x - u)² with u = mean x
.withColumn( "sqrDevFromMeanX", pow($"absDevFromMeanX",2.0) )
.withColumn( "sqrDevFromMeanY", pow($"absDevFromMeanY",2.0) )
.withColumn( "sqrDevFromMeanZ", pow($"absDevFromMeanZ",2.0) )
.withColumn( "varianceX", mean("sqrDevFromMeanX").over(wSpec1) )
.withColumn( "varianceY", mean("sqrDevFromMeanY").over(wSpec1) )
.withColumn( "varianceZ", mean("sqrDevFromMeanZ").over(wSpec1) )
.withColumn( "stddevX", pow($"varianceX",0.50) )
.withColumn( "stddevY", pow($"varianceY",0.50) )
.withColumn( "stddevZ", pow($"varianceZ",0.50) )
dataFeatDF: org.apache.spark.sql.DataFrame = [user_id: string, activity: string ... 27 more fields]
display(dataFeatDF.sample(false,0.1))
| user_id | activity | timeStampAsLong | x | y | z | meanX | meanY | meanZ | SqX | SqY | SqZ | resultant | meanResultant | absDevFromMeanX | absDevFromMeanY | absDevFromMeanZ | meanAbsDevFromMeanX | meanAbsDevFromMeanY | meanAbsDevFromMeanZ | sqrDevFromMeanX | sqrDevFromMeanY | sqrDevFromMeanZ | varianceX | varianceY | varianceZ | stddevX | stddevY | stddevZ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| user_001 | Jogging | 1.446047140892e12 | -5.01936 | 0.383802 | -1.61005 | 3.0104863636363643 | -8.294006090909091 | 0.4139589090909091 | 25.193974809599997 | 0.147303975204 | 2.5922610025 | 5.285218991423534 | 12.114445037568451 | 8.029846363636365 | 8.67780809090909 | 2.024008909090909 | 5.574814628099175 | 8.743996363636365 | 1.8780127768595039 | 64.47843262360415 | 75.30435326264728 | 4.0966120640793715 | 39.26105423020038 | 86.44128603240851 | 4.762676135262259 | 6.265864204577081 | 9.297380600599746 | 2.1823556390428807 |
| user_001 | Jogging | 1.446047141346e12 | 0.498763 | -7.51018 | 1.83878 | 2.8084320909090907 | -6.935377181818183 | -4.936863636363633e-2 | 0.24876453016900002 | 56.4028036324 | 3.3811118884000004 | 7.748075893469875 | 10.526229014345958 | 2.309669090909091 | 0.5748028181818174 | 1.8881486363636364 | 4.870479504132231 | 7.062020214876034 | 2.769514024793388 | 5.334571309500826 | 0.3303982797897595 | 3.5651052730018598 | 28.32052218489271 | 67.50085446063852 | 9.63958031560342 | 5.3217029403089295 | 8.215890363231397 | 3.104767352895128 |
| user_001 | Jogging | 1.44604714141e12 | 11.87989 | -19.58108 | 2.75846 | 3.738570272727273 | -7.440509 | -7.027136363636353e-2 | 141.13178641209998 | 383.4186939664 | 7.609101571599999 | 23.06858430745372 | 11.31119913133375 | 8.141319727272727 | 12.140571000000001 | 2.8287313636363636 | 5.357875181818181 | 7.761603768595041 | 2.783764652892562 | 66.28108690168007 | 147.39346420604105 | 8.00172112762004 | 33.64351026346047 | 79.10395343437911 | 9.717968828501292 | 5.800302601025266 | 8.894040332401191 | 3.1173656873233995 |
| user_001 | Jogging | 1.446047142255e12 | 5.32712 | -18.35483 | 3.94639 | 3.254341727272727 | -8.088470909090908 | 0.69265 | 28.378207494399998 | 336.8997843289 | 15.5739940321 | 19.515429430463477 | 14.672311468733064 | 2.0727782727272728 | 10.266359090909091 | 3.25374 | 4.968022991735537 | 9.121183421487604 | 2.997534545454546 | 4.296409767890256 | 105.39812898349174 | 10.5868239876 | 42.373279917383705 | 108.65275154076608 | 11.80757071836034 | 6.509476163055189 | 10.423663057714695 | 3.4362145914305673 |
| user_001 | Jogging | 1.44604714236e12 | -3.90807 | 1.87829 | -1.03525 | -0.38260427272727265 | -4.622226636363637 | 1.309258636363637 | 15.2730111249 | 3.5279733241 | 1.0717425625 | 4.457883692011267 | 6.235267429173871 | 3.5254657272727274 | 6.500516636363637 | 2.3445086363636367 | 2.0246421404958674 | 4.5952694710743796 | 1.275971702479339 | 12.42890859417462 | 42.25671653964041 | 5.496720745983679 | 5.322841770497276 | 27.27538573170004 | 2.7565803183488558 | 2.3071284685724107 | 5.222584200537129 | 1.660295250354242 |
| user_001 | Jogging | 1.446047142611e12 | 3.14286 | -19.58108 | 11.57214 | 4.797603636363636 | -11.600005181818183 | 3.4970007272727273 | 9.877568979600001 | 383.4186939664 | 133.9144241796 | 22.961068945621847 | 17.859074479791374 | 1.6547436363636359 | 7.981074818181817 | 8.075139272727272 | 4.461625685950413 | 7.032252909090909 | 8.588184256198346 | 2.7381765020859485 | 63.69755525341592 | 65.20787427394234 | 29.39875554713526 | 63.52664671148329 | 111.91945306916881 | 5.422061927637424 | 7.970360513269352 | 10.579199075032514 |
| user_001 | Jogging | 1.446047142723e12 | 3.33447 | -16.4005 | 4.59784 | 8.695826363636364 | -19.19787727272727 | 1.5635675454545455 | 11.1186901809 | 268.97640025000004 | 21.140132665599996 | 17.356129265953857 | 21.88260842177062 | 5.361356363636364 | 2.7973772727272674 | 3.0342724545454542 | 5.770848892561983 | 0.974475619834712 | 4.766286942148761 | 28.74414205790414 | 7.825319605971044 | 9.206809328413296 | 47.590515226108074 | 2.4736297184957947 | 29.04037921776094 | 6.898587915371382 | 1.5727777079090977 | 5.3889126192359935 |
| user_001 | Jogging | 1.446047143007e12 | 13.94919 | -19.58108 | -1.99325 | 7.406869090909091 | -15.54699727272727 | -2.132600909090909 | 194.5799016561 | 383.4186939664 | 3.9730455625 | 24.124088401118915 | 17.659646578855703 | 6.5423209090909085 | 4.034082727272731 | 0.13935090909090886 | 4.869846033057851 | 6.8751705371900815 | 2.2795826446280993 | 42.80196287752809 | 16.273823450480194 | 1.9418675864462744e-2 | 25.026920417585174 | 50.28981774904209 | 8.028885422595941 | 5.002691317439561 | 7.091531410706865 | 2.8335287933239606 |
| user_001 | Jogging | 1.446047143412e12 | -3.7722e-2 | -19.38948 | 16.74538 | 2.933844363636364 | -10.001001818181818 | 4.7859561818181815 | 1.422949284e-3 | 375.95193467039996 | 280.4077513444 | 25.61954544803799 | 14.94696596077227 | 2.971566363636364 | 9.388478181818181 | 11.959423818181818 | 4.184833462809917 | 6.708588429752067 | 8.137841272727274 | 8.830206653495043 | 88.14352257047602 | 143.0278180628946 | 25.580893194814248 | 48.75439847000317 | 93.00363187197318 | 5.057755746851981 | 6.9824349957592275 | 9.643839062944444 |
| user_001 | Jogging | 1.446047143428e12 | 5.99e-4 | -19.58108 | 2.49022 | 2.0455097272727274 | -12.96560090909091 | 4.8382107272727275 | 3.5880100000000004e-7 | 383.4186939664 | 6.2011956484 | 19.738791502359028 | 17.17647355875381 | 2.0449107272727276 | 6.615479090909091 | 2.3479907272727276 | 3.7078880247933883 | 7.136446049586777 | 7.307778793388429 | 4.181659882515076 | 43.764563602255365 | 5.513060455358713 | 22.443816935824188 | 54.075963421190316 | 85.24184299019907 | 4.737490573692384 | 7.3536360680407835 | 9.232650918896429 |
| user_001 | Jogging | 1.4460471435e12 | 8.92923 | -19.58108 | 7.62514 | 0.5335997272727272 | -19.581079999999996 | 1.8318090909090905 | 79.73114839290001 | 383.4186939664 | 58.1427600196 | 22.831833092831157 | 21.106302639228264 | 8.395630272727272 | 3.552713678800501e-15 | 5.793330909090909 | 4.901198421487603 | 2.9142437190082675 | 3.942557785123967 | 70.48660767633461 | 1.2621774483536189e-29 | 33.5626830222281 | 33.313236846605356 | 15.557764988992716 | 30.291063062675242 | 5.771762022693361 | 3.944333275598389 | 5.503731739708544 |
| user_001 | Jogging | 1.446047143517e12 | 10.42372 | -19.58108 | 8.73643 | 2.205758909090909 | -19.581079999999996 | 2.4379672727272723 | 108.65393863839999 | 383.4186939664 | 76.3252091449 | 23.841095649103462 | 21.67139468669904 | 8.21796109090909 | 3.552713678800501e-15 | 6.298462727272728 | 6.114780479338842 | 1.5831671074380202 | 4.6259896033057855 | 67.53488449169573 | 1.2621774483536189e-29 | 39.67063272684381 | 45.04989788390212 | 5.722576186684003 | 36.09638973021352 | 6.711922070756045 | 2.3921906668750306 | 6.008027107979251 |
| user_001 | Jogging | 1.446047143581e12 | 14.17911 | -4.75112 | -8.58435 | 12.837903636363638 | -14.822395454545456 | 1.539181818181818 | 201.04716039209998 | 22.573141254400003 | 73.6910649225 | 17.24271923360698 | 21.99929601707681 | 1.3412063636363616 | 10.071275454545455 | 10.123531818181819 | 6.092612942148761 | 3.6242771074380187 | 7.671663884297521 | 1.7988345098586722 | 101.43058928132977 | 102.48589647373969 | 41.440687896949285 | 28.9054852091499 | 72.55417043068063 | 6.437444205346504 | 5.376382167326827 | 8.517873586211563 |
| user_001 | Jogging | 1.446047143606e12 | 9.23579 | -0.497565 | 0.919089 | 13.25594272727273 | -10.042807727272727 | -1.2721355454545458 | 85.2998169241 | 0.24757092922499999 | 0.8447245899210001 | 9.294735738214724 | 18.876966595567662 | 4.02015272727273 | 9.545242727272727 | 2.1912245454545456 | 4.424255107438015 | 6.262994958677688 | 6.90810826446281 | 16.161627950598373 | 91.11165872255289 | 4.80146500860248 | 24.680606942273855 | 54.43818419281497 | 66.47172910922194 | 4.967958025413847 | 7.378223647519434 | 8.153019631352665 |
| user_001 | Jogging | 1.44604714363e12 | 5.82529 | 3.14286 | 3.21831 | 11.935633636363635 | -4.315663545454545 | -2.742242818181819 | 33.9340035841 | 9.877568979600001 | 10.357519256099998 | 7.359965476807618 | 14.48403310791005 | 6.110343636363635 | 7.458523545454545 | 5.960552818181819 | 4.2234690247933875 | 8.366494983471073 | 7.1579815123966934 | 37.33629935444957 | 55.629573478099836 | 35.52818989833523 | 22.091004368014264 | 72.82379547301899 | 68.08732499721447 | 4.700106846446607 | 8.53368592537943 | 8.251504408119436 |
| user_001 | Jogging | 1.446047143687e12 | 0.805325 | 5.40376 | -1.18853 | 4.815022272727273 | 3.9789428181818183 | 1.4346706363636363 | 0.6485483556249999 | 29.2006221376 | 1.4126035609000003 | 5.591222947989554 | 7.279610887559767 | 4.009697272727273 | 1.4248171818181818 | 2.623200636363636 | 4.808407685950414 | 5.901961702479338 | 3.262293380165289 | 16.07767221891653 | 2.0301040016043057 | 6.881181578618586 | 23.86087788087626 | 40.88601350062889 | 14.184341683283828 | 4.8847597567205145 | 6.394217192168944 | 3.7662105203086864 |
| user_001 | Jogging | 1.446047143703e12 | 0.498763 | 3.67935 | -5.0972 | 3.3832365454545457 | 4.700061818181818 | 0.36518618181818213 | 0.24876453016900002 | 13.5376164225 | 25.98144784 | 6.306173863181145 | 6.765815236942559 | 2.884473545454546 | 1.0207118181818182 | 5.462386181818182 | 4.530347388429752 | 4.320377892561984 | 3.515650859504132 | 8.320187634427118 | 1.041852615776033 | 29.83766279931822 | 21.64134645781683 | 25.2878319216034 | 16.07774203478182 | 4.652026059451606 | 5.028700818462299 | 4.009705978595166 |
| user_001 | Jogging | 1.4460471438e12 | 6.36177 | -19.58108 | 6.55217 | 2.181372181818182 | -11.164546363636363 | 3.423636363636378e-2 | 40.4721175329 | 383.4186939664 | 42.930931708900005 | 21.60605802103197 | 12.990120579315441 | 4.180397818181818 | 8.416533636363637 | 6.517933636363637 | 2.872756842975207 | 9.207959504132232 | 3.702816834710744 | 17.475725918259307 | 70.83803845204051 | 42.4834588880405 | 10.08973643029246 | 92.24096030173884 | 21.23336541278622 | 3.1764345468295834 | 9.604215756725733 | 4.607967601099884 |
| user_001 | Jogging | 1.446047143865e12 | 7.66466 | -19.58108 | 2.37526 | 6.337383636363636 | -19.581079999999996 | 3.5875754545454543 | 58.747012915599996 | 383.4186939664 | 5.6418600676 | 21.1614641967327 | 21.061833583938864 | 1.3272763636363631 | 3.552713678800501e-15 | 1.2123154545454544 | 2.492719975206612 | 4.63169049586777 | 2.403411570247934 | 1.7616625454677672 | 1.2621774483536189e-29 | 1.4697087613297517 | 7.974185257215893 | 38.68784266457513 | 9.625501871091812 | 2.82385999249536 | 6.21995519795562 | 3.1024992942935237 |
| user_001 | Jogging | 1.446047143906e12 | 2.22318 | -17.58843 | 3.67815 | 5.58143 | -19.372060909090905 | 3.406424545454546 | 4.9425293124000005 | 309.35286986489996 | 13.5287874225 | 18.10591579014439 | 20.5777606506816 | 3.35825 | 1.7836309090909062 | 0.27172545454545416 | 1.7852188099173552 | 0.911135950413226 | 1.229100991735537 | 11.2778430625 | 3.1813392198644523 | 7.383472264793367e-2 | 4.562496695217112 | 2.185291967910373 | 2.2448866682423736 | 2.136000162738082 | 1.4782733062293905 | 1.4982945866025057 |
| user_001 | Jogging | 1.446047143914e12 | 2.03158 | -15.25089 | 2.91174 | 5.28531909090909 | -18.97840727272727 | 3.3541690909090907 | 4.127317296399999 | 232.58964579210001 | 8.4782298276 | 15.658709810073754 | 20.13034914244241 | 3.2537390909090904 | 3.7275172727272707 | 0.44242909090909066 | 1.9736531404958675 | 0.9219033057851264 | 1.1711456198347105 | 10.586818071709914 | 13.89438501848015 | 0.1957435004826444 | 5.398146523816002 | 2.2642876788123227 | 2.1566573067818178 | 2.32339116892012 | 1.5047550228566517 | 1.468556198033231 |
| user_001 | Jogging | 1.446047143946e12 | 0.805325 | -4.67448 | 1.80046 | 3.285695 | -15.020963636363634 | 2.7131754545454543 | 0.6485483556249999 | 21.850763270399998 | 3.2416562115999996 | 5.073555739087233 | 15.738688413790378 | 2.48037 | 10.346483636363633 | 0.9127154545454543 | 2.33848826446281 | 3.4744802479338843 | 0.8097950413223138 | 6.152235336900001 | 107.04972363754042 | 0.8330495009661153 | 7.003361392995342 | 26.536731773096072 | 0.9551943565163034 | 2.6463864783880946 | 5.15138154023715 | 0.9773404506702377 |
| user_001 | Jogging | 1.446047144043e12 | -4.78944 | 6.93658 | 0.114362 | -4.071804181818182 | 5.051913545454545 | -2.2614949999999996 | 22.938735513599998 | 48.116142096400004 | 1.3078667044000002e-2 | 8.430181271897064 | 7.023773744750437 | 0.7176358181818179 | 1.8846664545454548 | 2.3758569999999994 | 2.8974588016528924 | 7.285610578512396 | 2.8448858760330578 | 0.5150011675374871 | 3.5519676448889346 | 5.644696484448997 | 9.729573142987029 | 65.17831055224397 | 9.956481900398632 | 3.119226369308106 | 8.073308525768352 | 3.1553893421254107 |
| user_001 | Jogging | 1.446047144092e12 | -1.99206 | 4.21583 | -2.98958 | -4.1693490909090904 | 6.340868181818181 | -2.1778882727272726 | 3.9683030435999997 | 17.773222588900005 | 8.937588576400001 | 5.538873008916164 | 8.085371578268587 | 2.1772890909090905 | 2.125038181818181 | 0.8116917272727275 | 1.5590985867768594 | 2.253931280991736 | 1.5172942727272727 | 4.7405877853917335 | 4.51578727418512 | 0.6588434601229839 | 3.3676530779136438 | 8.457330240795045 | 3.1033527599789106 | 1.8351166387763052 | 2.9081489371755094 | 1.761633548720877 |
| user_001 | Jogging | 1.446047144115e12 | 3.60271 | 3.8919e-2 | -6.36177 | -2.4414504545454547 | 4.870761454545455 | -2.3311700909090907 | 12.9795193441 | 1.514688561e-3 | 40.4721175329 | 7.3111662247250955 | 6.858484763711422 | 6.044160454545455 | 4.8318424545454555 | 4.03059990909091 | 1.9356506115702479 | 2.061379578512397 | 1.4881586776859501 | 36.53187560029112 | 23.346701505547852 | 16.24573562716365 | 6.431645076172794 | 6.610973877450948 | 3.3286675864357367 | 2.536068823232681 | 2.5711814166742393 | 1.824463643495188 |
| user_001 | Jogging | 1.44604714414e12 | 6.28513 | -1.72382 | 3.60151 | 0.6938477272727271 | 3.0348691818181806 | -2.150019636363636 | 39.5028591169 | 2.9715553923999996 | 12.970874280100002 | 7.446159331454035 | 6.6932802788723516 | 5.591282272727272 | 4.7586891818181805 | 5.751529636363636 | 3.6945847933884295 | 2.4996878512396696 | 2.011975041322314 | 31.262437453314252 | 22.645122729153385 | 33.08009315796922 | 20.548499768802852 | 9.118378197963445 | 7.077055657840811 | 4.533045308487756 | 3.0196652460104656 | 2.6602736058234333 |
| user_001 | Jogging | 1.44604714418e12 | -3.56319 | -9.92436 | -3.98591 | 2.9617137272727274 | -2.009475363636364 | 0.6369127272727273 | 12.696322976100001 | 98.4929214096 | 15.8874785281 | 11.272831184480676 | 7.331966995972979 | 6.5249037272727275 | 7.914884636363636 | 4.622822727272728 | 4.575635462809917 | 4.63612326446281 | 4.079687826446281 | 42.57436865017753 | 62.64539880694513 | 21.370489967789258 | 26.438045152664895 | 23.685324490452658 | 22.525681280821853 | 5.141793962486721 | 4.866757081512561 | 4.74612276293206 |
| user_001 | Jogging | 1.446047144221e12 | 4.714 | -19.58108 | 7.6042e-2 | 3.884886 | -8.196463636363637 | 3.967295636363637 | 22.221796000000005 | 383.4186939664 | 5.782385764e-3 | 20.140662162703688 | 14.018026468325372 | 0.8291140000000006 | 11.384616363636363 | 3.8912536363636367 | 3.830131181818182 | 6.218974305785125 | 8.42825211570248 | 0.6874300249960009 | 129.60948974717684 | 15.141854862513226 | 19.765861701657393 | 44.709337568159334 | 94.92463205415766 | 4.445881431353899 | 6.686504136554418 | 9.742927283632865 |
| user_001 | Jogging | 1.446047144254e12 | -1.18733 | -19.58108 | 7.93171 | 2.818885454545455 | -14.29636090909091 | 4.907885636363637 | 1.4097525289 | 383.4186939664 | 62.9120235241 | 21.159878780829533 | 19.09860199176969 | 4.006215454545455 | 5.284719090909091 | 3.023824363636363 | 3.438377512396694 | 7.292894024793389 | 7.597556247933884 | 16.049762268238844 | 27.928255869819008 | 9.143513782120856 | 17.21997488706634 | 58.23402061541601 | 88.07572005384371 | 4.1496957583739 | 7.6311218451428235 | 9.38486654427455 |
| user_001 | Jogging | 1.446047144278e12 | 1.22685 | -19.58108 | 5.44089 | 3.083644 | -17.351535454545452 | 8.287040181818181 | 1.5051609225 | 383.4186939664 | 29.603283992099996 | 20.35993955985626 | 20.824887979127013 | 1.856794 | 2.229544545454548 | 2.8461501818181816 | 3.1305487933884297 | 6.410893917355373 | 5.5722790413223136 | 3.4476839584360004 | 4.970868880166127 | 8.100570857463667 | 14.594451388452198 | 49.33036109995855 | 50.57366699564773 | 3.8202684969059697 | 7.023557581451052 | 7.111516504631606 |
| user_001 | Jogging | 1.446047144311e12 | 11.49669 | -19.58108 | 7.39522 | 4.355181272727273 | -19.535792727272725 | 6.182906363636363 | 132.1738809561 | 383.4186939664 | 54.6892788484 | 23.880574820780595 | 21.77720408013568 | 7.1415087272727265 | 4.528727272727551e-2 | 1.2123136363636373 | 4.527814231404959 | 3.726571074380167 | 2.767612314049586 | 51.00114690171252 | 2.050937071074632e-3 | 1.4697043529132254 | 29.305284421350223 | 23.940853207647194 | 8.62882105866129 | 5.413435546984024 | 4.892939117508739 | 2.937485499310812 |
| user_001 | Jogging | 1.446047144521e12 | 2.95126 | -1.11069 | -7.47306 | 2.6133480000000002 | 2.1569868181818177 | -3.048805 | 8.7099355876 | 1.2336322760999998 | 55.8466257636 | 8.111115436689333 | 5.523819861858547 | 0.33791199999999977 | 3.2676768181818177 | 4.4242550000000005 | 3.396256438016529 | 3.6597486033057858 | 3.397523859504133 | 0.11418451974399985 | 10.677711788082847 | 19.574032305025003 | 16.3348600923496 | 20.826787642321772 | 14.775003279205595 | 4.041640767355457 | 4.5636375450206135 | 3.8438266453113616 |
| user_001 | Jogging | 1.44604714465e12 | 1.99325 | -19.58108 | 4.25296 | 3.5539354545454547 | -19.33373999999999 | 3.173021363636363 | 3.9730455625 | 383.4186939664 | 18.0876687616 | 20.136519269488954 | 20.04234972512039 | 1.5606854545454547 | 0.24734000000000833 | 1.0799386363636367 | 1.341211223140496 | 5.445601107438019 | 1.5103279338842974 | 2.4357390880297527 | 6.117707560000412e-2 | 1.1662674583109511 | 2.0902997020591165 | 46.50264074581899 | 3.8864219929065356 | 1.4457868798889817 | 6.819284474621878 | 1.9714010228531726 |
| user_001 | Jogging | 1.446047144779e12 | -0.191003 | 0.920286 | -0.230521 | 2.95823 | -5.57326509090909 | 0.24325772727272724 | 3.6482146009e-2 | 0.8469263217960001 | 5.3139931441e-2 | 0.9677543072732873 | 6.782599679498083 | 3.1492329999999997 | 6.49355109090909 | 0.47377872727272724 | 1.9058816776859502 | 7.382202495867769 | 1.6845096694214876 | 9.917668488288998 | 42.166205770246634 | 0.22446628241616526 | 4.348254975048362 | 56.287978164098156 | 4.396775017887605 | 2.0852469817861774 | 7.502531450390472 | 2.096848830480539 |
| user_001 | Jogging | 1.446047144909e12 | 3.44943 | 2.8363 | -4.59904 | -1.8422599999999998 | 5.741678181818181 | -3.3414341818181814 | 11.8985673249 | 8.04459769 | 21.151168921599997 | 6.410486248054823 | 8.646625001195995 | 5.29169 | 2.905378181818181 | 1.2576058181818182 | 1.700345950413223 | 2.538641636363636 | 4.040733280991735 | 28.0019830561 | 8.44122237938512 | 1.5815723939247603 | 5.881941722491503 | 7.297593555367826 | 31.585934417060354 | 2.4252714739780172 | 2.7014058479554355 | 5.620136512315368 |
| user_001 | Jogging | 1.446047144965e12 | -1.49389 | -10.80573 | -8.50771 | 1.8608763636363634 | -1.807423636363636 | -0.2583900000000001 | 2.2317073320999996 | 116.76380083290002 | 72.3811294441 | 13.833894520672768 | 10.624124936163746 | 3.3547663636363634 | 8.998306363636365 | 8.249319999999999 | 3.971061611570248 | 5.76831652892562 | 7.454093057851239 | 11.254457354585949 | 80.9695174138587 | 68.05128046239999 | 21.70051268411826 | 45.90407271894876 | 64.16546233071315 | 4.658380908010664 | 6.775254439425043 | 8.010334720266885 |
| user_001 | Jogging | 1.446047145006e12 | 5.2888 | -19.50444 | 1.26397 | 4.592069272727273 | -9.882558181818181 | 4.744153636363636 | 27.97140544 | 380.42317971359995 | 1.5976201609 | 20.248264254362642 | 15.73332539025025 | 0.696730727272727 | 9.621881818181818 | 3.4801836363636363 | 3.560938619834711 | 8.04219826446281 | 9.370108743801651 | 0.4854337063259831 | 92.58060972305783 | 12.111678142813222 | 18.604081060610227 | 68.5146688204746 | 105.59536052408288 | 4.3132448412546935 | 8.277358807039514 | 10.275960321258685 |
| user_001 | Jogging | 1.446047145063e12 | 3.83263 | -19.58108 | 6.51385 | 2.3694895454545457 | -17.786991818181818 | 6.966730909090909 | 14.6890527169 | 383.4186939664 | 42.430241822499994 | 20.988996843722663 | 20.711596889168277 | 1.4631404545454543 | 1.7940881818181822 | 0.4528809090909096 | 3.6613319669421487 | 5.671721735537191 | 3.724989256198347 | 2.1407799897274784 | 3.218752404139671 | 0.20510111781900875 | 18.391050523378283 | 38.52759670800473 | 38.85600186927499 | 4.288478812280443 | 6.207060230737634 | 6.233458259206922 |
| user_001 | Jogging | 1.446047145095e12 | 8.39275 | -19.58108 | 7.77842 | 3.1184786363636365 | -19.581079999999996 | 5.907696363636364 | 70.4382525625 | 383.4186939664 | 60.50381769639999 | 22.679523015824206 | 21.447303073035563 | 5.2742713636363625 | 3.552713678800501e-15 | 1.8707236363636355 | 5.020279380165289 | 3.0450398347107455 | 1.37161479338843 | 27.817938417274576 | 1.2621774483536189e-29 | 3.4996069236495835 | 31.002898462194388 | 16.330388050525777 | 2.60981791889955 | 5.568024646335035 | 4.041087483651619 | 1.6154930884716128 |
| user_001 | Jogging | 1.446047145298e12 | 2.4531 | 2.68302 | -2.18486 | 2.9512618181818184 | 2.784047636363636 | -1.7006264545454548 | 6.01769961 | 7.1985963204 | 4.7736132196000005 | 4.241451302325656 | 4.838002482542072 | 0.49816181818181837 | 0.10102763636363621 | 0.4842335454545452 | 2.036044016528926 | 4.023950479338843 | 1.7253638842975207 | 0.24816519709421506 | 1.0206583309223109e-2 | 0.2344821265434791 | 5.415028704769436 | 23.42669537789521 | 4.255417333194267 | 2.3270214233585036 | 4.84011315755068 | 2.062866290672827 |
| user_001 | Jogging | 1.446047145826e12 | 3.18118 | -19.58108 | 4.7128 | 5.462984545454545 | -11.24467181818182 | 3.880204545454545 | 10.1199061924 | 383.4186939664 | 22.21048384 | 20.389926042013983 | 16.88118003999633 | 2.2818045454545453 | 8.33640818181818 | 0.8325954545454546 | 3.494114983471074 | 8.170460165289256 | 7.975376504132232 | 5.206631983657024 | 69.4957013738851 | 0.6932151909297521 | 16.238127510167725 | 74.18152678299337 | 85.41361229590531 | 4.0296560039496825 | 8.612869834323131 | 9.241948511861843 |
| user_001 | Jogging | 1.446047145906e12 | 7.24314 | -19.58108 | 1.57053 | 1.3661963636363637 | -19.581079999999996 | 4.9705900000000005 | 52.4630770596 | 383.4186939664 | 2.4665644809 | 20.936769939675507 | 20.824778135048515 | 5.876943636363636 | 3.552713678800501e-15 | 3.4000600000000007 | 4.809673801652893 | 3.080192148760331 | 2.9028430578512396 | 34.53846650499504 | 1.2621774483536189e-29 | 11.560408003600005 | 26.078049228796736 | 17.058755092359807 | 9.280744192829529 | 5.10666713510845 | 4.130224581346614 | 3.0464313865290857 |
| user_001 | Jogging | 1.446047145914e12 | 4.79064 | -19.58108 | 2.52854 | 1.5125109090909092 | -19.581079999999996 | 4.772020909090909 | 22.9502316096 | 383.4186939664 | 6.3935145316 | 20.316555813119503 | 20.818108114239923 | 3.2781290909090908 | 3.552713678800501e-15 | 2.2434809090909087 | 4.9002487603305775 | 2.3223368595041345 | 3.0311053719008267 | 10.746130336664462 | 1.2621774483536189e-29 | 5.03320658945537 | 26.581639988161047 | 10.74096405837025 | 9.675288865422766 | 5.155738549244041 | 3.277341004285372 | 3.1105126370781337 |
| user_001 | Jogging | 1.446047145947e12 | 2.68302 | -16.24721 | 0.267643 | 3.2264732727272727 | -19.002790909090912 | 2.504155 | 7.1985963204 | 263.97183278409995 | 7.163277544900001e-2 | 16.46942809814442 | 19.77419717829964 | 0.5434532727272727 | 2.755580909090913 | 2.236512 | 2.9690325619834708 | 0.9991787603305805 | 2.7372102479338847 | 0.29534145963798347 | 7.593226146546302 | 5.001985926143999 | 13.834117142120611 | 2.145853908953498 | 7.944784113633844 | 3.719424302512502 | 1.4648733422905538 | 2.8186493420845813 |
| user_001 | Jogging | 1.446047146142e12 | 2.49142 | -2.22198 | -3.41111 | 6.225908181818181 | 3.0348706363636366 | -3.9092709090909086 | 6.207173616400001 | 4.937195120399999 | 11.635671432099999 | 4.772844033582074 | 8.453049060126027 | 3.734488181818181 | 5.2568506363636365 | 0.4981609090909087 | 2.676405371900827 | 3.922289024793389 | 1.6018522727272726 | 13.946401980139665 | 27.63447861303677 | 0.24816429134628062 | 8.224617051718033 | 20.548089963168977 | 5.414473050834811 | 2.867859315189299 | 4.533000106239683 | 2.3269020286283673 |
| user_001 | Jogging | 1.446047146191e12 | 2.2615 | -18.23987 | 1.8771 | 3.0209363636363644 | -5.601133909090908 | -1.3069736363636362 | 5.114382249999999 | 332.69285761689997 | 3.52350441 | 18.475138545540055 | 8.593185614974145 | 0.7594363636363646 | 12.638736090909092 | 3.184073636363636 | 2.3581242148760326 | 6.585709553719008 | 2.062647512396694 | 0.5767435904132245 | 159.73764997564803 | 10.13832492178595 | 6.783338460343121 | 54.960802016525165 | 6.4349872719740056 | 2.6044842983483547 | 7.413555288559273 | 2.5367276700454084 |
| user_001 | Jogging | 1.446047146222e12 | 6.70665 | -19.58108 | 1.34061 | 3.292661818181818 | -13.000438181818181 | 0.8041272727272727 | 44.9791542225 | 383.4186939664 | 1.7972351721000002 | 20.741144697460648 | 13.747680096855284 | 3.4139881818181816 | 6.580641818181819 | 0.5364827272727274 | 1.9527515702479334 | 8.756033107438014 | 2.4198804049586773 | 11.655315305594213 | 43.30484673920332 | 0.2878137166619836 | 5.276590136867767 | 83.31892515202385 | 7.355384939688656 | 2.2970829625565914 | 9.127920089046784 | 2.7120812929719964 |
| user_001 | Jogging | 1.446047146247e12 | 6.70665 | -19.58108 | -2.0699 | 4.581617272727272 | -17.348051818181816 | 1.1106894545454544 | 44.9791542225 | 383.4186939664 | 4.28448601 | 20.801017624118778 | 18.08485443258767 | 2.1250327272727274 | 2.2330281818181845 | 3.1805894545454545 | 1.8029535537190085 | 8.180911776859503 | 2.438882479338843 | 4.515764091980166 | 4.986414860794227 | 10.116149278365752 | 4.334497354961382 | 78.27161570846312 | 7.517023131813722 | 2.081945569644265 | 8.847124714191787 | 2.7417190103680795 |
| user_001 | Jogging | 1.446047146255e12 | 6.55337 | -19.58108 | -1.61005 | 4.947401818181818 | -18.389667272727266 | 0.8738004545454544 | 42.9466583569 | 383.4186939664 | 2.5922610025 | 20.71129192797494 | 19.18895665104643 | 1.6059681818181826 | 1.1914127272727342 | 2.4838504545454545 | 1.8089707438016531 | 7.583304611570248 | 2.3552742644628095 | 2.579133801012399 | 1.4194642867074545 | 6.169513080545661 | 4.353425914253344 | 72.91914457913747 | 7.02478885722426 | 2.086486499897218 | 8.539270728764691 | 2.6504318246701346 |
| user_001 | Jogging | 1.446047146271e12 | 5.74865 | -19.58108 | -0.307161 | 5.564010909090908 | -19.452184545454543 | 0.41047318181818165 | 33.04697682249999 | 383.4186939664 | 9.434787992100001e-2 | 20.409802024243668 | 20.332815201913306 | 0.18463909090909159 | 0.12889545454545726 | 0.7176341818181817 | 1.7611500826446287 | 5.651453752066117 | 1.715229809917355 | 3.4091593891735786e-2 | 1.6614038202480037e-2 | 0.5149988189138511 | 4.313352366296018 | 51.02793158470397 | 3.854774185943344 | 2.076861181277174 | 7.143383762944839 | 1.9633578853442244 |
| user_001 | Jogging | 1.446047146295e12 | 4.10087 | -19.58108 | -0.422122 | 5.940246363636363 | -19.581079999999996 | -7.566363636363579e-3 | 16.817134756899996 | 383.4186939664 | 0.178186982884 | 20.010347715774056 | 20.51464954273569 | 1.8393763636363634 | 3.552713678800501e-15 | 0.4145556363636364 | 1.8916290082644627 | 2.4962036611570273 | 1.1429585702479337 | 3.3833054071041313 | 1.2621774483536189e-29 | 0.17185637564085954 | 4.597386198170472 | 14.204368818450723 | 2.222369381335169 | 2.1441516266743994 | 3.7688683737231687 | 1.490761342849743 |
| user_001 | Jogging | 1.44604714649e12 | 0.958607 | 3.52607 | 0.344284 | -0.678715818181818 | 1.8713269090909093 | 2.333455818181818 | 0.918927380449 | 12.4331696449 | 0.11853147265599999 | 3.6702354826366386 | 3.7110363855085042 | 1.637322818181818 | 1.6547430909090906 | 1.989171818181818 | 1.7076289090909091 | 3.528951776859505 | 1.723146338842975 | 2.680826010938851 | 2.7381746969113707 | 3.95680452224876 | 3.4146557666225394 | 14.091008592012436 | 3.72855255493712 | 1.847878720755921 | 3.753799221057572 | 1.9309460258995124 |
| user_001 | Jogging | 1.446047146627e12 | 8.77595 | -15.13592 | 11.64878 | 3.8744349090909096 | -4.806858181818182 | -2.1465371818181818 | 77.0172984025 | 229.0960742464 | 135.69407548840002 | 21.01921616372266 | 10.213391746577853 | 4.90151509090909 | 10.329061818181819 | 13.795317181818183 | 3.0817764958677687 | 5.029777553719009 | 5.655888396694215 | 24.024850186409545 | 106.6895180438215 | 190.31077614696795 | 12.519304124182344 | 31.099654858346547 | 56.03006032522963 | 3.538262868157529 | 5.576706452588889 | 7.485322994048395 |
| user_001 | Jogging | 1.4460471467e12 | 0.881966 | -19.58108 | -1.45677 | 3.3065973636363637 | -18.302574545454544 | 6.210776818181818 | 0.7778640251560001 | 383.4186939664 | 2.1221788328999995 | 19.654992669152943 | 20.497246490507834 | 2.4246313636363634 | 1.2785054545454564 | 7.667546818181818 | 3.0073537190082646 | 6.956878148760333 | 7.472461669421488 | 5.8788372495291314 | 1.6345761973024842 | 58.79127420901012 | 11.837209034079082 | 60.051072653539926 | 68.10106476024005 | 3.440524528916933 | 7.7492627167711845 | 8.252336927212804 |
| user_001 | Jogging | 1.446047146708e12 | 2.18486 | -19.58108 | -0.1922 | 2.616831909090909 | -19.065497272727267 | 5.611586818181818 | 4.7736132196000005 | 383.4186939664 | 3.694084e-2 | 19.703533896892708 | 20.818008869068755 | 0.4319719090909091 | 0.5155827272727329 | 5.803786818181818 | 2.473402603305785 | 6.281047404958678 | 7.099076429752066 | 0.18659973024364462 | 0.26582554866198926 | 33.68394143090103 | 8.239763534897493 | 54.32996020046944 | 62.23338618681305 | 2.8704988303250523 | 7.370885984769364 | 7.888813991140433 |
| user_001 | Jogging | 1.446047146789e12 | 5.94025 | -12.68342 | 0.804128 | 5.170359090909091 | -17.612809090909092 | 2.316038 | 35.2865700625 | 160.8691428964 | 0.646621840384 | 14.02862554918635 | 18.616420879792923 | 0.7698909090909085 | 4.929389090909092 | 1.5119099999999999 | 2.1500545950413223 | 1.5967857024793408 | 1.880862933884297 | 0.5927320119008256 | 24.298876809573567 | 2.2858718480999998 | 5.820509118572988 | 5.918452160384152 | 5.775440422698065 | 2.4125731322745407 | 2.4327869122436825 | 2.4032146018818348 |
| user_001 | Jogging | 1.446047146846e12 | 14.94552 | 2.52974 | -7.08986 | 8.124505454545455 | -8.865327727272726 | -0.9760245454545455 | 223.36856807040002 | 6.3995844675999995 | 50.2661148196 | 16.734224432509563 | 14.08539729512732 | 6.821014545454545 | 11.395067727272727 | 6.1138354545454545 | 2.543391082644628 | 6.365605413223141 | 2.754627909090909 | 46.52623942930248 | 129.84756850913243 | 37.37898396525703 | 11.835538075822928 | 47.25518283891866 | 14.352842783440092 | 3.4402816855343294 | 6.874240528154267 | 3.7885145879935704 |
| user_001 | Jogging | 1.44604714691e12 | 2.6447 | 6.51505 | -2.14654 | 8.817753636363635 | 3.034871363636364 | -3.5852909090909093 | 6.994438089999999 | 42.44587650249999 | 4.607633971599999 | 7.351730991004771 | 10.905216461572586 | 6.173053636363635 | 3.4801786363636356 | 1.4387509090909094 | 4.208898595041322 | 7.25900756198347 | 2.4344469173553724 | 38.1065911974223 | 12.111643341001853 | 2.0700041784099183 | 22.049739906490682 | 58.57205085462563 | 13.497022673672406 | 4.69571505805992 | 7.653237932707021 | 3.673829429038916 |
| user_001 | Jogging | 1.446047146926e12 | 0.881966 | 6.09353 | -1.80165 | 6.790261454545456 | 4.644324545454546 | -2.839787272727272 | 0.7778640251560001 | 37.1311078609 | 3.2459427224999997 | 6.4152096309127735 | 9.492024266035791 | 5.908295454545455 | 1.4492054545454547 | 1.0381372727272722 | 4.2548196694214875 | 5.969101487603305 | 1.4526874958677687 | 34.90795517820249 | 2.1001964494842977 | 1.0777289970256188 | 22.276377763682646 | 44.06520485639808 | 4.615243289794645 | 4.719785775189658 | 6.63816276212011 | 2.1483117301254593 |
| user_001 | Jogging | 1.446047146991e12 | 5.71033 | -7.7401 | 3.67815 | 1.2895554545454546 | 1.2895539999999999 | 0.4069892727272728 | 32.6078687089 | 59.90914801 | 13.5287874225 | 10.29785434648403 | 5.452298577041354 | 4.420774545454545 | 9.029654 | 3.2711607272727274 | 4.018882578512398 | 4.053401785123968 | 2.1415043057851237 | 19.543247581738843 | 81.53465135971601 | 10.70049250365144 | 19.656272774198364 | 24.692266006893522 | 5.525528248539532 | 4.433539531141948 | 4.969131313106298 | 2.3506442198979265 |
| user_001 | Jogging | 1.446047147008e12 | 5.86361 | -14.5228 | 1.64717 | 1.996739090909091 | -2.2498487272727274 | 1.2430692727272727 | 34.3819222321 | 210.91171984000002 | 2.7131690089 | 15.748231998576857 | 6.790361057403035 | 3.8668709090909097 | 12.272951272727273 | 0.4041007272727273 | 3.7151708429752066 | 5.61915094214876 | 2.1373874958677685 | 14.952690627573558 | 150.62533294273797 | 0.16329739778234711 | 16.52413057886283 | 47.64363520050973 | 5.706252704011319 | 4.064988386067398 | 6.902436903044441 | 2.3887764031008256 |
| user_001 | Jogging | 1.44604714755e12 | 16.86154 | -19.58108 | -1.49509 | 6.253777272727273 | -19.581079999999996 | 3.8035627272727273 | 284.31153117160005 | 383.4186939664 | 2.2352941081 | 25.883692148650276 | 21.360767742613746 | 10.607762727272728 | 3.552713678800501e-15 | 5.298652727272727 | 3.1489165289256196 | 3.1394150413223163 | 2.8486857355371904 | 112.52463007811656 | 1.2621774483536189e-29 | 28.07572072423471 | 16.449431349785637 | 18.39730278650308 | 10.253797064157284 | 4.055789855229883 | 4.289207710813628 | 3.2021550656014903 |
| user_001 | Jogging | 1.446047147622e12 | 2.37646 | -6.36057 | -5.13552 | 6.630013181818182 | -14.672596363636364 | 0.23280781818181825 | 5.647562131599999 | 40.4568507249 | 26.373565670399998 | 8.513399939325064 | 17.26086851800695 | 4.253553181818182 | 8.312026363636363 | 5.368327818181818 | 5.596982917355372 | 3.5789906611570257 | 3.841849239669422 | 18.09271467055558 | 69.08978226978594 | 28.81894356346476 | 38.34505562899607 | 23.791265035335616 | 19.437297262372166 | 6.192338462083292 | 4.877629038306995 | 4.408775029684795 |
| user_001 | Jogging | 1.446047147639e12 | 2.10822 | -3.86975 | -4.36911 | 4.651291363636364 | -11.934435454545456 | -0.6485594545454547 | 4.444591568400001 | 14.974965062499999 | 19.0891221921 | 6.205536143074182 | 14.146659379883403 | 2.543071363636364 | 8.064685454545456 | 3.7205505454545453 | 4.807773413223141 | 5.056698264462809 | 3.875102727272727 | 6.467211960547316 | 65.03915148075706 | 13.842496361282114 | 28.276395911400765 | 35.80188864520639 | 19.261341575418438 | 5.317555445070673 | 5.983467944696152 | 4.388774495849432 |
| user_001 | Jogging | 1.446047147655e12 | 3.2195 | -0.995729 | -4.86728 | 2.839785 | -8.691143545454544 | -1.735463090909091 | 10.36518025 | 0.991476241441 | 23.6904145984 | 5.920056679613887 | 10.914676311816635 | 0.379715 | 7.695414545454544 | 3.131816909090909 | 3.883018702479339 | 6.473599669421486 | 4.033451586776859 | 0.14418348122500002 | 59.21940502639337 | 9.808277152067735 | 21.574299434066237 | 46.895448857992044 | 19.698109391720433 | 4.644814251836799 | 6.848025179421586 | 4.438255219308646 |
| user_001 | Jogging | 1.446047148035e12 | -2.52854 | 0.307161 | 1.80046 | 2.7596614545454545 | -7.064272636363637 | 0.633429 | 6.3935145316 | 9.434787992100001e-2 | 3.2416562115999996 | 3.1192176299708554 | 8.707368733999472 | 5.2882014545454545 | 7.371433636363637 | 1.167031 | 4.633590611570247 | 7.0259181818181835 | 0.9637088925619838 | 27.965074623856662 | 54.338033855313235 | 1.361961354961 | 26.853786031159323 | 51.463071213056516 | 1.4078366659209935 | 5.1820638775645484 | 7.1737766352916585 | 1.1865229310556933 |
| user_001 | Jogging | 1.446047148423e12 | 3.41111 | -10.84405 | -5.99e-4 | 4.522395454545454 | -16.672220000000003 | 2.9640000909090904 | 11.635671432099999 | 117.59342040249999 | 3.5880100000000004e-7 | 11.367897439430081 | 17.70845344887635 | 1.1112854545454542 | 5.828170000000004 | 2.9645990909090902 | 2.8486870247933878 | 2.1940752892561997 | 1.3709815702479338 | 1.2349553614842967 | 33.96756554890004 | 8.788847769819004 | 9.0019158055592 | 10.105985401013 | 2.663159615259104 | 3.0003192839361614 | 3.1789912552589694 | 1.6319189977627884 |
| user_001 | Jogging | 1.446047148585e12 | 3.10454 | -5.24928 | -0.230521 | 7.291908181818181 | -0.17706772727272738 | -1.0526655454545453 | 9.638168611600001 | 27.5549405184 | 5.3139931441e-2 | 6.102970511270803 | 8.810267540587791 | 4.187368181818181 | 5.072212272727272 | 0.8221445454545453 | 2.3407064462809912 | 2.738793338842975 | 5.057014528925619 | 17.5340522901033 | 25.72733733960516 | 0.6759216536206609 | 8.01479266604553 | 10.180405784232084 | 31.71471424852974 | 2.8310409156431366 | 3.1906748164349317 | 5.631581860235163 |
| user_001 | Jogging | 1.446047148658e12 | 8.85259 | -19.58108 | -0.996927 | 5.82877 | -13.097980909090909 | 3.5980274545454547 | 78.36834970809998 | 383.4186939664 | 0.993863443329 | 21.512343134066754 | 15.243843861466813 | 3.0238199999999997 | 6.483099090909091 | 4.594954454545455 | 1.8757956198347103 | 7.861681504132232 | 4.55061688429752 | 9.143487392399999 | 42.03057382254628 | 21.11360643934712 | 5.88666845142667 | 68.0709621098762 | 23.399370359750957 | 2.426245752479882 | 8.250512839204372 | 4.837289567490348 |
| user_001 | Jogging | 1.446047148723e12 | 11.57333 | -19.58108 | 4.5212 | 11.754482727272727 | -19.581079999999996 | 3.4377791818181818 | 133.9419672889 | 383.4186939664 | 20.441249440000004 | 23.19055649818046 | 23.48316918410363 | 0.18115272727272647 | 3.552713678800501e-15 | 1.0834208181818186 | 3.3810542975206612 | 3.4684628016528944 | 2.8087840661157024 | 3.281631059834682e-2 | 1.2621774483536189e-29 | 1.1738006692697611 | 15.367657156510823 | 23.07182841418071 | 12.982727905804962 | 3.9201603483162297 | 4.803314315572187 | 3.603155270843176 |
| user_001 | Jogging | 1.446047148933e12 | -6.55217 | 0.958607 | 0.305964 | -8.673726363636364 | 1.5926342727272726 | -1.1432400909090907 | 42.930931708900005 | 0.918927380449 | 9.361396929600001e-2 | 6.628987332816756 | 10.970671818551988 | 2.1215563636363637 | 0.6340272727272727 | 1.4492040909090909 | 6.460932644628099 | 4.056886380165289 | 2.3039676280991737 | 4.501001404085951 | 0.4019905825619834 | 2.1001924971076447 | 69.60176164731864 | 24.599329097919824 | 6.676603281846297 | 8.342767025832535 | 4.959771073136322 | 2.5839123982531405 |
| user_001 | Jogging | 1.446047148956e12 | 0.805325 | 0.996927 | -7.81794 | -5.966918636363638 | 1.8991967272727273 | -2.0733782727272723 | 0.6485483556249999 | 0.993863443329 | 61.120185843600005 | 7.92228487511993 | 9.345324426929823 | 6.772243636363638 | 0.9022697272727273 | 5.744561727272728 | 4.943003884297521 | 2.234929743801653 | 2.3356376694214878 | 45.863283870267786 | 0.8140906607528016 | 32.99998943844663 | 48.60011409208167 | 10.094791644327438 | 7.651563012286325 | 6.971378206070996 | 3.177230184347278 | 2.7661458768991785 |
| user_001 | Jogging | 1.446047148981e12 | 12.79958 | -0.650847 | -9.69564 | 5.633772727272737e-2 | 1.442836727272727 | -4.118291 | 163.82924817640003 | 0.42360181740899994 | 94.00543500959998 | 16.07041645395069 | 9.496347796961764 | 12.743242272727274 | 2.093683727272727 | 5.577348999999999 | 6.552456983471075 | 1.3722480165289257 | 3.4896825041322317 | 162.3902236214234 | 4.383511549846618 | 31.10682186780099 | 75.01246971118104 | 4.620914972535286 | 18.221982671141372 | 8.660973947032806 | 2.1496313573576487 | 4.268721432834587 |
| user_001 | Jogging | 1.446047149102e12 | 3.90927 | -19.58108 | 6.24561 | 6.884320090909092 | -13.331385454545455 | 5.482689090909091 | 15.282391932899998 | 383.4186939664 | 39.0076442721 | 20.921489673811468 | 20.6720219868055 | 2.975050090909092 | 6.249694545454545 | 0.7629209090909095 | 3.4754289752066114 | 6.580958413223141 | 10.052906661157023 | 8.850923043418195 | 39.0586819114843 | 0.5820483135280998 | 18.677426649414937 | 52.092639445531944 | 133.3166367591787 | 4.321738845582289 | 7.217523082438459 | 11.546282378288636 |
| user_001 | Jogging | 1.446047149231e12 | 9.35075 | -7.51018 | 2.49022 | 8.326558181818182 | -15.567899090909092 | 4.092707272727273 | 87.4365255625 | 56.4028036324 | 6.2011956484 | 12.249103021988997 | 18.352229744497517 | 1.0241918181818175 | 8.057719090909092 | 1.6024872727272728 | 1.0755028099173556 | 3.044405785123968 | 1.612303834710744 | 1.048968880430577 | 64.92683694800085 | 2.5679654592528927 | 2.035575883434261 | 18.39229401416228 | 3.28217159657215 | 1.4267360945298402 | 4.288623790234145 | 1.8116764602356983 |
| user_001 | Jogging | 1.446047149264e12 | 11.11349 | -2.6435 | -2.0699 | 8.939681818181818 | -10.05674090909091 | 2.490221 | 123.50965998010001 | 6.98809225 | 4.28448601 | 11.609575282502801 | 14.592364505757205 | 2.173808181818183 | 7.413240909090911 | 4.5601210000000005 | 1.1815951239669422 | 5.758498429752065 | 2.2574150991735538 | 4.725442011339674 | 54.95614077621904 | 20.794703534641005 | 2.1236015528734042 | 40.66845828931668 | 6.497162255694437 | 1.4572582313623774 | 6.377182629446697 | 2.5489531685957743 |
| user_001 | Jogging | 1.44604714928e12 | 13.22111 | -0.842448 | -3.10454 | 9.89768909090909 | -6.970213454545455 | 1.1037228181818184 | 174.7977496321 | 0.7097186327039999 | 9.638168611600001 | 13.60682317355539 | 13.2681391170847 | 3.3234209090909097 | 6.127765454545455 | 4.208262818181819 | 1.6344719834710744 | 6.779212396694215 | 2.755262090909091 | 11.045126538982649 | 37.549509465920664 | 17.709475946891583 | 3.6817334965635626 | 48.05599824849062 | 9.364926510827347 | 1.918784379903996 | 6.932243377759512 | 3.060216742459159 |
| user_001 | Jogging | 1.446047149394e12 | 2.68302 | -4.29128 | 2.49022 | 5.776515454545454 | -0.11436245454545459 | -1.007377090909091 | 7.1985963204 | 18.415084038400003 | 6.2011956484 | 5.640467711741643 | 6.993932828298204 | 3.0934954545454536 | 4.176917545454546 | 3.4975970909090908 | 4.619971983471075 | 2.365723818181818 | 2.9259617520661156 | 9.569714127293382 | 17.44664018152603 | 12.233185410335734 | 27.913373627294142 | 7.4033716166812065 | 13.02444588125051 | 5.283310858476353 | 2.7209137466449036 | 3.608939717043014 |
| user_001 | Jogging | 1.446047149402e12 | 5.86361 | -6.36057 | 5.55585 | 5.1494563636363635 | -0.9643760909090912 | -2.8467090909090942e-2 | 34.3819222321 | 40.4568507249 | 30.867469222500006 | 10.281354102427365 | 6.646319206470613 | 0.7141536363636369 | 5.396193909090909 | 5.584317090909091 | 4.641190991735538 | 2.4572493553719004 | 3.33323420661157 | 0.5100154163314058 | 29.11890870450982 | 31.184597371819375 | 27.93872818168933 | 8.299004545766735 | 15.748543563853593 | 5.285709808690724 | 2.880799289392917 | 3.9684434686478265 |
| user_001 | Jogging | 1.446047149434e12 | 6.89825 | -19.58108 | 9.04299 | 4.630389999999999 | -7.005050545454545 | 2.9082616363636364 | 47.5858530625 | 383.4186939664 | 81.7756681401 | 22.644650917357946 | 10.195556118901754 | 2.2678600000000007 | 12.576029454545456 | 6.134728363636363 | 4.597170826446281 | 5.882325909090909 | 3.9169054380165282 | 5.143188979600003 | 158.15651684159488 | 37.634892095604485 | 26.575008670971535 | 50.825509825809554 | 21.491222617181755 | 5.155095408522672 | 7.129201205311122 | 4.635862661596195 |
| user_001 | Jogging | 1.446047149563e12 | 3.98591 | -15.74905 | 2.95007 | 11.92169909090909 | -19.128203636363637 | 0.5080178181818181 | 15.8874785281 | 248.0325759025 | 8.702913004900001 | 16.511298175355567 | 23.144901652304863 | 7.93578909090909 | 3.379153636363636 | 2.442052181818182 | 5.774967107438017 | 0.8791501652892587 | 3.5599889008264465 | 62.97674849539172 | 11.418679298149584 | 5.963618858722944 | 41.11842897361307 | 2.1366249748332855 | 16.233832526013103 | 6.412365318165604 | 1.4617198687960993 | 4.029123046769992 |
| user_001 | Jogging | 1.44604714962e12 | -2.33694 | -4.21464 | -0.843646 | 2.188339454545454 | -12.40473181818182 | 3.2217923636363635 | 5.461288563599999 | 17.7631903296 | 0.711738573316 | 4.892465377140241 | 13.428669191244422 | 4.525279454545454 | 8.19009181818182 | 4.065438363636363 | 6.705104636363637 | 4.739049917355373 | 2.9405300743801646 | 20.478154141731203 | 67.07760399024879 | 16.52778908852631 | 47.59374277461837 | 31.377978538409174 | 10.302701637973604 | 6.8988218396055405 | 5.601604996642406 | 3.209782179209923 |
| user_001 | Jogging | 1.446047149669e12 | -10.34589 | -0.152682 | 0.152682 | -5.040265090909091 | -4.266891727272728 | 0.9608932727272728 | 107.03743989210001 | 2.3311793124000002e-2 | 2.3311793124000002e-2 | 10.34814299661287 | 8.821005416974698 | 5.30562490909091 | 4.114209727272727 | 0.8082112727272728 | 6.073611198347108 | 6.515719066115702 | 2.7058581157024792 | 28.149655675965928 | 16.926721679985526 | 0.653205461363438 | 38.210883633728024 | 44.103016009657594 | 9.257819553824516 | 6.181495258732148 | 6.641010164851248 | 3.042666520311504 |
| user_001 | Jogging | 1.446047149782e12 | 8.00954 | 2.52974 | -14.9072 | -2.4971918181818182 | 1.1780775454545456 | -3.1916349999999998 | 64.1527310116 | 6.3995844675999995 | 222.22461184 | 17.110725505343133 | 7.3390330778954285 | 10.506731818181818 | 1.3516624545454543 | 11.715565 | 3.812711074380165 | 0.9545242231404959 | 3.9469921652892554 | 110.3914134991942 | 1.8269913910278424 | 137.254463269225 | 20.90936161935897 | 1.1652671375526948 | 36.498496284713816 | 4.572675542760384 | 1.079475399234598 | 6.041398537152951 |
| user_001 | Jogging | 1.446047149863e12 | 15.32872 | -11.18893 | -5.05888 | 8.033928999999999 | -5.155224363636363 | -3.5748378181818183 | 234.96965683840003 | 125.19215454489998 | 25.592266854400002 | 19.640623163171274 | 15.724526838982381 | 7.294791000000002 | 6.033705636363636 | 1.484042181818182 | 7.81702794214876 | 4.484109628099174 | 8.200230900826448 | 53.213975733681025 | 36.40560370628631 | 2.20238119741567 | 78.26415031479696 | 30.749846915804653 | 98.87448495723784 | 8.846702793402578 | 5.54525445005048 | 9.943565002414267 |
| user_001 | Jogging | 1.446047149871e12 | 11.15181 | -15.21256 | 16.7837 | 8.31958990909091 | -6.768160727272726 | -0.6938469090909084 | 124.36286627609998 | 231.4219817536 | 281.69258569 | 25.248315463010595 | 16.46430774422488 | 2.8322200909090895 | 8.444399272727274 | 17.477546909090908 | 7.11934505785124 | 5.128903884297521 | 8.724047438016528 | 8.021470643349092 | 71.30787907723692 | 305.46464595947316 | 68.9577918733565 | 37.06629125091457 | 114.16631974726042 | 8.304082843599074 | 6.088209199010376 | 10.684864049077106 |
| user_001 | Jogging | 1.446047149944e12 | 9.38908 | -19.58108 | 8.58315 | 6.636980909090909 | -18.389666363636362 | 5.719578181818182 | 88.1548232464 | 383.4186939664 | 73.67046392249999 | 23.350459976953342 | 22.55196135090203 | 2.752099090909091 | 1.191413636363638 | 2.8635718181818177 | 2.407844991735537 | 5.750263537190083 | 6.912224115702478 | 7.574049406182646 | 1.4194664529132273 | 8.200043557885122 | 9.657153846656074 | 41.139247568377066 | 86.3043109583581 | 3.1075961524393856 | 6.413988429080385 | 9.290011354048934 |
| user_001 | Jogging | 1.446047150016e12 | 6.51505 | -13.14327 | 3.10335 | 7.936387272727271 | -18.02736636363636 | 4.813826363636364 | 42.44587650249999 | 172.74554629289997 | 9.6307812225 | 14.994072296007511 | 20.44310503121508 | 1.4213372727272713 | 4.8840963636363615 | 1.7104763636363645 | 2.761913388429752 | 1.6195868595041334 | 1.1109719834710745 | 2.0201996428437976 | 23.85439728928593 | 2.9257293905586805 | 10.517149891811421 | 5.401965107519985 | 1.8010637061118713 | 3.2430155552836037 | 2.324212793080699 | 1.3420371478136779 |
| user_001 | Jogging | 1.446047150073e12 | 17.89618 | 0.307161 | -19.54396 | 8.793368181818183 | -10.115964454545455 | -2.2684658181818182 | 320.27325859240005 | 9.434787992100001e-2 | 381.96637248159993 | 26.501584461196295 | 16.630296665418793 | 9.102811818181818 | 10.423125454545456 | 17.275494181818182 | 2.970298512396694 | 5.750580082644627 | 4.936670809917355 | 82.86118299723059 | 108.64154424119342 | 298.4426992260339 | 17.678004749792333 | 38.80134902204815 | 65.50860297388387 | 4.204521940695795 | 6.229072886236615 | 8.09373850416999 |
| user_001 | Jogging | 1.446047150219e12 | 7.51138 | -8.12331 | -0.383802 | 6.535954545454545 | -0.10739454545454558 | -3.163767454545454 | 56.4208295044 | 65.9881653561 | 0.147303975204 | 11.070514840589123 | 10.938402494551363 | 0.975425454545455 | 8.015915454545455 | 2.7799654545454544 | 3.8171466942148764 | 3.090644190082645 | 7.9747422314049565 | 0.9514548173752075 | 64.25490057442067 | 7.728207928466115 | 19.912643683929602 | 16.446861510478875 | 100.57568271687927 | 4.4623585337722025 | 4.0554730316547385 | 10.028742828334929 |
| user_001 | Jogging | 1.446047150243e12 | 8.1245 | -15.71073 | 7.20362 | 7.567119090909089 | -4.44804 | -3.1707347272727264 | 66.00750024999999 | 246.8270371329 | 51.8921411044 | 19.09781868400944 | 13.582559288601772 | 0.5573809090909103 | 11.26269 | 10.374354727272726 | 2.450916115702479 | 5.704342760330579 | 7.738803190082644 | 0.3106734778190096 | 126.84818603609999 | 107.62723600728596 | 11.5941647847275 | 46.82268835630012 | 97.03941247664534 | 3.405020526329834 | 6.842710600069253 | 9.850858463943402 |
| user_001 | Jogging | 1.446047150268e12 | 12.41638 | -19.4278 | 10.46085 | 8.852590909090909 | -10.168218181818181 | -5.2854727272726845e-2 | 154.1664923044 | 377.43941284000005 | 109.42938272250001 | 25.318674686225187 | 17.246433333074293 | 3.5637890909090917 | 9.25958181818182 | 10.513704727272728 | 2.286550661157025 | 8.188196363636363 | 8.710745471074379 | 12.70059268448265 | 85.73985544760333 | 110.53798709227691 | 10.773763729945234 | 76.44156760443937 | 104.44351299752908 | 3.282341196454938 | 8.7430868464427 | 10.2197609070628 |
| user_001 | Jogging | 1.446047150284e12 | 10.80693 | -19.58108 | 9.11964 | 8.702792727272728 | -14.045537272727275 | 4.956656181818182 | 116.7897360249 | 383.4186939664 | 83.1678337296 | 24.15318330408851 | 17.820332107144218 | 2.1041372727272716 | 5.535542727272725 | 4.162983818181819 | 1.7776185950413228 | 8.856742892561984 | 6.789029223140495 | 4.427393662480161 | 30.64223328546196 | 17.330434270443675 | 4.639401814140121 | 82.55556567736312 | 59.70904086977294 | 2.1539270679714577 | 9.086009337292314 | 7.727162536777193 |
| user_001 | Jogging | 1.446047150324e12 | 12.14814 | -19.58108 | 4.29128 | 11.284189090909091 | -18.926151818181815 | 5.541912636363636 | 147.57730545959998 | 383.4186939664 | 18.415084038400003 | 23.439519693551745 | 23.198834022657703 | 0.8639509090909083 | 0.6549281818181854 | 1.250632636363636 | 2.00912479338843 | 6.009955454545454 | 6.8473013553719 | 0.746411173319007 | 0.42893092333967403 | 1.5640819911378585 | 5.249374220344404 | 52.14090314037559 | 61.60130984272627 | 2.2911512870922346 | 7.220865816533055 | 7.848650192404186 |
| user_001 | Jogging | 1.446047150405e12 | 1.07357 | -10.42253 | 1.07237 | 6.981863636363636 | -17.13903090909091 | 4.709316363636364 | 1.1525525448999998 | 108.6291316009 | 1.1499774169 | 10.532410054811766 | 19.27835656772833 | 5.908293636363636 | 6.716500909090909 | 3.6369463636363637 | 3.372187024793389 | 1.9901223966942168 | 1.179696619834711 | 34.90793369349504 | 45.11138446181901 | 13.227378851967769 | 14.967308917364766 | 8.682548946544332 | 2.3495476234109898 | 3.868760643586621 | 2.9466165251936554 | 1.5328234155997846 |
| user_001 | Jogging | 1.446047150648e12 | 4.98224 | -9.69444 | 13.948 | 6.37222090909091 | -2.2289466363636365 | -8.420690909090825e-2 | 24.8227154176 | 93.9821669136 | 194.546704 | 17.701739641379884 | 12.222252634351964 | 1.3899809090909097 | 7.465493363636364 | 14.032206909090908 | 6.154051157024793 | 3.024770818181818 | 7.538651479338842 | 1.9320469276371917 | 55.73359116249859 | 196.90283073953862 | 50.009996299928396 | 14.55400636162292 | 78.89689127763887 | 7.0717746216864406 | 3.8149713447970903 | 8.88239220467318 |
| user_001 | Jogging | 1.446047150697e12 | 1.53341 | -17.51178 | 5.55585 | 7.535765454545455 | -9.994035454545456 | 3.1486354545454547 | 2.3513462280999997 | 306.6624387684001 | 30.867469222500006 | 18.435868686313647 | 16.111863223352596 | 6.002355454545455 | 7.517744545454546 | 2.4072145454545457 | 3.9055057851239665 | 6.042574842975207 | 8.693645363636366 | 36.02827100271158 | 56.51648305071158 | 5.7946818678479355 | 19.9161504314891 | 41.937659552253066 | 111.7926043798458 | 4.462751441822533 | 6.475929242375419 | 10.57320218192416 |
| user_001 | Jogging | 1.446047150793e12 | 10.46204 | -19.54276 | 6.24561 | 7.661177272727273 | -19.535792727272725 | 5.656872090909091 | 109.4542809616 | 381.91946841760006 | 39.0076442721 | 23.030010717568068 | 22.099586635479316 | 2.800862727272727 | 6.967272727276708e-3 | 0.5887379090909093 | 2.9281772727272735 | 2.3704752066115735 | 2.3156854628099173 | 7.844832017025618 | 4.8542889256253815e-5 | 0.34661232560073574 | 12.87407932845424 | 9.62765954508025 | 7.460318108028584 | 3.588046728856 | 3.102847006392718 | 2.731358289940846 |
| user_001 | Jogging | 1.446047150834e12 | 8.50771 | -11.4955 | 2.8351 | 9.511003636363638 | -17.368954545454542 | 3.9045884545454546 | 72.3811294441 | 132.14652025 | 8.03779201 | 14.579624196257598 | 20.393900266321058 | 1.0032936363636384 | 5.873454545454543 | 1.0694884545454544 | 2.6957220082644624 | 2.1975576033057864 | 1.385232033057851 | 1.0065981207677728 | 34.497468297520626 | 1.1438055544060244 | 12.06560516315159 | 8.839956388241845 | 2.4539926417881213 | 3.473557997666311 | 2.9732064153438533 | 1.5665224676933687 |
| user_001 | Jogging | 1.446047151037e12 | 5.40376 | -4.55952 | -0.728685 | 4.024230909090909 | -0.13526436363636377 | -2.6272832727272726 | 29.2006221376 | 20.7892226304 | 0.530981829225 | 7.107800404993446 | 7.222132790105045 | 1.3795290909090907 | 4.424255636363636 | 1.8985982727272726 | 2.206425950413223 | 2.662786214876033 | 5.014576909090909 | 1.9031005126644622 | 19.5740379358954 | 3.6046754012029827 | 7.852155238665514 | 8.991493953473054 | 37.35349103853367 | 2.8021697376614276 | 2.9985819904536632 | 6.111750243468205 |
| user_001 | Jogging | 1.446047151109e12 | 5.74865 | -19.58108 | 6.16897 | 5.313188181818181 | -13.79123 | 2.1592725454545456 | 33.04697682249999 | 383.4186939664 | 38.056190860899996 | 21.319518325933164 | 15.60131921150684 | 0.4354618181818184 | 5.7898499999999995 | 4.009697454545455 | 1.1201566115702482 | 7.893667628099174 | 4.2361372479338835 | 0.18962699509421507 | 33.522363022499995 | 16.077673676988297 | 1.8859984245438768 | 68.41907075554805 | 20.595874900877785 | 1.3733165784129588 | 8.271582119253127 | 4.538267830447844 |
| user_001 | Jogging | 1.446047151206e12 | 1.68669 | -14.5228 | 4.7128 | 7.905035454545454 | -18.184130909090907 | 0.5498201818181818 | 2.8449231561 | 210.91171984000002 | 22.21048384 | 15.361221528123993 | 20.60780788439442 | 6.218345454545455 | 3.661330909090907 | 4.162979818181817 | 4.84134479338843 | 1.2287837190082649 | 3.097295462809917 | 38.66782019206612 | 13.405344025864448 | 17.330400966589117 | 28.15586054618888 | 3.1095243914700963 | 17.112671343026122 | 5.306209621395378 | 1.763384357271578 | 4.136746468304061 |
| user_001 | Jogging | 1.446047151312e12 | -3.33327 | 0.613724 | -1.11189 | -0.23629118181818212 | -3.733888636363637 | 0.23977381818181834 | 11.1106888929 | 0.37665714817600005 | 1.2362993721000002 | 3.567021924964297 | 5.887372545391934 | 3.096978818181818 | 4.347612636363637 | 1.3516638181818184 | 2.5582775619834717 | 6.220242198347108 | 2.9696667768595044 | 9.59127780026685 | 18.901735635868775 | 1.8269950773818517 | 10.102695203948885 | 39.87971431446006 | 10.319315675667944 | 3.178473722393955 | 6.315038742118694 | 3.212369168646086 |
| user_001 | Jogging | 1.446047151352e12 | -0.267643 | 4.21583 | -1.91661 | -1.9084504545454544 | 0.4639268181818181 | -1.063117090909091 | 7.163277544900001e-2 | 17.773222588900005 | 3.6733938920999996 | 4.638776698273911 | 3.685062453253754 | 1.6408074545454543 | 3.7519031818181823 | 0.8534929090909089 | 2.8578729834710743 | 4.62060579338843 | 2.3017529421487604 | 2.692249102891933 | 14.0767774857374 | 0.7284501458684625 | 10.69858823466386 | 22.2415387209836 | 7.617826076039722 | 3.2708696450124486 | 4.7160935869619465 | 2.760040955500429 |
| user_001 | Jogging | 1.446047151417e12 | 6.9749 | -0.344284 | 8.19995 | 2.5680608181818183 | 3.209053363636363 | -3.9232039090909097 | 48.64923001 | 0.11853147265599999 | 67.23918000249999 | 10.770651859806629 | 9.713949174801307 | 4.406839181818182 | 3.553337363636363 | 12.12315390909091 | 3.6635484710743795 | 3.071323785123967 | 6.242726438016529 | 19.42023157440794 | 12.62620641981422 | 146.9708607035062 | 19.565297848868102 | 10.906363885450785 | 60.51857971860243 | 4.423267779466681 | 3.302478445872249 | 7.779368850916019 |
| user_001 | Jogging | 1.446047151578e12 | 9.54236 | -19.58108 | 2.60518 | 4.891664545454546 | -19.56714545454545 | 4.1031581818181815 | 91.0566343696 | 383.4186939664 | 6.7869628323999995 | 21.93769110841886 | 21.159088532938316 | 4.650695454545454 | 1.3934545454549863e-2 | 1.4979781818181817 | 4.304542066115702 | 4.257987305785124 | 1.9438857024793388 | 21.628968210929752 | 1.9417155702491625e-4 | 2.2439386332033053 | 26.518021072216154 | 29.578972161291038 | 6.378130290521789 | 5.1495651342823265 | 5.43865536334957 | 2.5254960484074784 |
| user_001 | Jogging | 1.446047151635e12 | 3.29615 | -12.56846 | 4.63616 | 7.093340909090909 | -17.65113 | 3.9045881818181813 | 10.864604822499999 | 157.9661867716 | 21.493979545600002 | 13.795824409570455 | 19.577262417347413 | 3.797190909090909 | 5.0826699999999985 | 0.7315718181818189 | 2.8806734710743807 | 1.836841239669422 | 1.3944159504132232 | 14.418658800082643 | 25.833534328899983 | 0.5351973251578523 | 10.559005899553947 | 6.313381829459578 | 2.739620745666492 | 3.2494624016218356 | 2.5126443897733672 | 1.6551799737993727 |
| user_001 | Jogging | 1.446047151724e12 | 6.82161 | 2.91294 | -5.4804 | 7.260554545454545 | -3.1416661818181826 | -2.338138363636364 | 46.53436299209999 | 8.485219443599998 | 30.034784160000005 | 9.222492428606271 | 9.907660701299607 | 0.43894454545454487 | 6.054606181818182 | 3.1422616363636364 | 1.4650392561983472 | 7.173182066115704 | 4.107240454545455 | 0.19267231398429702 | 36.65825601691095 | 9.873808191362677 | 2.9706421240818184 | 51.96965262163333 | 20.13785207873389 | 1.7235550829845323 | 7.2089980317401485 | 4.487521819304491 |
| user_001 | Jogging | 1.446047151733e12 | 7.62634 | 3.94759 | -5.02056 | 7.518345454545454 | -1.7969716363636365 | -3.101061090909091 | 58.1610617956 | 15.583466808099999 | 25.206022713599996 | 9.94738917089806 | 9.691493439543757 | 0.10799454545454612 | 5.7445616363636365 | 1.9194989090909087 | 1.2918062809917357 | 7.148796347107439 | 4.224101760330578 | 1.1662821847934029e-2 | 32.999988393980864 | 3.6844760620011887 | 2.6031193905631858 | 51.68294079364623 | 20.43626016398501 | 1.6134185416571813 | 7.189084837004375 | 4.520648201749945 |
| user_001 | Jogging | 1.446047151741e12 | 7.24314 | 4.40743 | -4.21583 | 7.685561818181816 | -0.5985916363636367 | -3.7246365454545454 | 52.4630770596 | 19.425439204899998 | 17.773222588900005 | 9.468988269788913 | 9.585143130985788 | 0.44242181818181603 | 5.006021636363636 | 0.49119345454545504 | 1.2357502479338842 | 6.958460958677687 | 4.146827280991736 | 0.1957370652033039 | 25.060252623740855 | 0.24127100978829802 | 2.5189534991681444 | 49.37879148738046 | 20.294661930347736 | 1.5871211356314754 | 7.02700444623315 | 4.504959703520969 |
| user_001 | Jogging | 1.446047151781e12 | 4.48408 | 2.52974 | -1.91661 | 6.466280000000001 | 2.944294727272727 | -4.0068127272727265 | 20.106973446399998 | 6.3995844675999995 | 3.6733938920999996 | 5.493628291584715 | 8.398280777683723 | 1.9822000000000015 | 0.41455472727272724 | 2.0902027272727266 | 1.1502414876033058 | 4.31594256198347 | 2.787565033057851 | 3.929116840000006 | 0.17185562190416526 | 4.368947441098344 | 1.8744906532619081 | 24.103478716583982 | 11.197507370263382 | 1.3691203939982444 | 4.909529378319676 | 3.346267677616867 |
| user_001 | Jogging | 1.44604715191e12 | 9.77228 | -19.50444 | 1.53221 | 4.724448181818182 | -13.442863636363633 | 1.4625413636363638 | 95.4974563984 | 380.42317971359995 | 2.3476674841 | 21.86934620870272 | 15.13713907118882 | 5.047831818181819 | 6.061576363636366 | 6.96686363636363e-2 | 1.8713614049586773 | 7.98075973553719 | 4.206682272727272 | 25.480606064648764 | 36.74270801219507 | 4.8537188927685855e-3 | 5.178756076771825 | 67.91527688974624 | 23.678325446133485 | 2.2756880446958947 | 8.241072557000468 | 4.866037961846731 |
| user_001 | Jogging | 1.446047151951e12 | 10.92189 | -19.58108 | 1.8771 | 9.577193636363639 | -18.46630818181818 | 0.8528991818181818 | 119.2876811721 | 383.4186939664 | 3.52350441 | 22.49955287441286 | 21.701183277391326 | 1.344696363636361 | 1.1147718181818185 | 1.0242008181818183 | 3.7595082644628093 | 5.658104900826447 | 5.013626694214876 | 1.8082083103768523 | 1.2427162066123973 | 1.048987315964306 | 24.23546088704974 | 43.77931753685487 | 35.968189974079955 | 4.922952456306047 | 6.616594103982416 | 5.997348578670407 |
| user_001 | Jogging | 1.446047151976e12 | 4.25415 | -19.58108 | 4.67448 | 10.047488181818183 | -19.225746363636357 | 1.0549519090909092 | 18.0977922225 | 383.4186939664 | 21.850763270399998 | 20.57589000406301 | 22.60094362279498 | 5.793338181818183 | 0.35533363636364257 | 3.619528090909091 | 4.706749008264463 | 3.0827260330578525 | 4.312143818181818 | 33.56276728891241 | 0.12626199313140937 | 13.100983600880006 | 29.278619037157853 | 16.794070458794213 | 28.49283913515609 | 5.410972097244437 | 4.098056912586038 | 5.337868407440941 |
| user_001 | Jogging | 1.446047152016e12 | 4.48408 | -14.98264 | 6.05401 | 7.438222727272728 | -18.466307272727274 | 3.2914646363636364 | 20.106973446399998 | 224.4795013696 | 36.6510370801 | 16.77013750379227 | 20.591913786123246 | 2.9541427272727283 | 3.483667272727274 | 2.7625453636363635 | 4.226635619834712 | 1.3982168595041338 | 2.6770381157024796 | 8.726959253098354 | 12.135937667071085 | 7.631656886148768 | 20.821342925891145 | 3.0201815245361403 | 8.235197835964952 | 4.563040973505623 | 1.7378669467298526 | 2.8697034404211443 |
| user_001 | Jogging | 1.446047152064e12 | 1.87829 | -2.8351 | -1.53341 | 3.9789436363636366 | -11.882180000000004 | 2.9535480000000005 | 3.5279733241 | 8.03779201 | 2.3513462280999997 | 3.7305645098563835 | 13.13876244651408 | 2.100653636363637 | 9.047080000000003 | 4.4869580000000004 | 2.783764876033059 | 5.077600413223141 | 2.743226958677686 | 4.41274569996777 | 81.84965652640005 | 20.132792093764003 | 10.053849827812401 | 36.27602943311487 | 8.753824143514345 | 3.1707806338207 | 6.022958528257925 | 2.958686219171331 |
| user_001 | Jogging | 1.446047152089e12 | -1.95374 | -0.229323 | -1.72501 | 2.4844516363636364 | -7.1304620909090906 | 1.6715598181818179 | 3.8170999876000002 | 5.2589038329e-2 | 2.9756595001 | 2.616361696331186 | 8.520474343812703 | 4.438191636363636 | 6.901139090909091 | 3.396569818181818 | 2.494937132231405 | 6.901456363636364 | 3.1270625537190084 | 19.69754500108813 | 47.62572075207355 | 11.536686529783667 | 7.469635887459421 | 51.97030174191706 | 11.108772798224885 | 2.7330634620256116 | 7.209043053132438 | 3.3329825679449456 |
| user_001 | Jogging | 1.446047152121e12 | -7.1653 | 2.41478 | -1.26517 | -0.9608938181818183 | -1.619304454545455 | -0.8958999090909091 | 51.34152409 | 5.8311624484 | 1.6006551288999997 | 7.66637734965479 | 5.000751755683819 | 6.204406181818182 | 4.034084454545455 | 0.3692700909090908 | 3.5713902892561977 | 7.143729504132231 | 2.8534371157024796 | 38.49465606898367 | 16.273837386405297 | 0.13636040004000818 | 16.102131490102952 | 53.84664377135873 | 10.161223564866225 | 4.012746128289572 | 7.338027239753115 | 3.1876674175431514 |
| user_001 | Jogging | 1.446047153934e12 | 7.47306 | -7.51018 | 2.52854 | 4.94740290909091 | -6.4894690909090915 | -0.23400418181818186 | 55.8466257636 | 56.4028036324 | 6.3935145316 | 10.89233418178124 | 11.335213146620982 | 2.52565709090909 | 1.0207109090909086 | 2.762544181818182 | 3.6483468760330577 | 7.761921628099173 | 2.983284371900827 | 6.378943740859366 | 1.041850759937189 | 7.631650356497488 | 17.440279192640055 | 74.55776113851049 | 11.886500614431673 | 4.1761560306866 | 8.634683615426248 | 3.4476804687255567 |
| user_001 | Jogging | 1.446047154257e12 | 4.17751 | 0.307161 | 0.650847 | 5.1006838181818175 | -7.677398090909091 | 0.23280609090909074 | 17.4515898001 | 9.434787992100001e-2 | 0.42360181740899994 | 4.239049362466778 | 12.33218067780529 | 0.9231738181818177 | 7.984559090909091 | 0.4180409090909092 | 3.599259553719008 | 8.311389859504134 | 3.1143959008264472 | 0.8522498985763958 | 63.753183876219005 | 0.1747582016735538 | 17.226113662722206 | 86.6462533486209 | 15.967773060603179 | 4.150435358215112 | 9.308396926894604 | 3.995969602061955 |
| user_001 | Jogging | 1.44604715471e12 | 9.00587 | -2.22198 | -1.49509 | 5.100684 | -7.464894272727275 | -0.5545014545454546 | 81.1056944569 | 4.937195120399999 | 2.2352941081 | 9.395647060495621 | 11.52705378980653 | 3.9051859999999996 | 5.242914272727274 | 0.9405885454545454 | 3.9270390578512395 | 7.693831363636364 | 1.828923950413223 | 15.250477694595997 | 27.488150071167365 | 0.8847068118402974 | 21.882637782047397 | 70.36756291734724 | 7.930507014658335 | 4.67788817545347 | 8.388537591102947 | 2.81611558971899 |
| user_001 | Jogging | 1.446047154905e12 | 6.74497 | -15.40417 | 3.48655 | 4.529362181818183 | -9.478453363636364 | 0.7449061818181818 | 45.4946203009 | 237.28845338890002 | 12.1560309025 | 17.173791211968894 | 12.247786756547718 | 2.2156078181818177 | 5.925716636363637 | 2.741643818181818 | 3.307896446280992 | 8.032380694214877 | 1.6208546280991738 | 4.908918003988394 | 35.11411765447677 | 7.5166108257745785 | 17.328900067769936 | 72.46294735240768 | 4.432801456994455 | 4.162799546911902 | 8.512517098508976 | 2.1054219189973433 |
| user_001 | Jogging | 1.44604715497e12 | 5.86361 | -4.7128 | 0.650847 | 4.682644000000001 | -9.934813454545456 | 0.7449061818181818 | 34.3819222321 | 22.21048384 | 0.42360181740899994 | 7.550894509229288 | 12.548863588071583 | 1.1809659999999997 | 5.222013454545456 | 9.405918181818185e-2 | 3.3313320991735536 | 7.781240181818182 | 1.591401743801653 | 1.3946806931559994 | 27.269424519453768 | 8.847129684305791e-3 | 17.378211958186263 | 69.14624195633812 | 4.41771863226816 | 4.16871826322987 | 8.315421934955442 | 2.1018369661484595 |
| user_001 | Jogging | 1.446047155164e12 | 5.59536 | -16.13225 | 3.7722e-2 | 4.355179454545455 | -6.994599818181817 | 8.649363636363634e-2 | 31.308053529600002 | 260.24949006249994 | 1.422949284e-3 | 17.075097848662068 | 10.670622017712152 | 1.2401805454545451 | 9.13765018181818 | 4.877163636363634e-2 | 3.3595184628099175 | 7.907918900826448 | 1.9141160082644626 | 1.5380477853239332 | 83.49665084528183 | 2.3786725135867747e-3 | 16.90470911643385 | 71.7752335717931 | 6.28171303261849 | 4.111533669621817 | 8.472026532760216 | 2.506334581140054 |
| user_001 | Jumping | 1.446047227671e12 | 0.575403 | -0.727487 | 2.95007 | 2.4530965 | -6.7246185 | -0.11555499999999985 | 0.331088612409 | 0.529237335169 | 8.702913004900001 | 3.092448698439151 | 8.451269086203546 | 1.8776935 | 5.9971315 | 3.065625 | 0.93884675 | 2.99856575 | 1.5328125 | 3.52573287994225 | 35.96558622829225 | 9.398056640624999 | 1.762866439971125 | 17.982793114146126 | 4.699028320312499 | 1.3277298068399026 | 4.240612351317452 | 2.1677242260750096 |
| user_001 | Jumping | 1.446047228771e12 | 5.63368 | -19.58108 | 5.59417 | 2.6865030909090906 | -9.102216272727274 | 1.904966727272727 | 31.7383503424 | 383.4186939664 | 31.2947379889 | 21.129405630488048 | 10.699369611977787 | 2.9471769090909095 | 10.478863727272726 | 3.6892032727272728 | 2.3082646342516067 | 7.828420587603304 | 1.485286929476584 | 8.685851733478646 | 109.80658501475205 | 13.61022078750162 | 7.602895554261843 | 72.41129287122205 | 4.47538958087852 | 2.757334864368462 | 8.509482526641795 | 2.1155116593577357 |
| user_001 | Jumping | 1.446047229678e12 | 1.64837 | -0.727487 | -0.307161 | 1.7702999999999998 | -6.9388619090909085 | 0.6334268181818182 | 2.7171236568999997 | 0.529237335169 | 9.434787992100001e-2 | 1.8277606167083258 | 8.146417896575903 | 0.12192999999999987 | 6.2113749090909085 | 0.9405878181818182 | 1.3057419917355373 | 6.445413867768594 | 0.7844572066115703 | 1.4866924899999969e-2 | 38.581178261284094 | 0.8847054437120332 | 2.9860285836722325 | 51.402171643057116 | 0.9434764262345948 | 1.7280129003199693 | 7.169530782628464 | 0.9713271468638127 |
| user_001 | Jumping | 1.446047229743e12 | 2.56806 | -1.03405 | 0.267643 | 1.8504245454545454 | -6.945829272727272 | 0.5149825454545455 | 6.5949321636 | 1.0692594024999997 | 7.163277544900001e-2 | 2.7813349926876842 | 8.172391978699084 | 0.7176354545454546 | 5.911779272727272 | 0.24733954545454545 | 1.3551465785123966 | 6.545490115702479 | 0.7524711818181818 | 0.5150006456206613 | 34.949134169447795 | 6.1176850745661156e-2 | 3.0300886047082014 | 52.47526121010706 | 0.9163994449650903 | 1.7407149694042967 | 7.243981033251472 | 0.9572875456022032 |
| user_001 | Jumping | 1.44604723039e12 | 2.68302 | 0.268841 | 0.535886 | 2.156987 | -6.151554090909091 | 0.6194927272727272 | 7.1985963204 | 7.2275483281e-2 | 0.287173804996 | 2.749189991375096 | 7.848112788192466 | 0.526033 | 6.420395090909091 | 8.360672727272722e-2 | 2.190274570247934 | 6.47613232231405 | 0.7508874214876033 | 0.27671071708899997 | 41.221473123369556 | 6.990084845256189e-3 | 7.960874463777737 | 50.38179048260929 | 1.1474568828624658 | 2.8215021644113154 | 7.0980131362663235 | 1.071194138736049 |
| user_001 | Jumping | 1.446047231296e12 | 11.61165 | -19.58108 | 4.25296 | 3.6932826363636355 | -7.416123636363636 | 0.5672378181818182 | 134.8304157225 | 383.4186939664 | 18.0876687616 | 23.158945970196918 | 9.36085068307741 | 7.918367363636364 | 12.164956363636364 | 3.6857221818181816 | 3.0409216446280993 | 7.26154073553719 | 1.760200305785124 | 62.70054170550149 | 147.98616332917686 | 13.584548001546576 | 12.618763656560139 | 62.25575293517845 | 3.835529500171806 | 3.5522899173012523 | 7.890231488060312 | 1.9584507908476552 |
| user_001 | Jumping | 1.446047231555e12 | -0.919089 | -0.382604 | 0.114362 | 2.867653454545455 | -6.116717181818182 | 0.5776896363636365 | 0.8447245899210001 | 0.146385820816 | 1.3078667044000002e-2 | 1.0020923499263927 | 8.397198505817867 | 3.786742454545455 | 5.7341131818181825 | 0.46332763636363644 | 3.6160431239669424 | 7.249506231404959 | 1.7985207438016528 | 14.339418417056937 | 32.88005398190104 | 0.2146724986183141 | 15.941718766454512 | 64.06415040944202 | 4.433685351168226 | 3.99270819951252 | 8.004008396387526 | 2.1056318175712074 |
| user_001 | Jumping | 1.446047232332e12 | 1.45677 | -0.957409 | 0.420925 | 2.8606881818181815 | -6.423278727272727 | -0.7321674545454546 | 2.1221788328999995 | 0.9166319932809999 | 0.177177855625 | 1.7933177860619125 | 8.07830938399036 | 1.4039181818181816 | 5.465869727272727 | 1.1530924545454546 | 1.8216388347107435 | 6.94674379338843 | 1.2500011074380166 | 1.970986261239669 | 29.875731875516436 | 1.3296222087296614 | 5.385024996212779 | 55.70059721558134 | 2.283652852964922 | 2.3205656629823643 | 7.463283273170149 | 1.5111759834529273 |
| user_001 | Jumping | 1.446047232397e12 | 1.18853 | -2.95007 | 0.650847 | 2.84327 | -6.649717363636362 | -0.7008144545454547 | 1.4126035609000003 | 8.702913004900001 | 0.42360181740899994 | 3.246400835264956 | 8.238312707705102 | 1.6547399999999999 | 3.6996473636363616 | 1.3516614545454546 | 1.8213221900826446 | 6.764009280991735 | 1.3415265041322315 | 2.7381644675999994 | 13.687390615261481 | 1.826988687703934 | 5.383975964246953 | 53.981179779284275 | 2.438929673971659 | 2.320339622608499 | 7.347188562932374 | 1.5617072945887327 |
| user_001 | Jumping | 1.446047233692e12 | 1.91661 | -2.95007 | -0.345482 | 2.4530986363636362 | -6.485985363636365 | 0.1108783636363636 | 3.6733938920999996 | 8.702913004900001 | 0.119357812324 | 3.534920750076867 | 7.642951055439692 | 0.5364886363636363 | 3.5359153636363647 | 0.4563603636363636 | 1.7491166859504133 | 6.342802719008264 | 0.7968080991735537 | 0.287820056947314 | 12.502697458799686 | 0.20826478149831404 | 5.789309919787663 | 48.261592607761735 | 0.9506238958732367 | 2.406098485055768 | 6.947056398775077 | 0.9749994337809826 |
| user_001 | Jumping | 1.446047233755e12 | 4.75232 | -14.63776 | -3.67935 | 2.6690859090909087 | -7.51714990909091 | -9.117436363636361e-2 | 22.5845453824 | 214.2640178176 | 13.5376164225 | 15.823595660357984 | 8.689111065763383 | 2.0832340909090914 | 7.12061009090909 | 3.588175636363636 | 1.9223500247933885 | 6.688635768595042 | 1.0153291322314049 | 4.3398642775258285 | 50.70308806675636 | 12.875004397393585 | 6.1809734275535115 | 51.871071733025275 | 1.99354172880025 | 2.486156356216059 | 7.202157436006607 | 1.411928372404298 |
| user_001 | Jumping | 1.446047234273e12 | 1.11189 | -0.152682 | 1.83878 | 2.8850731818181816 | -5.9042141818181815 | -1.0387308181818182 | 1.2362993721000002 | 2.3311793124000002e-2 | 3.3811118884000004 | 2.1542337509249085 | 7.883338389741368 | 1.7731831818181816 | 5.751532181818181 | 2.8775108181818183 | 1.935018611570248 | 6.829566181818181 | 1.9438862644628097 | 3.1441785962828503 | 33.08012243849021 | 8.280068508753397 | 7.562639685647951 | 53.59971111397245 | 5.921171536456791 | 2.75002539727326 | 7.321182357650467 | 2.433345749468577 |
| user_001 | Jumping | 1.446047235892e12 | 2.79798 | -9.88604 | -0.843646 | 2.5436736363636365 | -6.792547727272727 | -0.22703800000000005 | 7.8286920804 | 97.7337868816 | 0.711738573316 | 10.308938720126141 | 8.932443619128527 | 0.2543063636363634 | 3.0934922727272722 | 0.6166079999999999 | 2.782497404958678 | 6.824814438016529 | 1.274070082644628 | 6.467172658595029e-2 | 9.569694441423344 | 0.3802054256639999 | 12.44456348689605 | 56.63568852073141 | 2.3675355543652423 | 3.5276852873939943 | 7.525668642767326 | 1.5386798089158258 |
| user_001 | Jumping | 1.446047236475e12 | 7.58802 | -18.54643 | 3.10335 | 3.8918525454545456 | -8.22433409090909 | 0.22583972727272725 | 57.578047520400006 | 343.9700657449 | 9.6307812225 | 20.277546559872572 | 9.852919901323235 | 3.6961674545454546 | 10.32209590909091 | 2.8775102727272728 | 3.0387060082644624 | 7.119976330578512 | 1.2436688099173556 | 13.661653852041026 | 106.5456639564713 | 8.280065369650984 | 13.376589391758433 | 61.73144706318171 | 2.1075806428934363 | 3.6574020002945304 | 7.856936238966288 | 1.451750888717977 |
| user_001 | Jumping | 1.44604723654e12 | 0.767005 | -6.28393 | 0.689167 | 3.000034818181818 | -7.029436818181818 | 0.4313758181818182 | 0.5882966700250001 | 39.4877762449 | 0.47495115388899994 | 6.36796859829051 | 8.41581035264782 | 2.233029818181818 | 0.7455068181818181 | 0.2577911818181818 | 2.5535269834710745 | 6.1192146776859495 | 1.1641781074380164 | 4.986422168889123 | 0.5557804159555784 | 6.645629342321485e-2 | 8.620365385624313 | 49.222535047066316 | 1.9970901490347777 | 2.9360458759400054 | 7.015877354049621 | 1.4131844002234024 |
| user_001 | Jumping | 1.446047236669e12 | 0.11556 | -0.114362 | -1.11189 | 2.7248254545454547 | -5.9285986363636365 | 0.25719245454545453 | 1.3354113599999998e-2 | 1.3078667044000002e-2 | 1.2362993721000002 | 1.1237135545787458 | 7.6763875816918326 | 2.6092654545454548 | 5.814236636363637 | 1.3690824545454545 | 2.8306369256198347 | 6.579058876033057 | 1.2154832396694213 | 6.808266212284298 | 33.805347663633135 | 1.8743867673442065 | 9.500612024374687 | 54.4232190659533 | 2.0968215239484818 | 3.082306283349318 | 7.37720943622677 | 1.4480405809052734 |
| user_001 | Jumping | 1.446047238481e12 | -0.152682 | -1.41725 | -1.22685 | 2.965196909090909 | -5.3712112727272725 | -9.465809090909091e-2 | 2.3311793124000002e-2 | 2.0085975625 | 1.5051609225 | 1.8807100462655055 | 7.1786868769543135 | 3.117878909090909 | 3.9539612727272724 | 1.1321919090909092 | 2.806566479338843 | 6.916658347107438 | 0.8180279834710745 | 9.721168891753916 | 15.633809746227072 | 1.2818585190109175 | 11.351055486512807 | 57.72618438699079 | 0.722933934470577 | 3.3691327499095087 | 7.597774962907943 | 0.8502552172557232 |
| user_001 | Jumping | 1.4460472401e12 | 0.996927 | 3.8919e-2 | -0.575403 | 2.8502368181818176 | -5.782284454545455 | -0.1294945454545454 | 0.993863443329 | 1.514688561e-3 | 0.331088612409 | 1.1517233801130375 | 7.488380834527844 | 1.8533098181818177 | 5.821203454545455 | 0.4459084545454546 | 2.339439314049587 | 6.414693702479339 | 1.1030548595041323 | 3.434757282169122 | 33.88640965921194 | 0.19883434983511572 | 7.295192824921782 | 51.8848540825245 | 1.7815078835544063 | 2.700961463057513 | 7.20311419335585 | 1.3347313900386124 |
| user_001 | Jumping | 1.446047240295e12 | 7.39642 | -19.58108 | 1.11069 | 3.553936818181818 | -7.583338636363636 | 4.817236363636368e-2 | 54.7070288164 | 383.4186939664 | 1.2336322760999998 | 20.960900626139612 | 9.373704248824843 | 3.842483181818182 | 11.997741363636365 | 1.0625176363636362 | 2.5554266694214878 | 7.10002667768595 | 1.1068550661157026 | 14.76467700255558 | 143.945797828711 | 1.1289437275837682 | 8.508716858026242 | 63.728283208053945 | 1.7536987839118117 | 2.91697049317031 | 7.982999637232482 | 1.3242729265192321 |
| user_001 | Jumping | 1.446047241459e12 | -0.765807 | -1.41725 | 7.6042e-2 | 2.278915636363636 | -6.360573545454546 | 0.5358855454545455 | 0.586460361249 | 2.0085975625 | 5.782385764e-3 | 1.6127120975279499 | 8.104703046272826 | 3.044722636363636 | 4.943323545454546 | 0.4598435454545455 | 2.9804321652892556 | 7.0712062975206615 | 1.907149148760331 | 9.27033593238513 | 24.4364476750453 | 0.21145608629620663 | 10.990495416205771 | 58.02346179023377 | 6.027803224777757 | 3.315191610782968 | 7.617313292115125 | 2.4551584928019934 |
| user_001 | Jumping | 1.446047242042e12 | -0.995729 | 0.460442 | 0.305964 | 2.6307664545454545 | -5.716095636363637 | 1.1420436363636361 | 0.991476241441 | 0.21200683536400003 | 9.361396929600001e-2 | 1.1389016841242268 | 7.747039606303429 | 3.6264954545454544 | 6.176537636363637 | 0.8360796363636361 | 3.649297107438016 | 6.35737238016529 | 2.1823569752066114 | 13.151469281838843 | 38.1496171734165 | 0.69902915834195 | 16.822933334173815 | 47.906882437427655 | 9.32110861503425 | 4.101576932616749 | 6.921479786680566 | 3.0530490685598637 |
| user_001 | Jumping | 1.446047243531e12 | 11.57333 | -19.58108 | 0.344284 | 4.5014933636363645 | -7.2245218181818185 | 4.817318181818176e-2 | 133.9419672889 | 383.4186939664 | 0.11853147265599999 | 22.748168997261207 | 9.614007130330195 | 7.071836636363636 | 12.356558181818182 | 0.2961108181818182 | 3.602741462809917 | 7.240955603305786 | 1.341211165289256 | 50.01087341141494 | 152.68453010065784 | 8.768161664430581e-2 | 18.025288293019972 | 62.05824709530112 | 2.6538719664479 | 4.245619895023573 | 7.877705699967543 | 1.6290708905532318 |
| user_001 | Jumping | 1.446047243855e12 | -0.535886 | -0.957409 | 1.18733 | 3.3867207272727273 | -5.573264727272727 | 0.3268666363636364 | 0.287173804996 | 0.9166319932809999 | 1.4097525289 | 1.6166503416561664 | 7.784189991593638 | 3.9226067272727274 | 4.615855727272727 | 0.8604633636363637 | 3.7680581983471075 | 6.829566041322314 | 0.9785924380165288 | 15.386843536845257 | 21.306124094996438 | 0.740397200160405 | 20.30549192921104 | 54.09496022705587 | 1.5180806496353243 | 4.506161551610311 | 7.354927615351213 | 1.2321041553518617 |
| user_001 | Jumping | 1.446047244373e12 | -0.535886 | -1.18733 | 1.64717 | 4.111323636363637 | -5.952984272727273 | -0.17478236363636365 | 0.287173804996 | 1.4097525289 | 2.7131690089 | 2.10002270054302 | 8.42056546855258 | 4.647209636363637 | 4.765654272727272 | 1.8219523636363637 | 3.491582256198347 | 6.728223553719008 | 0.8411461652892562 | 21.596557404311042 | 22.711460647163708 | 3.3195104153601327 | 18.261239277957863 | 53.18916722596664 | 1.0814781705921834 | 4.273317128175472 | 7.293090375551824 | 1.0399414265198705 |
| user_001 | Standing | 1.446047262759e12 | 1.41845 | -9.11964 | 0.305964 | 1.41845 | -9.11964 | 0.305964 | 2.0120004025 | 83.1678337296 | 9.361396929600001e-2 | 9.234362354889265 | 9.234362354889265 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| user_001 | Standing | 1.446047263664e12 | 0.613724 | -9.6178 | 0.229323 | 1.0387312727272728 | -9.384396363636363 | 0.3930553636363637 | 0.37665714817600005 | 92.50207684000002 | 5.2589038329e-2 | 9.640089368180412 | 9.456826268521324 | 0.4250072727272728 | 0.23340363636363826 | 0.16373236363636368 | 0.23116370721500726 | 9.653719375573999e-2 | 0.16283225243342514 | 0.18063118187107444 | 5.447725746776948e-2 | 2.6808286901950425e-2 | 8.253073051903255e-2 | 1.3798900617681253e-2 | 3.6981783747246e-2 | 0.2872816223134236 | 0.11746872186961622 | 0.19230648389288907 |
| user_001 | Standing | 1.446047263988e12 | 1.45677 | -9.08132 | -0.575403 | 0.9516397272727274 | -9.391362727272728 | 0.23629054545454545 | 2.1221788328999995 | 82.47037294239999 | 0.331088612409 | 9.215402345405707 | 9.459287967285888 | 0.5051302727272725 | 0.3100427272727284 | 0.8116935454545454 | 0.33731404958677685 | 0.2481306611570256 | 0.28309523966942146 | 0.2551565924255287 | 9.612649273471145e-2 | 0.6588464117325702 | 0.16787532121653645 | 0.12819331652524443 | 0.13775742846689407 | 0.4097259098672385 | 0.358040942526472 | 0.37115687851216506 |
| user_001 | Standing | 1.446047265736e12 | 1.03525 | -9.46452 | -0.613724 | 0.9934438181818183 | -9.373945454545453 | -0.27580827272727276 | 1.0717425625 | 89.5771388304 | 0.37665714817600005 | 9.540730503534622 | 9.43637041659947 | 4.180618181818174e-2 | 9.057454545454746e-2 | 0.3379157272727273 | 0.18526814049586776 | 0.12287735537190142 | 0.2039529008264463 | 1.7477568382148697e-3 | 8.203748284297884e-3 | 0.11418703873825621 | 5.9142881348738546e-2 | 1.964663117896333e-2 | 5.5500906570311793e-2 | 0.24319309478013257 | 0.1401664409870042 | 0.23558630386826776 |
| user_001 | Standing | 1.446047265929e12 | 0.958607 | -9.34956 | -0.230521 | 0.9795095454545454 | -9.384396363636363 | -0.32806318181818184 | 0.918927380449 | 87.41427219360001 | 5.3139931441e-2 | 9.401400933131722 | 9.44541709001559 | 2.0902545454545396e-2 | 3.483636363636222e-2 | 9.754218181818183e-2 | 0.13142985950413222 | 9.595834710743874e-2 | 0.1992023388429752 | 4.369164064793364e-4 | 1.2135722314048601e-3 | 9.514477233851243e-3 | 2.979612839872803e-2 | 1.4155768455597456e-2 | 5.6693495851969186e-2 | 0.17261555086007757 | 0.11897801669046873 | 0.23810396017699745 |
| user_001 | Standing | 1.446047266059e12 | 1.07357 | -9.31124 | -0.537083 | 1.0317647272727273 | -9.373945454545455 | -0.35593254545454545 | 1.1525525448999998 | 86.6991903376 | 0.28845814888899995 | 9.388301285716656 | 9.440500136148358 | 4.1805272727272635e-2 | 6.270545454545484e-2 | 0.18115045454545453 | 0.1111615041322314 | 9.817520661157078e-2 | 0.18495103305785127 | 1.7476808278016453e-3 | 3.931974029752103e-3 | 3.281548718202479e-2 | 2.106930566511646e-2 | 1.492473195131493e-2 | 4.429172460069797e-2 | 0.14515269775349152 | 0.12216682017354356 | 0.2104559920760109 |
| user_001 | Standing | 1.446047266189e12 | 0.767005 | -9.15796 | 3.7722e-2 | 0.9899605454545455 | -9.346076363636364 | -0.33503045454545455 | 0.5882966700250001 | 83.86823136159998 | 1.422949284e-3 | 9.190100705700074 | 9.40808410333243 | 0.22295554545454543 | 0.18811636363636453 | 0.3727524545454545 | 0.12509620661157025 | 0.11305983471074428 | 0.20490293388429756 | 4.9709175248933875e-2 | 3.5387766267768926e-2 | 0.13894439236966113 | 2.469465301667093e-2 | 1.9301314716754423e-2 | 5.478269009800525e-2 | 0.1571453245141927 | 0.13892917158305676 | 0.23405702317598856 |
| user_001 | Standing | 1.446047267548e12 | 0.805325 | -9.92436 | -3.8919e-2 | 0.28625918181818183 | -9.429683636363636 | 0.44879372727272726 | 0.6485483556249999 | 98.4929214096 | 1.514688561e-3 | 9.957057017702871 | 9.475865926635327 | 0.5190658181818182 | 0.4946763636363638 | 0.48771272727272724 | 0.4639609173553719 | 0.21155173553719064 | 0.5874721818181817 | 0.26942932360476035 | 0.24470470474049602 | 0.23786370434380164 | 0.2774613912096176 | 6.034322433538719e-2 | 0.43992543707595644 | 0.5267460405258093 | 0.24564857894029674 | 0.6632687517710724 |
| user_001 | Standing | 1.446047268131e12 | 0.268841 | -9.77108 | 0.382604 | 0.5266323636363637 | -9.450583636363636 | 0.1979701818181818 | 7.2275483281e-2 | 95.4740043664 | 0.146385820816 | 9.782262809314469 | 9.485315427162762 | 0.2577913636363637 | 0.320496363636364 | 0.1846338181818182 | 0.33823227272727274 | 0.4072706611570247 | 0.3923870495867768 | 6.645638716549589e-2 | 0.10271791910413249 | 3.40896468163967e-2 | 0.16539073699623363 | 0.23160763757513123 | 0.22108198617076258 | 0.40668259981985166 | 0.4812563117249801 | 0.4701935624514255 |
| user_001 | Standing | 1.446047269102e12 | 0.498763 | -9.38788 | 0.305964 | 0.5649523636363637 | -9.373945454545453 | 0.20493727272727272 | 0.24876453016900002 | 88.13229089439999 | 9.361396929600001e-2 | 9.40609745823766 | 9.39505555154261 | 6.618936363636369e-2 | 1.393454545454631e-2 | 0.1010267272727273 | 9.849275206611571e-2 | 6.143867768595015e-2 | 0.12604526446280992 | 4.381031858586783e-3 | 1.9417155702481723e-4 | 1.0206399623438021e-2 | 1.6186066393682193e-2 | 7.226271014274902e-3 | 2.495586758379489e-2 | 0.12722447246376065 | 8.500747622577029e-2 | 0.1579742624094029 |
| user_001 | Standing | 1.446047269296e12 | 0.728685 | -9.50284 | 0.191003 | 0.6346258181818183 | -9.391363636363636 | 0.16661700000000002 | 0.530981829225 | 90.30396806560002 | 3.6482146009e-2 | 9.5326508401826 | 9.416083143172584 | 9.405918181818174e-2 | 0.11147636363636515 | 2.438599999999999e-2 | 0.11337746280991735 | 4.0220165289256087e-2 | 0.12192832231404958 | 8.84712968430577e-3 | 1.2426979649587114e-2 | 5.946769959999995e-4 | 1.9112998952507135e-2 | 2.688614116303524e-3 | 2.452890948348835e-2 | 0.138249770171625 | 5.185184776170974e-2 | 0.15661707915642006 |
| user_001 | Standing | 1.446047269814e12 | 0.805325 | -9.38788 | 0.114362 | 0.6276583636363636 | -9.401814545454544 | 0.14571509090909093 | 0.6485483556249999 | 88.13229089439999 | 1.3078667044000002e-2 | 9.423052473432852 | 9.42527326939873 | 0.17766663636363633 | 1.3934545454544534e-2 | 3.135309090909093e-2 | 0.13712972727272724 | 5.4471404958677964e-2 | 4.5921033057851245e-2 | 3.156543367676858e-2 | 1.941715570247677e-4 | 9.8301630955372e-4 | 2.469660010670247e-2 | 5.1499592510894174e-3 | 3.4167996044695724e-3 | 0.15715151958127058 | 7.176321656036203e-2 | 5.8453396859973604e-2 |
| user_001 | Standing | 1.446047270655e12 | 0.652044 | -9.34956 | -0.767005 | 0.6973317272727272 | -9.41574909090909 | -5.982109090909093e-2 | 0.42516137793599995 | 87.41427219360001 | 0.5882966700250001 | 9.403601982302368 | 9.447406279998765 | 4.528772727272723e-2 | 6.618909090908964e-2 | 0.7071839090909091 | 0.11876135537190079 | 6.777256198347135e-2 | 0.1973020330578512 | 2.0509782415289216e-3 | 4.380995755371733e-3 | 0.5001090812770992 | 2.098084363964838e-2 | 8.945130592937738e-3 | 7.410079386978813e-2 | 0.14484765665915475 | 9.457870052468334e-2 | 0.2722146099491872 |
| user_001 | Standing | 1.446047271109e12 | 0.652044 | -9.27292 | -7.7239e-2 | 0.6590114545454546 | -9.332141818181817 | -0.5475343636363635 | 0.42516137793599995 | 85.98704532639998 | 5.965863121e-3 | 9.296137507989917 | 9.381926009361129 | 6.9674545454546655e-3 | 5.9221818181818264e-2 | 0.47029536363636354 | 0.13998007438016533 | 9.754181818181804e-2 | 0.4522431487603306 | 4.854542284297688e-5 | 3.5072237487603405e-3 | 0.2211777290578594 | 3.434468631198724e-2 | 1.3510368677986363e-2 | 0.28600870763474 | 0.18532319420943305 | 0.11623411150770828 | 0.5347978193997616 |
| user_001 | Standing | 1.446047271885e12 | 0.652044 | -9.57948 | -5.99e-4 | 0.7495870909090908 | -9.520258181818182 | -0.3385142727272727 | 0.42516137793599995 | 91.7664370704 | 3.5880100000000004e-7 | 9.60164563015825 | 9.56080406607656 | 9.754309090909086e-2 | 5.9221818181818264e-2 | 0.3379152727272727 | 0.2087035702479339 | 0.22263603305785085 | 0.15169771074380167 | 9.514654584099164e-3 | 3.5072237487603405e-3 | 0.11418673154234707 | 6.712845657103984e-2 | 6.522730094305014e-2 | 2.9760565016948907e-2 | 0.259091598804438 | 0.2553963604733829 | 0.1725125068421096 |
| user_001 | Standing | 1.446047272015e12 | 0.307161 | -9.34956 | -0.383802 | 0.7008157272727272 | -9.461036363636364 | -0.289743 | 9.434787992100001e-2 | 87.41427219360001 | 0.147303975204 | 9.362474248227603 | 9.495551423431182 | 0.3936547272727272 | 0.11147636363636337 | 9.4059e-2 | 0.20680329752066118 | 0.18969983471074334 | 0.14504701652892563 | 0.15496404430416524 | 1.2426979649586719e-2 | 8.847095481000001e-3 | 5.4621756809489865e-2 | 5.085639922824927e-2 | 2.827003845455672e-2 | 0.23371297954861187 | 0.2255136342402589 | 0.16813696337973016 |
| user_001 | Standing | 1.44604727318e12 | 0.690364 | -9.65612 | -0.15388 | 0.6172074545454543 | -9.46452 | -0.12252709090909089 | 0.476602452496 | 93.24065345439999 | 2.3679054399999996e-2 | 9.68199023761623 | 9.489000354835825 | 7.315654545454564e-2 | 0.19159999999999933 | 3.13529090909091e-2 | 0.1944520743801653 | 0.11876033057851258 | 0.14124687603305783 | 5.351880142843002e-3 | 3.671055999999974e-2 | 9.830049084628104e-4 | 6.190883812321337e-2 | 2.0106685379414013e-2 | 3.6171692128354624e-2 | 0.2488148671667619 | 0.14179804434269894 | 0.19018856992036778 |
| user_001 | Standing | 1.446047273504e12 | 0.498763 | -9.04299 | -0.307161 | 0.5545015454545456 | -9.401813636363636 | -0.24097163636363633 | 0.24876453016900002 | 81.7756681401 | 9.434787992100001e-2 | 9.061941323479754 | 9.425355606305523 | 5.573854545454554e-2 | 0.35882363636363657 | 6.618936363636369e-2 | 0.22105472727272726 | 0.14377999999999957 | 9.975949586776861e-2 | 3.106785449388439e-3 | 0.12875440201322327 | 4.381031858586783e-3 | 6.747596271471225e-2 | 3.0185444226896983e-2 | 1.7790134395435767e-2 | 0.25976135723912486 | 0.1737395873912937 | 0.13337966260054704 |
| user_001 | Standing | 1.446047273698e12 | 0.996927 | -9.50284 | 0.114362 | 0.5649526363636365 | -9.422715454545456 | -0.17129827272727274 | 0.993863443329 | 90.30396806560002 | 1.3078667044000002e-2 | 9.555674239736986 | 9.447872353105327 | 0.43197436363636355 | 8.012454545454517e-2 | 0.28566027272727273 | 0.2508241239669422 | 0.1719658677685949 | 0.15866502479338843 | 0.18660185083904124 | 6.419942784297476e-3 | 8.160179141461985e-2 | 8.260832342466494e-2 | 3.940090960833961e-2 | 3.97539387786266e-2 | 0.28741663734840567 | 0.19849662366987408 | 0.19938389799235695 |
| user_001 | Standing | 1.44604727428e12 | 0.843646 | -10.001 | 1.18733 | 0.4674097272727273 | -9.530709090909092 | -6.330463636363635e-2 | 0.711738573316 | 100.020001 | 1.4097525289 | 10.10650741365265 | 9.557781404278169 | 0.3762362727272727 | 0.47029090909090776 | 1.2506346363636363 | 0.23973968595041326 | 0.21946917355371875 | 0.30561234710743795 | 0.14155373291571074 | 0.22117353917355248 | 1.5640869936724047 | 8.573595310997745e-2 | 6.325482515229132e-2 | 0.1940719009672727 | 0.29280702366913514 | 0.25150511953495364 | 0.4405359247181468 |
| user_001 | Standing | 1.446047274345e12 | 0.268841 | -9.6178 | 0.229323 | 0.45347509090909094 | -9.520258181818182 | -3.5435363636363626e-2 | 7.2275483281e-2 | 92.50207684000002 | 5.2589038329e-2 | 9.624289135391248 | 9.547060792013365 | 0.18463409090909094 | 9.754181818181884e-2 | 0.2647583636363636 | 0.2482904545454546 | 0.19793388429752037 | 0.31954696694214874 | 3.4089747525826455e-2 | 9.514406294215004e-3 | 7.009699111540495e-2 | 8.808920780299098e-2 | 5.395218709721989e-2 | 0.1993146053286213 | 0.29679826111854324 | 0.23227610100313784 | 0.44644664331655726 |
| user_001 | Standing | 1.446047274604e12 | 0.383802 | -9.50284 | 0.152682 | 0.2479389090909091 | -9.454067272727272 | -5.285372727272728e-2 | 0.147303975204 | 90.30396806560002 | 2.3311793124000002e-2 | 9.511812857385705 | 9.47688636967962 | 0.13586309090909088 | 4.8772727272728744e-2 | 0.2055357272727273 | 0.27615973553719014 | 0.2802762809917354 | 0.31637996694214876 | 1.845877947137189e-2 | 2.378778925619978e-3 | 4.224493518552893e-2 | 0.14886588857759583 | 0.10927961653260691 | 0.1975118552372246 | 0.3858314250778387 | 0.3305746761816563 | 0.44442305884958827 |
| user_001 | Standing | 1.446047275057e12 | -0.191003 | -9.4262 | -3.8919e-2 | -4.817263636363636e-2 | -9.349557272727273 | 8.997654545454543e-2 | 3.6482146009e-2 | 88.85324643999999 | 1.514688561e-3 | 9.428215275149904 | 9.360878751218927 | 0.14283036363636364 | 7.664272727272703e-2 | 0.12889554545454543 | 0.28724402479338845 | 0.26222611570247945 | 0.2007856859504132 | 2.040051277649587e-2 | 5.874107643801615e-3 | 1.6614061638024785e-2 | 0.1588823698457934 | 0.10760016523786636 | 6.317299137085047e-2 | 0.3986005141062834 | 0.32802464120530084 | 0.2513423787801223 |
| user_001 | Standing | 1.446047276481e12 | 0.652044 | -9.4262 | 3.7722e-2 | 0.14691254545454546 | -9.4262 | 0.14571509090909093 | 0.42516137793599995 | 88.85324643999999 | 1.422949284e-3 | 9.448800493566367 | 9.432439259405239 | 0.5051314545454545 | 0.0 | 0.10799309090909093 | 0.2055362479338843 | 9.089123966942157e-2 | 0.11496090082644628 | 0.25515778637120656 | 0.0 | 1.1662507684099177e-2 | 9.67772748796018e-2 | 1.3306267893613832e-2 | 2.066963877129452e-2 | 0.3110904609267243 | 0.11535279751100028 | 0.14376939441791678 |
| user_001 | Standing | 1.44604727661e12 | 0.652044 | -9.38788 | 0.114362 | 0.20961854545454547 | -9.398330909090909 | 0.16313336363636363 | 0.42516137793599995 | 88.13229089439999 | 1.3078667044000002e-2 | 9.411191791658482 | 9.407127586327759 | 0.44242545454545446 | 1.0450909090909732e-2 | 4.877136363636363e-2 | 0.1909682479338843 | 8.36072727272727e-2 | 0.10070957024793388 | 0.19574028282975198 | 1.092215008264597e-4 | 2.3786459109504123e-3 | 7.211494891861005e-2 | 1.1758411674830887e-2 | 1.652468511798798e-2 | 0.2685422665403159 | 0.10843621016445977 | 0.12854837656690957 |
| user_001 | Standing | 1.446047277128e12 | 0.843646 | -9.50284 | 0.152682 | 0.5231484545454544 | -9.457552727272729 | 6.210718181818182e-2 | 0.711738573316 | 90.30396806560002 | 2.3311793124000002e-2 | 9.54143691652573 | 9.47373391303917 | 0.32049754545454556 | 4.5287272727271954e-2 | 9.05748181818182e-2 | 0.23625582644628104 | 5.257123966942116e-2 | 7.600708264462812e-2 | 0.1027186766423885 | 2.05093707107431e-3 | 8.203797688669424e-3 | 8.311785452996621e-2 | 5.452249061457492e-3 | 7.488935699339596e-3 | 0.28830167278385016 | 7.383934629624975e-2 | 8.653863703190383e-2 |
| user_001 | Standing | 1.446047277193e12 | 0.460442 | -9.34956 | 0.152682 | 0.5057300909090908 | -9.450585454545454 | 7.255809090909092e-2 | 0.21200683536400003 | 87.41427219360001 | 2.3311793124000002e-2 | 9.362136018136459 | 9.465855324363725 | 4.5288090909090806e-2 | 0.10102545454545364 | 8.012390909090909e-2 | 0.19445188429752067 | 6.1755371900826035e-2 | 7.347352066115703e-2 | 2.051011178190073e-3 | 1.0206142466115519e-2 | 6.419840808008265e-3 | 6.010814769423744e-2 | 6.380080194740721e-3 | 7.0123296196949675e-3 | 0.2451696304484661 | 7.987540419140751e-2 | 8.373965380687316e-2 |
| user_001 | Standing | 1.446047277258e12 | 0.307161 | -9.27292 | -0.11556 | 0.4743770909090909 | -9.426200000000001 | 4.817245454545455e-2 | 9.434787992100001e-2 | 85.98704532639998 | 1.3354113599999998e-2 | 9.27872552239374 | 9.439863721746752 | 0.1672160909090909 | 0.1532800000000023 | 0.16373245454545454 | 0.16879945454545456 | 6.523900826446277e-2 | 8.709147107438019e-2 | 2.796122105891735e-2 | 2.3494758400000707e-2 | 2.6808316671479336e-2 | 4.429063096928475e-2 | 7.314530812922642e-3 | 9.431796205738542e-3 | 0.21045339381745487 | 8.552503032985515e-2 | 9.711743512747102e-2 |
| user_001 | Standing | 1.446047278099e12 | 0.383802 | -9.34956 | -7.7239e-2 | 0.49527909090909095 | -9.443618181818183 | -6.330454545454546e-2 | 0.147303975204 | 87.41427219360001 | 5.965863121e-3 | 9.357753043969744 | 9.457485944637822 | 0.11147709090909097 | 9.405818181818226e-2 | 1.3934454545454542e-2 | 7.949088429752069e-2 | 7.759008264462883e-2 | 6.777307438016529e-2 | 1.2427141797553732e-2 | 8.846941566942232e-3 | 1.9416902347933873e-4 | 9.047890380927874e-3 | 8.23794895627359e-3 | 7.462477452530428e-3 | 9.51203993942828e-2 | 9.076314756702519e-2 | 8.638563221120991e-2 |
| user_001 | Standing | 1.446047278552e12 | 0.575403 | -9.50284 | -0.1922 | 0.4674098181818181 | -9.429683636363636 | -4.588627272727273e-2 | 0.331088612409 | 90.30396806560002 | 3.694084e-2 | 9.52218449296216 | 9.442231526747948 | 0.1079931818181819 | 7.315636363636457e-2 | 0.1463137272727273 | 9.215876033057852e-2 | 6.618909090909174e-2 | 5.922222314049586e-2 | 1.1662527319214895e-2 | 5.351853540496005e-3 | 2.1407706788438022e-2 | 1.2904862468259955e-2 | 5.113552084147344e-3 | 5.369589683709241e-3 | 0.11359957072216406 | 7.150910490383267e-2 | 7.327748415242735e-2 |
| user_001 | Standing | 1.44604727894e12 | 0.498763 | -9.38788 | -0.11556 | 0.4221221818181818 | -9.422716363636361 | -5.9821000000000006e-2 | 0.24876453016900002 | 88.13229089439999 | 1.3354113599999998e-2 | 9.401830116427812 | 9.433455159578939 | 7.664081818181823e-2 | 3.483636363636222e-2 | 5.573899999999999e-2 | 9.057520661157026e-2 | 6.333884297520695e-2 | 7.632404132231403e-2 | 5.8738150115785195e-3 | 1.2135722314048601e-3 | 3.106836120999999e-3 | 1.256615708409317e-2 | 5.659659588279576e-3 | 7.598206086597294e-3 | 0.11209887191266989 | 7.523070907734139e-2 | 8.716768946460204e-2 |
| user_001 | Standing | 1.446047279199e12 | 0.383802 | -9.46452 | 3.7722e-2 | 0.3768345454545454 | -9.433167272727273 | -4.937000000000002e-2 | 0.147303975204 | 89.5771388304 | 1.422949284e-3 | 9.47237381836718 | 9.441813943148617 | 6.9674545454545544e-3 | 3.135272727272742e-2 | 8.709200000000002e-2 | 8.139101652892565e-2 | 5.4154710743801585e-2 | 8.70917355371901e-2 | 4.854542284297533e-5 | 9.829935074380258e-4 | 7.5850164640000025e-3 | 9.215557223067626e-3 | 5.537199117655957e-3 | 9.473758131908342e-3 | 9.599769384244408e-2 | 7.441235863521567e-2 | 9.733323241271885e-2 |
| user_001 | Standing | 1.446047279264e12 | 0.268841 | -9.4262 | -0.11556 | 0.34896527272727274 | -9.4262 | -4.240272727272727e-2 | 7.2275483281e-2 | 88.85324643999999 | 1.3354113599999998e-2 | 9.430741012077524 | 9.43350089943183 | 8.012427272727274e-2 | 0.0 | 7.315727272727272e-2 | 7.8857479338843e-2 | 4.750413223140481e-2 | 8.044114876033058e-2 | 6.419899080074382e-3 | 0.0 | 5.351986552892561e-3 | 8.738954655873033e-3 | 5.050666977610866e-3 | 8.014147201404209e-3 | 9.348237617793545e-2 | 7.106804470091228e-2 | 8.95217694273533e-2 |
| user_001 | Standing | 1.446047279458e12 | 0.575403 | -9.57948 | -0.11556 | 0.39773654545454545 | -9.433167272727273 | -5.982109090909091e-2 | 0.331088612409 | 91.7664370704 | 1.3354113599999998e-2 | 9.597441315080234 | 9.44306526313639 | 0.17766645454545454 | 0.14631272727272737 | 5.573890909090909e-2 | 0.10704340495867769 | 5.288793388429689e-2 | 9.53259173553719e-2 | 3.156536907075207e-2 | 2.14074141619835e-2 | 3.1068259866446277e-3 | 1.4841089900563487e-2 | 5.720338199849679e-3 | 1.1808280995708492e-2 | 0.12182401200323148 | 7.563291743579431e-2 | 0.10866591459932821 |
| user_001 | Standing | 1.446047279652e12 | 0.307161 | -9.34956 | -0.230521 | 0.38380181818181813 | -9.454069090909092 | -0.1120760909090909 | 9.434787992100001e-2 | 87.41427219360001 | 5.3139931441e-2 | 9.357444095743347 | 9.463956302277147 | 7.664081818181812e-2 | 0.104509090909092 | 0.1184449090909091 | 0.1007095123966942 | 6.523900826446245e-2 | 0.11021065289256195 | 5.873815011578502e-3 | 1.0922150082644855e-2 | 1.4029196489553721e-2 | 1.4068810789141245e-2 | 7.166695650187808e-3 | 1.4382200098872278e-2 | 0.11861201789507353 | 8.465633851158345e-2 | 0.11992581081181931 |
| user_001 | Walking | 1.446047179961e12 | 2.41478 | -10.92069 | -0.422122 | 2.41478 | -10.92069 | -0.422122 | 5.8311624484 | 119.26147007610001 | 0.178186982884 | 11.1924447511428 | 11.1924447511428 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| user_001 | Walking | 1.446047180025e12 | 3.48775 | -7.70178 | 0.420925 | 2.9512650000000002 | -9.311235 | -5.985000000000018e-4 | 12.1644000625 | 59.317415168400004 | 0.177177855625 | 8.465163500283087 | 9.828804125712942 | 0.5364849999999999 | 1.6094549999999996 | 0.4215235 | 0.26824249999999994 | 0.8047274999999998 | 0.21076175 | 0.28781615522499987 | 2.5903453970249988 | 0.17768206105224998 | 0.14390807761249994 | 1.2951726985124994 | 8.884103052612499e-2 | 0.3793521815048649 | 1.1380565445145945 | 0.29806212527948767 |
| user_001 | Walking | 1.446047180155e12 | 3.60271 | -4.63616 | -2.41478 | 3.1045450000000003 | -7.2132 | -0.738265 | 12.9795193441 | 21.493979545600002 | 5.8311624484 | 6.348595225567622 | 8.084041303291126 | 0.49816499999999975 | 2.5770399999999993 | 1.676515 | 0.26505 | 1.6661345833333328 | 0.6139237083333333 | 0.24816836722499974 | 6.641135161599997 | 2.810702545225 | 0.1341593312374999 | 3.8430448301256916 | 0.7790756647626735 | 0.3662776695862033 | 1.9603685444644565 | 0.882652629726255 |
| user_001 | Walking | 1.446047180867e12 | 2.8363 | -8.96635 | 0.612526 | 3.829145454545454 | -8.58315 | -0.29670972727272726 | 8.04459769 | 80.3954323225 | 0.375188100676 | 9.424182623080688 | 9.539108041248436 | 0.9928454545454541 | 0.38320000000000043 | 0.9092357272727273 | 1.4637776923455335 | 1.5073407908631777 | 0.6176389226223272 | 0.9857420966115694 | 0.14684224000000032 | 0.8267096077491654 | 3.856431482421451 | 3.619418070049718 | 0.5527401162198362 | 1.9637798966333908 | 1.902476825101877 | 0.7434649394691294 |
| user_001 | Walking | 1.446047181772e12 | 2.98958 | -10.95901 | 0.459245 | 3.8395972727272722 | -8.569218181818183 | -3.891881818181819e-2 | 8.937588576400001 | 120.09990018009998 | 0.210905970025 | 11.368746400836153 | 9.573815275666846 | 0.8500172727272721 | 2.3897918181818163 | 0.4981638181818182 | 1.1372585950413223 | 1.8580585123966946 | 1.0203968016528926 | 0.7225293639347097 | 5.711104934248751 | 0.24816718974548763 | 1.9972560146706988 | 4.208256607415629 | 1.4502039611254238 | 1.413243084069651 | 2.0514035700991724 | 1.2042441451489079 |
| user_001 | Walking | 1.446047183261e12 | 3.25783 | -10.38421 | -0.307161 | 4.146160909090909 | -8.774753636363636 | -0.3803181818181818 | 10.613456308899998 | 107.83181732409999 | 9.434787992100001e-2 | 10.887590252802545 | 9.813098538885068 | 0.8883309090909095 | 1.6094563636363635 | 7.315718181818176e-2 | 1.316192231404959 | 1.6306713223140499 | 0.43862497520661153 | 0.7891318040462817 | 2.590349786449586 | 5.351973251578504e-3 | 2.978785422953119 | 3.2734880230278747 | 0.35883803393886404 | 1.725915821514224 | 1.809278315524694 | 0.5990309123399761 |
| user_001 | Walking | 1.446047184233e12 | 0.575403 | -6.82042 | 0.267643 | 3.5748384545454552 | -8.116340909090908 | 0.20842054545454544 | 0.331088612409 | 46.51812897640001 | 7.163277544900001e-2 | 6.849879587573638 | 9.021436625002817 | 2.999435454545455 | 1.295920909090908 | 5.922245454545458e-2 | 1.6651907438016529 | 1.9701695867768594 | 0.6460616611570248 | 8.9966130459843 | 1.6794110026190057 | 3.5072991223884338e-3 | 3.910278616992638 | 4.716892291846582 | 0.5916326549642051 | 1.9774424434083127 | 2.171840761162425 | 0.7691766084354134 |
| user_001 | Walking | 1.446047185009e12 | 5.67201 | -9.08132 | 1.30229 | 2.811915363636364 | -8.774753636363636 | -0.1991684545454546 | 32.171697440100004 | 82.47037294239999 | 1.6959592440999998 | 10.786010830079858 | 9.416481422101649 | 2.860094636363636 | 0.3065663636363638 | 1.5014584545454546 | 1.2762887355371904 | 1.8564774380165294 | 0.8443138677685951 | 8.18014132895604 | 9.398293531322324e-2 | 2.254377490726025 | 2.7301153425964624 | 5.033581220307515 | 0.9013750185915839 | 1.6523060680746962 | 2.243564400748843 | 0.9494077198925569 |
| user_001 | Walking | 1.446047185592e12 | 5.05888 | -11.30389 | 0.957409 | 4.118290000000001 | -8.854876363636363 | 0.734453909090909 | 25.592266854400002 | 127.77792913210001 | 0.9166319932809999 | 12.421224898526756 | 9.898016127468361 | 0.9405899999999994 | 2.449013636363638 | 0.22295509090909094 | 1.782054132231405 | 1.4732742148760332 | 0.8367127520661157 | 0.8847095480999988 | 5.99766779109505 | 4.9708972562281004e-2 | 4.0363248511438465 | 2.844052404847483 | 0.9860011653576671 | 2.009060688765734 | 1.6864318559750593 | 0.9929759137852575 |
| user_001 | Walking | 1.446047186174e12 | 9.84892 | -9.92436 | 3.7722e-2 | 4.275055727272728 | -8.628437272727274 | -0.18523336363636364 | 97.00122516639999 | 98.4929214096 | 1.422949284e-3 | 13.98197301975955 | 9.930992956529197 | 5.573864272727271 | 1.2959227272727265 | 0.22295536363636365 | 2.105083793388429 | 1.9061979338842983 | 0.9491400082644629 | 31.067962930785512 | 1.6794157150619815 | 4.9709094174223145e-2 | 6.514026192334838 | 4.609580222217958 | 1.286088016757356 | 2.5522590370757507 | 2.1469932981306576 | 1.134058206952957 |
| user_001 | Walking | 1.446047186822e12 | 6.43841 | -10.26924 | 1.76214 | 4.160093727272727 | -9.980099090909091 | 0.7170357272727274 | 41.453123328100006 | 105.4572901776 | 3.1051373796000004 | 12.248083559696186 | 11.236332039074833 | 2.278316272727273 | 0.28914090909090895 | 1.0451042727272726 | 2.1487878099173554 | 1.5632160330578513 | 1.4099342892561983 | 5.190725038573894 | 8.360246530991727e-2 | 1.0922429408728014 | 6.734969483419675 | 4.5926103210730265 | 2.969415216736809 | 2.5951819750105534 | 2.143037638743899 | 1.7231991227762418 |
| user_001 | Walking | 1.446047187081e12 | -2.56686 | -6.05401 | 1.80046 | 2.7944973636363644 | -9.314719090909092 | 1.567050090909091 | 6.5887702596 | 36.6510370801 | 3.2416562115999996 | 6.817731554652178 | 10.290301485292778 | 5.3613573636363645 | 3.260709090909092 | 0.23340990909090897 | 2.1662055454545457 | 1.6354220661157024 | 1.1347246198347107 | 28.74415278061787 | 10.632223775537199 | 5.448018566182639e-2 | 7.208394281388505 | 4.799637820054472 | 1.7933034590091324 | 2.6848452993400764 | 2.1908075725755722 | 1.3391428075485947 |
| user_001 | Walking | 1.446047187404e12 | 1.30349 | -9.6178 | -3.79431 | 2.9268773636363634 | -9.89649272727273 | 0.9783109090909092 | 1.6990861801000001 | 92.50207684000002 | 14.396788376099998 | 10.421034084782566 | 11.359368511348634 | 1.6233873636363634 | 0.27869272727272865 | 4.772620909090909 | 3.208138504132231 | 2.1231347107438023 | 2.1525879504132233 | 2.6353865324142225 | 7.766963623471151e-2 | 22.777910341891737 | 15.854389547210962 | 6.541645332155071 | 6.480901320259005 | 3.981757092944139 | 2.5576640381713687 | 2.545761442134554 |
| user_001 | Walking | 1.446047188311e12 | 0.268841 | -6.59049 | -1.68669 | 1.3487765454545457 | -9.25201272727273 | 1.1106903636363639 | 7.2275483281e-2 | 43.4345584401 | 2.8449231561 | 6.808212473144548 | 9.785277241718822 | 1.0799355454545458 | 2.6615227272727298 | 2.797380363636364 | 1.8925786776859506 | 2.2517144628099177 | 0.9893604297520661 | 1.1662607823362074 | 7.083703227789269 | 7.825336898858317 | 4.470154434567544 | 6.05348710545417 | 1.7177182240601185 | 2.114273973393123 | 2.4603835281220223 | 1.310617497235604 |
| user_001 | Walking | 1.446047188375e12 | 6.01689 | -9.34956 | -1.53341 | 1.6170192727272725 | -9.408778181818182 | 0.9156052727272727 | 36.2029652721 | 87.41427219360001 | 2.3513462280999997 | 11.223572679579352 | 10.056389478817382 | 4.399870727272727 | 5.921818181818139e-2 | 2.4490152727272725 | 2.14720352892562 | 2.123768347107438 | 1.1936296694214876 | 19.35886241671144 | 3.5067930578511894e-3 | 5.997675806051437 | 5.997615329821671 | 5.858261339012097 | 2.2592500544313614 | 2.4490029256457966 | 2.4203845436236153 | 1.503080188955786 |
| user_001 | Walking | 1.446047189217e12 | -0.152682 | -11.53382 | 2.91174 | 1.648372090909091 | -10.011453636363637 | 1.9398018181818182 | 2.3311793124000002e-2 | 133.0290037924 | 8.4782298276 | 11.896661103567 | 10.591852408482149 | 1.801054090909091 | 1.5223663636363636 | 0.9719381818181818 | 1.4004332396694215 | 1.1701958677685953 | 0.8126441322314051 | 3.2437958383803727 | 2.3175993451314048 | 0.9446638292760331 | 2.8743965742624247 | 1.9556462032567998 | 0.9955235722866645 | 1.6954045459011913 | 1.3984442081315935 | 0.9977592757206843 |
| user_001 | Walking | 1.446047189346e12 | -0.842448 | -5.97737 | 7.6042e-2 | 1.8155880909090911 | -9.464518181818182 | 1.995540181818182 | 0.7097186327039999 | 35.7289521169 | 5.782385764e-3 | 6.036924145238865 | 10.046823549952526 | 2.658036090909091 | 3.487148181818182 | 1.919498181818182 | 1.3383605454545455 | 1.3598966942148765 | 0.8006093388429751 | 7.065155860575281 | 12.160202441957853 | 3.6844732700033065 | 2.5872731488798784 | 2.843621813011271 | 1.0801741658893318 | 1.6085002794155425 | 1.686304187568563 | 1.0393142767658547 |
| user_001 | Walking | 1.446047191677e12 | 0.537083 | -8.50651 | -0.307161 | 2.3903937272727274 | -9.610832727272728 | 2.0234110909090908 | 0.28845814888899995 | 72.36071238010001 | 9.434787992100001e-2 | 8.528981088553897 | 10.3080660888608 | 1.8533107272727274 | 1.1043227272727272 | 2.330572090909091 | 1.2826233140495868 | 1.4156352066115703 | 0.9757431983471075 | 3.4347606518241656 | 1.2195286859710741 | 5.431566270924372 | 2.484879298377201 | 2.485166069981818 | 1.4304228509468984 | 1.576349992348527 | 1.5764409503631331 | 1.1960028641048057 |
| user_001 | Walking | 1.446047191871e12 | -0.765807 | -7.08866 | 0.842448 | 2.0455110909090912 | -8.945452727272729 | 1.6785274545454547 | 0.586460361249 | 50.2491005956 | 0.7097186327039999 | 7.179504132567444 | 9.599315317951014 | 2.8113180909090913 | 1.8567927272727287 | 0.8360794545454547 | 1.8520439338842976 | 1.8403253719008268 | 1.0935538099173556 | 7.903509408272738 | 3.4476792320528977 | 0.6990288543130251 | 4.646339945674888 | 4.588313531416454 | 1.6593189163133457 | 2.1555370434476155 | 2.142034904341303 | 1.28814553382502 |
| user_001 | Walking | 1.446047192259e12 | 1.38013 | -8.96635 | 1.95374 | 1.9862877272727275 | -9.130086363636364 | 1.1490115454545455 | 1.9047588169000003 | 80.3954323225 | 3.8170999876000002 | 9.279940254495177 | 9.802935927818352 | 0.6061577272727274 | 0.1637363636363638 | 0.8047284545454545 | 2.306503066115703 | 1.7801533057851238 | 0.9453408016528926 | 0.3674271903324382 | 2.6809596776859554e-2 | 0.6475878855551156 | 8.746389046668401 | 4.834757295079263 | 1.3251393449103732 | 2.957429466051287 | 2.1988081533138044 | 1.151146969292094 |
| user_001 | Walking | 1.446047192713e12 | 3.48775 | -8.46819 | 2.72014 | 2.7074066363636367 | -9.342589090909089 | 2.371776363636364 | 12.1644000625 | 71.7102418761 | 7.399161619599999 | 9.553732441208515 | 10.136127996634109 | 0.7803433636363635 | 0.8743990909090886 | 0.348363636363636 | 0.9355228181818181 | 1.186662809917355 | 0.8322783471074383 | 0.6089357651713138 | 0.7645737701826406 | 0.1213572231404956 | 1.5311013897875918 | 2.217004014144252 | 1.0647209232821324 | 1.237376818025775 | 1.4889607161185456 | 1.0318531500567958 |
| user_001 | Walking | 1.446047192907e12 | 7.7239e-2 | -8.16163 | 0.420925 | 2.6307662727272727 | -9.30426818181818 | 2.0965664545454548 | 5.965863121e-3 | 66.61220425690001 | 0.177177855625 | 8.172842099028099 | 10.023297609841405 | 2.5535272727272726 | 1.1426381818181799 | 1.6756414545454548 | 0.840830570247934 | 1.3127081818181816 | 1.100519958677686 | 6.520501532561983 | 1.3056220145487558 | 2.8077742841912072 | 1.316778352532375 | 2.4672248687925626 | 1.6075270925270289 | 1.1475096306926469 | 1.5707402295709378 | 1.2678829175152684 |
| user_001 | Walking | 1.446047195044e12 | 6.89825 | -11.80206 | -0.728685 | 4.52936309090909 | -9.527226363636366 | 0.46272772727272726 | 47.5858530625 | 139.2886202436 | 0.530981829225 | 13.689611211985715 | 10.874214765787855 | 2.36888690909091 | 2.274833636363635 | 1.1914127272727273 | 2.0290765454545463 | 1.1739958677685949 | 1.149926256198347 | 5.611625188062285 | 5.174868073131398 | 1.419464286707438 | 6.536308023397742 | 2.107853223673479 | 2.4247353765251414 | 2.556620430059523 | 1.4518447656941422 | 1.557156182444504 |
| user_001 | Walking | 1.446047195367e12 | -0.689167 | -5.70913 | 1.41725 | 3.250857909090909 | -8.875780909090908 | 0.6264598181818182 | 0.47495115388899994 | 32.5941653569 | 2.0085975625 | 5.922644179189646 | 9.73663651646609 | 3.940024909090909 | 3.166650909090908 | 0.7907901818181817 | 1.6598082479338843 | 1.578417024793388 | 0.9950606942148762 | 15.523796284256825 | 10.027677980046274 | 0.6253491116600329 | 4.28074090199422 | 4.0350131294623575 | 1.3578484293102449 | 2.068995143057185 | 2.008734210756206 | 1.1652675355085822 |
| user_001 | Walking | 1.446047196079e12 | 4.94392 | -8.46819 | 1.22565 | 3.595740636363636 | -8.809589090909089 | 0.3756373636363637 | 24.442344966400004 | 71.7102418761 | 1.5022179224999999 | 9.882044564005973 | 9.982507618675971 | 1.3481793636363641 | 0.34139909090908915 | 0.8500126363636362 | 1.7785685785123972 | 1.1999639669421482 | 1.4507888016528925 | 1.8175875965349517 | 0.11655333927355252 | 0.7225214819778593 | 7.081309334228584 | 2.3297273267289245 | 3.3739169697874605 | 2.661072966723871 | 1.526344432534454 | 1.8368225199478203 |
| user_001 | Walking | 1.446047196533e12 | -1.76214 | -6.66714 | 0.344284 | 2.8084324545454553 | -8.987257272727273 | 0.6961345454545455 | 3.1051373796000004 | 44.4507557796 | 0.11853147265599999 | 6.904666873344144 | 9.701824413344516 | 4.570572454545456 | 2.3201172727272734 | 0.35185054545454547 | 1.2557032892561986 | 1.4583886776859503 | 1.0045616694214876 | 20.890132562249672 | 5.382944159207441 | 0.12379880633666117 | 3.183751993088845 | 3.0256084847150273 | 1.6856977704544505 | 1.7843071465106126 | 1.7394276313532067 | 1.2983442418921303 |
| user_001 | Walking | 1.446047196855e12 | 4.10087 | -12.56846 | 2.75846 | 2.508837 | -9.614316363636364 | 0.22583909090909088 | 16.817134756899996 | 157.9661867716 | 7.609101571599999 | 13.505273899484601 | 10.186220027020314 | 1.5920329999999994 | 2.9541436363636357 | 2.532620909090909 | 1.4302031735537193 | 2.04206132231405 | 1.1198392231404959 | 2.534569073088998 | 8.726964624267765 | 6.414168669164462 | 3.542454035813926 | 5.100414254760933 | 2.3829433887385965 | 1.882140811898495 | 2.2584096738105184 | 1.5436785250623255 |
| user_002 | Jogging | 1.446034900214e12 | -12.56846 | -11.8787 | -0.767005 | -8.356712727272727 | -6.796032454545454 | -1.251235818181818 | 157.9661867716 | 141.10351369 | 0.5882966700250001 | 17.310632487914038 | 12.83587713807946 | 4.211747272727273 | 5.082667545454546 | 0.4842308181818179 | 6.308627701856225 | 4.529697224016136 | 2.365780258618654 | 17.73881508932562 | 25.83350937761694 | 0.23447948527703277 | 55.93649891061648 | 28.580764721688173 | 8.90295637602717 | 7.479070725071162 | 5.346098083807308 | 2.983782226642415 |
| user_002 | Jogging | 1.446034900667e12 | 0.230521 | -0.229323 | 1.91542 | -9.39136081818182 | -6.583527272727272 | -1.310457 | 5.3139931441e-2 | 5.2589038329e-2 | 3.6688337763999996 | 1.9428233955174616 | 12.346907069341832 | 9.62188181818182 | 6.354204272727272 | 3.2258769999999997 | 5.931096528925619 | 4.556951537190082 | 2.00754094214876 | 92.58060972305788 | 40.37591193954552 | 10.406282419128997 | 44.472090029696176 | 27.521761508496457 | 6.616798714486936 | 6.668739763230844 | 5.246118708959649 | 2.5723138833522894 |
| user_002 | Jogging | 1.446034901479e12 | 3.2195 | -2.03038 | -3.44943 | 3.863981818181818 | -0.6543304545454546 | -2.5018709090909095 | 10.36518025 | 4.1224429444 | 11.8985673249 | 5.136749022416805 | 4.770432523840446 | 0.6444818181818182 | 1.3760495454545456 | 0.9475590909090905 | 3.242975231404958 | 1.182546305785124 | 1.6772268595041324 | 0.41535681396694213 | 1.8935123515456616 | 0.8978682307644621 | 15.171435204052345 | 1.8653871867810379 | 3.1895666684273536 | 3.8950526574171427 | 1.3657917801704027 | 1.7859357962780615 |
| user_002 | Jogging | 1.446034901487e12 | 2.4531 | -3.02671 | -2.41478 | 3.8395963636363635 | -0.8668343636363637 | -2.578511818181818 | 6.01769961 | 9.1609734241 | 5.8311624484 | 4.583648708452689 | 4.894646629782785 | 1.3864963636363634 | 2.1598756363636364 | 0.16373181818181815 | 2.7435442396694216 | 1.2234004545454547 | 1.544531 | 1.922372166376859 | 4.665062764557224 | 2.6808108285123956e-2 | 11.042772225629728 | 2.0235072717044216 | 2.9524234973187875 | 3.3230666899160672 | 1.4225003591227743 | 1.7182617662390056 |
| user_002 | Jogging | 1.446034901511e12 | -5.05768 | -3.75479 | 0.919089 | 1.9619021818181819 | -1.8875475454545454 | -1.6274715454545452 | 25.580126982400003 | 14.098447944099998 | 0.8447245899210001 | 6.365791350368076 | 5.146547077019307 | 7.0195821818181825 | 1.8672424545454545 | 2.546560545454545 | 2.8376038925619835 | 1.3266429338842978 | 1.7925044545454545 | 49.274534007299316 | 3.4865943840569336 | 6.48497061166575 | 12.369201859800137 | 2.4836913994747216 | 4.655043712006266 | 3.5169876115505634 | 1.5759731595032707 | 2.157555031049328 |
| user_002 | Jogging | 1.446034901705e12 | -10.88237 | -9.69444 | 0.650847 | -13.599630000000001 | -14.836330000000002 | 1.3406110000000002 | 118.4259768169 | 93.9821669136 | 0.42360181740899994 | 14.588754077984488 | 20.226008797532412 | 2.7172600000000013 | 5.141890000000002 | 0.6897640000000003 | 1.0017113223140504 | 1.4735917355371904 | 1.4628225206611574 | 7.3835019076000075 | 26.43903277210002 | 0.47577437569600034 | 1.8488825679129242 | 4.517931804138544 | 3.464271909362718 | 1.3597362126210084 | 2.125542708142686 | 1.8612554659053975 |
| user_002 | Jogging | 1.446034901818e12 | -4.9044 | 4.25415 | -4.02423 | -2.5459604545454546 | -0.9399914545454545 | -3.32749890909091 | 24.05313936 | 18.0977922225 | 16.1944270929 | 7.6384133611241545 | 6.456276805127437 | 2.3584395454545453 | 5.194141454545455 | 0.6967310909090902 | 3.4811313636363637 | 5.85255605785124 | 2.658035719008265 | 5.562237089563842 | 26.97910544982757 | 0.48543421303937095 | 19.643839108268764 | 36.150759753165694 | 9.796203638719605 | 4.432137081394117 | 6.012550187163987 | 3.1298887582020556 |
| user_002 | Jogging | 1.446034901891e12 | -2.68182 | -7.6042e-2 | 0.995729 | -2.866457272727273 | 0.28625900000000004 | -0.9202861818181817 | 7.192158512400001 | 5.782385764e-3 | 0.991476241441 | 2.861715768486626 | 3.8984117439855948 | 0.1846372727272727 | 0.36230100000000004 | 1.9160151818181816 | 0.8411455785123967 | 2.412912652892562 | 2.5782283223140494 | 3.409092248016528e-2 | 0.13126201460100004 | 3.6711141769577598 | 1.2003020330379224 | 8.579598918568538 | 7.813035155574325 | 1.095582964926857 | 2.9290952389037366 | 2.795180701774811 |
| user_002 | Jogging | 1.446034902086e12 | -12.18526 | -5.82409 | -3.90927 | -15.679373636363636 | -11.188933636363636 | -7.281458181818183 | 148.4805612676 | 33.9200243281 | 15.282391932899998 | 14.059977863730795 | 20.738969766811962 | 3.494113636363636 | 5.364843636363636 | 3.3721881818181836 | 1.291173578512396 | 2.5475080991735535 | 2.0585292561983475 | 12.208830103822311 | 28.7815472426314 | 11.371653133594227 | 3.1528208578917405 | 9.549293706104132 | 6.302081652987829 | 1.7756184437800089 | 3.0901931502907924 | 2.5103947205544848 |
| user_002 | Jogging | 1.446034902093e12 | -10.84405 | -5.36425 | -3.0279 | -15.156823636363638 | -10.258795454545453 | -6.63698 | 117.59342040249999 | 28.7751780625 | 9.16817841 | 12.471438444501901 | 19.607507902411463 | 4.312773636363639 | 4.894545454545453 | 3.6090800000000005 | 1.361796363636363 | 2.6593016528925615 | 1.946102314049587 | 18.600016438513247 | 23.956575206611554 | 13.025458446400004 | 3.7071176739448544 | 10.506175181325467 | 5.351528005717207 | 1.925387668482598 | 3.241323060314332 | 2.313336984902374 |
| user_002 | Jogging | 1.446034902134e12 | -4.7128 | -1.53221 | -3.8919e-2 | -11.13319181818182 | -5.541913636363636 | -3.292661363636364 | 22.21048384 | 2.3476674841 | 1.514688561e-3 | 4.955770980650841 | 12.951905235740183 | 6.42039181818182 | 4.009703636363636 | 3.253742363636364 | 3.776608512396695 | 4.219351074380165 | 2.819235206611571 | 41.22143109897605 | 16.077723251467763 | 10.586839368921954 | 18.97191993703419 | 18.784659065863558 | 9.502431067894996 | 4.3556767484553065 | 4.334127255384129 | 3.0826013475464187 |
| user_002 | Jogging | 1.446034902207e12 | 1.57173 | 1.22685 | -1.15021 | -1.2779061818181818 | 1.5125097272727273 | -0.7844235454545454 | 2.4703351929000004 | 1.5051609225 | 1.3229830441 | 2.3018425574960597 | 3.8283394853389723 | 2.849636181818182 | 0.28565972727272726 | 0.36578645454545455 | 5.201743809917356 | 3.6169951900826445 | 1.362746347107438 | 8.120426368727308 | 8.160147978552892e-2 | 0.13379973032893389 | 28.055339392748365 | 15.483652908929882 | 3.2315638527796566 | 5.296729122085475 | 3.9349273066893984 | 1.7976550983933643 |
| user_002 | Jogging | 1.446034902231e12 | -1.57053 | -0.344284 | -1.11189 | -0.11784572727272725 | 1.8573923636363632 | -1.1467244545454547 | 2.4665644809 | 0.11853147265599999 | 1.2362993721000002 | 1.9548389513348663 | 2.9538514725909635 | 1.4526842727272726 | 2.201676363636363 | 3.483445454545464e-2 | 3.776608099173554 | 2.949714570247934 | 0.648277909090909 | 2.110291596229165 | 4.847378810195039 | 1.2134392234793455e-3 | 17.801429361908134 | 11.44855536925793 | 0.6513529155226738 | 4.219174014177199 | 3.383571392664551 | 0.8070643812749227 |
| user_002 | Jogging | 1.446034902256e12 | -3.83143 | -3.06503 | 0.765807 | -0.5184667272727271 | 0.7217173636363636 | -0.955123 | 14.6798558449 | 9.3944089009 | 0.586460361249 | 4.965956615502093 | 3.1629018655347987 | 3.312963272727273 | 3.7867473636363638 | 1.72093 | 3.1571484297520653 | 2.450917454545454 | 0.6970493388429753 | 10.975725646439804 | 14.339455596006951 | 2.9616000649000003 | 12.507239421669167 | 7.526855335243767 | 0.6687449028157296 | 3.5365575665707984 | 2.743511497195477 | 0.8177682451744686 |
| user_002 | Jogging | 1.446034902345e12 | -15.97897 | -10.69077 | 6.39889 | -5.165675545454545 | -10.286663636363636 | 1.2361025454545453 | 255.3274822609 | 114.2925631929 | 40.945793232099994 | 20.26242430426083 | 12.469106604092127 | 10.813294454545456 | 0.4041063636363642 | 5.162787454545454 | 3.1954709173553724 | 5.691991082644628 | 2.7904146694214877 | 116.92733696070351 | 0.16330195313140541 | 26.65437430081193 | 25.07221924340757 | 44.56485109069431 | 11.683520211182929 | 5.007216716241426 | 6.675691057163618 | 3.4181164712722896 |
| user_002 | Jogging | 1.446034902498e12 | -6.82042 | -10.001 | -1.83997 | -16.61648272727273 | -13.056175454545455 | -2.1430520000000004 | 46.51812897640001 | 100.020001 | 3.3854896009 | 12.244330099164266 | 21.356799196416535 | 9.796062727272728 | 3.0551754545454557 | 0.3030820000000003 | 5.281235619834711 | 2.6536024793388435 | 0.7819239338842976 | 95.96284495666201 | 9.334097058057031 | 9.185869872400018e-2 | 33.41177882625298 | 8.344735970507742 | 0.8624901692798557 | 5.780292278618182 | 2.888725665498152 | 0.9287034883534441 |
| user_002 | Jogging | 1.446034902563e12 | -6.13065 | 2.56806 | -0.843646 | -5.507074545454545 | -5.120387272727272 | -1.7145605454545458 | 37.5848694225 | 6.5949321636 | 0.711738573316 | 6.700114936284003 | 8.609060382124232 | 0.6235754545454553 | 7.688447272727272 | 0.8709145454545458 | 6.654433884297521 | 5.132704710743802 | 0.6333935123966944 | 0.38884634751157116 | 59.112221465507425 | 0.7584921454842982 | 56.966221893599545 | 28.942580369569132 | 0.4629910177542008 | 7.5475970940160515 | 5.379830886707233 | 0.6804344331044695 |
| user_002 | Jogging | 1.446034902636e12 | -1.91542 | -3.21831 | -0.805325 | -4.4550090909090905 | 2.167436727272727 | -3.0209355454545457 | 3.6688337763999996 | 10.357519256099998 | 0.6485483556249999 | 3.830783390916928 | 6.343693907563632 | 2.5395890909090904 | 5.385746727272727 | 2.215610545454546 | 0.8652156198347104 | 4.714664330578511 | 1.6072363801652896 | 6.44951275066446 | 29.006267810328886 | 4.90893008912939 | 1.2775306625186316 | 27.85260437570646 | 3.7260181570074264 | 1.1302790197639836 | 5.277556667218881 | 1.9302896562452554 |
| user_002 | Jogging | 1.446034902724e12 | -10.72909 | -13.06663 | -2.18486 | -2.8072346363636367 | -8.112857272727272 | -0.1643308181818182 | 115.11337222809999 | 170.7368195569 | 4.7736132196000005 | 17.047692072670717 | 9.016976973428022 | 7.921855363636363 | 4.953772727272728 | 2.0205291818181816 | 3.2974469504132227 | 5.105151702479339 | 2.174439933884298 | 62.755792402374205 | 24.539864233471082 | 4.0825381745788505 | 15.810664296136576 | 26.721917705194425 | 5.090688727973939 | 3.9762626040210893 | 5.1693246856039545 | 2.256255466026385 |
| user_002 | Jogging | 1.446034902838e12 | -14.75272 | -6.20729 | -5.63368 | -16.456233636363635 | -10.419043636363636 | -6.685749999999999 | 217.6427473984 | 38.530449144100004 | 31.7383503424 | 16.967956473450183 | 20.801511960308556 | 1.7035136363636347 | 4.211753636363635 | 1.0520699999999987 | 3.477331760330579 | 2.66595388429752 | 2.398978809917355 | 2.9019587092768537 | 17.738868693422305 | 1.1068512848999974 | 16.279098479944164 | 8.922266403834108 | 8.364879422591338 | 4.034736482094483 | 2.98701630458123 | 2.8922101276690353 |
| user_002 | Jogging | 1.446034902895e12 | -6.28393 | -1.49389 | 1.49389 | -12.14694 | -4.357465454545455 | -3.3414327272727267 | 39.4877762449 | 2.2317073320999996 | 2.2317073320999996 | 6.6295694361775865 | 13.545898291325244 | 5.863010000000001 | 2.863575454545455 | 4.835322727272727 | 3.6717825619834716 | 4.083489504132231 | 2.464850578512396 | 34.37488626010001 | 8.20006438387521 | 23.380345876880163 | 17.17902520682983 | 17.189657202903156 | 8.621513752262207 | 4.144758763405879 | 4.14604114824047 | 2.9362414328972006 |
| user_002 | Jogging | 1.446034903033e12 | -2.60518 | -5.2876 | 1.49389 | 3.620127363636364 | -2.727110272727273 | -1.8957118181818187 | 6.7869628323999995 | 27.958713760000002 | 2.2317073320999996 | 6.080903216176032 | 6.2024957720086515 | 6.225307363636364 | 2.5604897272727274 | 3.3896018181818186 | 4.484111008264463 | 2.532939611570248 | 2.103498991735538 | 38.75445177174513 | 6.556107643469166 | 11.489400485821491 | 24.000768885446554 | 7.253680017838164 | 5.325640246205652 | 4.89905795898013 | 2.693265679029487 | 2.307734873464812 |
| user_002 | Jogging | 1.446034903138e12 | -12.41518 | -11.72542 | -7.7239e-2 | -12.181777272727272 | -11.728901818181818 | 0.26764363636363636 | 154.1366944324 | 137.4854741764 | 5.965863121e-3 | 17.07712313218831 | 17.103313548749245 | 0.23340272727272726 | 3.4818181818181415e-3 | 0.34488263636363636 | 5.954216669421488 | 3.747789917355372 | 1.2268833388429752 | 5.4476833098347104e-2 | 1.2123057851239389e-5 | 0.11894403286513223 | 41.42377563739363 | 22.08025245799609 | 2.545374521742775 | 6.4361304863554185 | 4.698962913026245 | 1.595422991479932 |
| user_002 | Jogging | 1.446034903162e12 | -15.82569 | -12.79839 | -0.345482 | -13.836517272727274 | -12.927282727272729 | 0.260676 | 250.4524639761 | 163.7987865921 | 0.119357812324 | 20.35609511621824 | 19.101228338167484 | 1.9891727272727255 | 0.1288927272727296 | 0.606158 | 4.112941603305784 | 2.917411074380166 | 1.1961640578512398 | 3.956808138925613 | 1.6613335143802255e-2 | 0.36742752096399994 | 25.41021847834294 | 18.272074295056942 | 2.3195890725029122 | 5.040854935260778 | 4.274584692699039 | 1.5230197216395172 |
| user_002 | Jogging | 1.446034903332e12 | -3.90807 | 2.68302 | -4.59904 | -3.5248700000000004 | -3.507452363636365 | -2.418262727272727 | 15.2730111249 | 7.1985963204 | 21.151168921599997 | 6.604754073158213 | 6.2858962299151155 | 0.38319999999999954 | 6.190472363636365 | 2.1807772727272727 | 4.531614380165291 | 4.087922247933886 | 1.003929388429752 | 0.14684223999999965 | 38.3219480849456 | 4.755789513243801 | 26.03798165480255 | 17.937086675501124 | 1.3010746092393464 | 5.1027425620741 | 4.23521979069577 | 1.1406465750789534 |
| user_002 | Jogging | 1.446034903364e12 | -3.06503 | 3.90927 | -2.52974 | -2.817686363636364 | 0.32109581818181826 | -3.007001818181818 | 9.3944089009 | 15.282391932899998 | 6.3995844675999995 | 5.574619745005035 | 5.520307117261362 | 0.24734363636363632 | 3.5881741818181814 | 0.47726181818181823 | 2.2561485950413216 | 4.613006 | 1.049850388429752 | 6.117887444958676e-2 | 12.874993959066575 | 0.22777884309421492 | 10.156513458621633 | 22.844478304031163 | 1.672854066725234 | 3.186928530516747 | 4.779589763152394 | 1.2933885984982372 |
| user_002 | Jogging | 1.446034903559e12 | -19.58108 | -10.42253 | -8.81427 | -15.08018545454545 | -16.330821818181818 | -7.13166 | 383.4186939664 | 108.6291316009 | 77.69135563290001 | 23.86920989895141 | 23.72028927423204 | 4.50089454545455 | 5.908291818181818 | 1.6826100000000004 | 5.996020479338842 | 5.813287438016529 | 3.578356950413223 | 20.258051709302517 | 34.90791220879421 | 2.8311764121000014 | 37.15895341889142 | 43.90340861610187 | 14.47403434508205 | 6.095814418015974 | 6.625964730973284 | 3.80447556768105 |
| user_002 | Jogging | 1.446034903656e12 | -3.02671 | -2.10702 | 1.83878 | -12.244483636363638 | -4.859112727272729 | -3.811728818181818 | 9.1609734241 | 4.439533280399999 | 3.3811118884000004 | 4.120875949710206 | 13.973353179277263 | 9.217773636363638 | 2.752092727272729 | 5.650508818181818 | 5.437367190082646 | 4.621873553719009 | 3.1761519338842983 | 84.96735081124054 | 7.574014379507447 | 31.928249904350483 | 40.0247632765042 | 23.596950291099475 | 14.544418632169284 | 6.326512726337015 | 4.857669224134089 | 3.81371454518679 |
| user_002 | Jogging | 1.446034903664e12 | -1.91542 | -1.37893 | 2.98839 | -10.638514545454544 | -4.179798181818182 | -2.759661545454545 | 3.6688337763999996 | 1.9014479449 | 8.9304747921 | 3.8079858867122915 | 12.21590272555592 | 8.723094545454545 | 2.8008681818181818 | 5.748051545454545 | 5.953899504132232 | 4.342230082644629 | 3.610976867768595 | 76.09237844893885 | 7.844862571921487 | 33.04009656920238 | 46.1014217529465 | 21.170256110268816 | 17.46341094419858 | 6.789802777175969 | 4.6011146595437955 | 4.178924615759248 |
| user_002 | Jogging | 1.446034903688e12 | 1.68669 | 0.767005 | 1.8771 | -5.3433436363636355 | -2.4519013636363636 | -0.11207609090909067 | 2.8449231561 | 0.5882966700250001 | 3.52350441 | 2.637560281040985 | 7.330031668983031 | 7.030033636363635 | 3.2189063636363637 | 1.9891760909090908 | 7.687181198347106 | 3.4079735454545457 | 4.361865388429752 | 49.42137292840412 | 10.361358177858678 | 3.9568215206443713 | 62.03217093976252 | 12.505821022904898 | 21.370784418181128 | 7.876050465795818 | 3.5363570270696507 | 4.622854574630391 |
| user_002 | Jogging | 1.446034903802e12 | -8.81307 | -2.91174 | -1.61005 | -0.5846565454545452 | -0.9922438181818181 | -2.317236909090909 | 77.6702028249 | 8.4782298276 | 2.5922610025 | 9.420227898251719 | 6.299631258550062 | 8.228413454545455 | 1.919496181818182 | 0.7071869090909091 | 4.653226421487603 | 2.1057176611570245 | 1.7256828016528922 | 67.70678797894467 | 3.684465592014579 | 0.5001133243895537 | 30.638221487023472 | 5.665971506206171 | 4.285349532874221 | 5.535180348193134 | 2.38033012546709 | 2.0701085799721284 |
| user_002 | Jogging | 1.446034903891e12 | -14.79104 | -15.78737 | 2.56686 | -12.119069090909091 | -13.000438181818181 | 1.3022913636363636 | 218.77486428160003 | 249.24105151689997 | 6.5887702596 | 21.785423706187125 | 18.60925747454722 | 2.671970909090909 | 2.7869318181818183 | 1.2645686363636366 | 4.556316016528926 | 5.576081132231405 | 3.255326991735537 | 7.1394285390281 | 7.766988959194216 | 1.5991338360745873 | 23.245656366794588 | 43.7177504637925 | 17.94761159847474 | 4.821374945676242 | 6.61193999245248 | 4.236462155912022 |
| user_002 | Jogging | 1.446034904036e12 | -4.5212 | -3.86975 | -2.72134 | -8.691145454545454 | -7.99092909090909 | -1.2721353636363635 | 20.441249440000004 | 14.974965062499999 | 7.405691395600001 | 6.543844886463921 | 12.128840394525708 | 4.169945454545454 | 4.121179090909091 | 1.4492046363636366 | 3.656264380165289 | 2.8268364462809914 | 1.3494450743801654 | 17.388445093884293 | 16.984117099346278 | 2.1001940780578603 | 14.260647954712015 | 10.580754409606984 | 2.6915586961146896 | 3.776327310325737 | 3.2528071583798175 | 1.6405970547683821 |
| user_002 | Jogging | 1.446034904052e12 | -2.4519 | -2.4519 | -1.34181 | -7.266326363636364 | -6.377991818181818 | -1.7737833636363638 | 6.011813610000001 | 6.011813610000001 | 1.8004540760999999 | 3.7180749449278188 | 10.010471067635713 | 4.814426363636364 | 3.926091818181818 | 0.43197336363636385 | 3.708519669421487 | 3.345269421487602 | 1.1705110743801652 | 23.17870121087686 | 15.414196964794213 | 0.18660098689131424 | 14.723721494830494 | 13.114459041074072 | 2.413511653743653 | 3.8371501788215814 | 3.62138910379347 | 1.5535480854301398 |
| user_002 | Jogging | 1.446034904068e12 | -3.06503 | 1.03525 | -1.03525 | -6.00524 | -4.249472 | -1.9235813636363637 | 9.3944089009 | 1.0717425625 | 1.0717425625 | 3.3967475658193975 | 7.858612444492149 | 2.9402099999999995 | 5.284722 | 0.8883313636363637 | 3.514700991735537 | 4.132577867768595 | 1.2607692396694217 | 8.644834844099996 | 27.928286617284005 | 0.7891326116200413 | 13.18482379751194 | 17.94021551195684 | 2.50944098331362 | 3.6310912681330305 | 4.235589157597422 | 1.584121517849442 |
| user_002 | Jogging | 1.446034904141e12 | -4.98104 | -1.03405 | 0.459245 | -4.110125454545455 | 2.7248261818181816 | -2.1918236363636363 | 24.8107594816 | 1.0692594024999997 | 0.210905970025 | 5.107927647698722 | 6.009845601241079 | 0.8709145454545455 | 3.7588761818181817 | 2.6510686363636364 | 2.47118347107438 | 4.154430181818182 | 1.6769083719008264 | 0.7584921454842976 | 14.129150150240033 | 7.028164914710951 | 7.068442432986174 | 20.290694168959366 | 3.8194279934045157 | 2.658654252246082 | 4.504519304982427 | 1.9543356910736998 |
| user_002 | Jogging | 1.44603490419e12 | -0.842448 | -2.18366 | 1.30229 | -3.5527377272727274 | -0.22583909090909077 | -0.26884136363636374 | 0.7097186327039999 | 4.768370995600001 | 1.6959592440999998 | 2.6784415006499582 | 5.301884437122247 | 2.7102897272727273 | 1.9578209090909093 | 1.5711313636363637 | 2.3647737520661156 | 3.229358115702479 | 2.469601165289256 | 7.345670405760075 | 3.8330627120735548 | 2.46845376180186 | 6.257878597314694 | 12.208151199119698 | 6.884548005580183 | 2.5015752231973147 | 3.4940164852386855 | 2.623842221929547 |
| user_002 | Jogging | 1.44603490423e12 | -9.2346 | -13.33487 | -3.87095 | -2.5668612727272726 | -4.789440909090909 | -0.23748899999999998 | 85.27783716 | 177.8187579169 | 14.9842539025 | 16.675756324059186 | 5.879288130861806 | 6.667738727272727 | 8.545429090909092 | 3.633461 | 2.5617600909090905 | 4.247855041322314 | 2.637450396694215 | 44.45873973517253 | 73.0243583477554 | 13.202038838521 | 8.912440530990839 | 21.106037928951018 | 7.913980677297903 | 2.985371087652394 | 4.594130813217123 | 2.813179816026324 |
| user_002 | Jogging | 1.446034904247e12 | -11.4955 | -11.95534 | -4.94392 | -4.120576727272727 | -6.674103636363637 | -1.198980909090909 | 132.14652025 | 142.93015451559998 | 24.442344966400004 | 17.306617801638772 | 8.42209099464779 | 7.374923272727273 | 5.281236363636363 | 3.7449390909090914 | 3.597359198347107 | 4.569618677685951 | 2.868639413223141 | 54.389493278614346 | 27.89145752859503 | 14.024568794619011 | 18.159871432887243 | 25.35676770577446 | 9.372191101671767 | 4.26144006562186 | 5.035550387571796 | 3.061403452939806 |
| user_002 | Jogging | 1.446034904433e12 | -4.48288 | -1.80046 | 1.45557 | -9.56206 | -3.9951645454545455 | -3.5992239999999995 | 20.0962130944 | 3.2416562115999996 | 2.1186840249000003 | 5.045448773984332 | 11.195118393459259 | 5.079180000000001 | 2.1947045454545453 | 5.054793999999999 | 5.569430082644629 | 2.9224779338842977 | 3.0064033553719 | 25.79806947240001 | 4.816728041838842 | 25.550942382435995 | 33.99202304405725 | 8.910876950714426 | 11.493034828647646 | 5.830267836391159 | 2.9851092024772603 | 3.390137877527645 |
| user_002 | Jogging | 1.446034904538e12 | 2.72134 | -2.6435 | -3.41111 | 3.4424599090909087 | -0.6090423636363635 | -1.4950904545454546 | 7.405691395600001 | 6.98809225 | 11.635671432099999 | 5.101907004023103 | 4.55478708374157 | 0.7211199090909086 | 2.034457636363636 | 1.9160195454545452 | 4.419503983471075 | 1.9660537438016528 | 2.015458099173554 | 0.5200139232872804 | 4.139017874158314 | 3.6711308985638422 | 22.016640849837806 | 4.48710159313118 | 4.649995244198693 | 4.692189345053949 | 2.1182779782481758 | 2.1563847625594774 |
| user_002 | Jogging | 1.446034904684e12 | -12.22358 | -12.76007 | -0.767005 | -8.001378818181818 | -5.799704 | -0.22703663636363636 | 149.4159080164 | 162.81938640490003 | 0.5882966700250001 | 17.686819699746053 | 10.153678420593193 | 4.222201181818182 | 6.9603660000000005 | 0.5399683636363637 | 6.781428462809917 | 3.266728289256198 | 1.2351171652892563 | 17.826982819746853 | 48.446694853956004 | 0.2915658337281323 | 48.622979422365624 | 18.36825048902798 | 2.086747875931804 | 6.973017956549777 | 4.285819698614021 | 1.44455802096413 |
| user_002 | Jogging | 1.446034904692e12 | -12.6451 | -12.30022 | -0.920286 | -9.081314545454545 | -6.775130363636364 | -0.1573635454545455 | 159.89855400999997 | 151.29541204839998 | 0.8469263217960001 | 17.664679232304106 | 11.538779339376402 | 3.563785454545455 | 5.525089636363636 | 0.7629224545454545 | 6.725056115702478 | 3.691418768595042 | 1.289588834710744 | 12.700566766029754 | 30.526615489852855 | 0.5820506716496611 | 48.18622526434305 | 21.077174385491052 | 2.1372244049430615 | 6.94162987088357 | 4.59098838873407 | 1.4619248971623207 |
| user_002 | Jogging | 1.446034904756e12 | -14.06296 | -10.57581 | -0.690364 | -14.644726363636364 | -12.767033636363635 | -0.25839 | 197.76684396160002 | 111.84775715610002 | 0.476602452496 | 17.609406678539628 | 19.475279502295486 | 0.5817663636363637 | 2.1912236363636346 | 0.43197399999999997 | 3.2382232727272733 | 4.010647933884297 | 0.4766286280991736 | 0.33845210185867775 | 4.8014610245586695 | 0.18660153667599996 | 12.985092626615568 | 20.309954201626507 | 0.2758041644698933 | 3.603483401739984 | 4.506656654508585 | 0.5251706051083718 |
| user_002 | Jogging | 1.446034904846e12 | -5.86241 | 0.881966 | -2.22318 | -8.245237272727273 | -6.050527636363636 | -2.007189090909091 | 34.3678510081 | 0.7778640251560001 | 4.9425293124000005 | 6.331527805013257 | 10.729046543855477 | 2.3828272727272735 | 6.9324936363636365 | 0.21599090909090934 | 4.018248099173554 | 3.8266475206611585 | 0.9485068347107437 | 5.677865811652896 | 48.05946801822232 | 4.665207280991746e-2 | 18.196603209903987 | 16.717464723465596 | 1.2873520720054776 | 4.265747673023334 | 4.08869963722766 | 1.134615385055869 |
| user_002 | Jogging | 1.446034904854e12 | -5.36425 | 3.14286 | -2.52974 | -7.541537272727272 | -4.8382130909090915 | -2.0594445454545456 | 28.7751780625 | 9.877568979600001 | 6.3995844675999995 | 6.712103359581109 | 9.819530956042142 | 2.1772872727272716 | 7.981073090909092 | 0.4702954545454543 | 4.071453140495868 | 4.330194743801655 | 0.856348520661157 | 4.74057986798016 | 63.69752768243321 | 0.22117781456611546 | 18.397149987892792 | 21.966001243425268 | 1.1072440962802064 | 4.289189898791238 | 4.686790078873308 | 1.0522566684417858 |
| user_002 | Jogging | 1.446034904878e12 | -2.10702 | 4.5224 | -4.36911 | -5.378180909090909 | -1.4207367272727274 | -2.4983863636363637 | 4.439533280399999 | 20.45210176 | 19.0891221921 | 6.631798943914087 | 7.87933259704107 | 3.271160909090909 | 5.943136727272727 | 1.8707236363636364 | 3.951109338842975 | 5.189711123966943 | 0.8959354462809919 | 10.70049369316446 | 35.320874159057986 | 3.4996069236495866 | 17.659479898589257 | 31.220205179266724 | 1.239098651056581 | 4.20231839567033 | 5.587504378456157 | 1.1131480813694918 |
| user_002 | Jogging | 1.44603490491e12 | -1.34061 | -1.11069 | 0.497565 | -3.281014181818182 | 1.4567706363636361 | -2.52974 | 1.7972351721000002 | 1.2336322760999998 | 0.24757092922499999 | 1.810645845389153 | 5.179318167006664 | 1.940404181818182 | 2.567460636363636 | 3.0273049999999997 | 2.96301573553719 | 4.889799148760331 | 1.2677373636363638 | 3.7651683888174885 | 6.591854119276767 | 9.164575563024998 | 9.386070086848262 | 30.130454763090782 | 2.3786902039375906 | 3.0636693827579147 | 5.489121492833874 | 1.5423002962904437 |
| user_002 | Jogging | 1.446034904918e12 | -1.30229 | -2.03038 | 1.53221 | -2.9361314545454547 | 1.5821824545454546 | -2.1813736363636362 | 1.6959592440999998 | 4.1224429444 | 2.3476674841 | 2.857633579135016 | 4.84376388010314 | 1.6338414545454547 | 3.6125624545454547 | 3.7135836363636363 | 2.677355123966942 | 4.804924578512398 | 1.5457965702479337 | 2.669437898591207 | 13.05060748799148 | 13.790703424267768 | 7.55499995359014 | 29.43798477202156 | 3.5933962969675406 | 2.748636016934607 | 5.425678277600097 | 1.8956255687681416 |
| user_002 | Jogging | 1.446034905008e12 | -14.40784 | -15.86401 | -3.67935 | -4.430622363636363 | -7.21407090909091 | -0.7252006363636364 | 207.5858534656 | 251.6668132801 | 13.5376164225 | 21.74374124129056 | 8.775807580478014 | 9.977217636363637 | 8.64993909090909 | 2.9541493636363634 | 2.9516145867768597 | 5.136505157024794 | 1.7073115454545453 | 99.54487176336559 | 74.82144627643717 | 8.72699846267313 | 19.804817630125125 | 33.486343327562736 | 4.005089556027638 | 4.450260400260318 | 5.786738574323428 | 2.0012719845207543 |
| user_002 | Jogging | 1.446034905072e12 | -17.51178 | -12.83671 | -9.88724 | -16.44578272727273 | -14.568086363636365 | -6.950508181818182 | 306.6624387684001 | 164.7811236241 | 97.75751481760001 | 23.857935309035025 | 23.287859721835208 | 1.065997272727273 | 1.7313763636363646 | 2.9367318181818183 | 7.326463776859504 | 4.696928760330579 | 3.9678935537190085 | 1.1363501854619842 | 2.997664112558681 | 8.624393771921488 | 63.111678662534125 | 30.135037766228844 | 17.07495917870299 | 7.94428591268807 | 5.489538939312558 | 4.132185762850333 |
| user_002 | Jogging | 1.44603490508e12 | -17.24354 | -10.95901 | -8.69931 | -17.327149999999996 | -14.338164545454545 | -7.570600909090908 | 297.3396717316 | 120.09990018009998 | 75.67799447610001 | 22.20625061526146 | 23.891116942422705 | 8.360999999999663e-2 | 3.3791545454545453 | 1.1287090909090924 | 6.854269198347108 | 4.205415454545455 | 3.891886876033058 | 6.990632099999437e-3 | 11.418685442066115 | 1.27398421190083 | 60.58007336537378 | 24.155800296036443 | 16.839833156973718 | 7.7833202019044405 | 4.914855063583914 | 4.103636577107397 |
| user_002 | Jogging | 1.446034905177e12 | -5.47921 | 0.345482 | 2.2603 | -12.596334545454546 | -2.800267454545455 | -3.7943080000000005 | 30.021742224100002 | 0.119357812324 | 5.1089560899999995 | 5.937175770214656 | 13.939753347675527 | 7.117124545454546 | 3.145749454545455 | 6.054608 | 3.6648138842975198 | 5.536176719008265 | 3.1514500495867774 | 50.653461795511575 | 9.895739630773027 | 36.658278033664 | 18.033930212002403 | 31.976428244376894 | 13.719504742224348 | 4.246637518319924 | 5.654770397140532 | 3.703984981371327 |
| user_002 | Jogging | 1.446034905234e12 | 5.97857 | 1.83997 | -2.6447 | -1.852711727272727 | 1.1327883636363636 | 0.6821992727272729 | 35.7432992449 | 3.3854896009 | 6.994438089999999 | 6.791408317558296 | 6.010528689599741 | 7.831281727272727 | 0.7071816363636365 | 3.326899272727273 | 7.4420579008264465 | 2.783447090909091 | 4.073986148760331 | 61.32897349191571 | 0.5001058668099506 | 11.068258770873257 | 57.684432289683144 | 10.435126585578507 | 19.986831797446357 | 7.595026812966703 | 3.230344654302155 | 4.470663462781151 |
| user_002 | Jogging | 1.446034905339e12 | -5.47921 | -3.52487 | -4.59904 | -1.4451221818181816 | -3.3228180909090907 | -1.2372999999999998 | 30.021742224100002 | 12.424708516899999 | 21.151168921599997 | 7.97481157536653 | 6.509562618986421 | 4.034087818181819 | 0.2020519090909092 | 3.3617399999999997 | 5.088052165289255 | 2.322020347107438 | 1.70287826446281 | 16.273864524802946 | 4.0824973967281034e-2 | 11.301295827599999 | 31.004942615609924 | 7.3387421684781975 | 4.120292266177289 | 5.568208205123971 | 2.70901128983956 | 2.0298503063470688 |
| user_002 | Jogging | 1.446034905355e12 | -5.63249 | -2.95007 | -4.75232 | -3.6328649090909084 | -3.6398318181818183 | -2.097765272727273 | 31.724943600099998 | 8.702913004900001 | 22.5845453824 | 7.938035146520832 | 6.782794744382937 | 1.9996250909090914 | 0.6897618181818181 | 2.654554727272727 | 4.8840994710743795 | 2.0626460743801656 | 2.206426214876033 | 3.998500504193192 | 0.47577136582148755 | 7.0466608000859825 | 29.68185233968346 | 6.288701452326577 | 6.453050962520794 | 5.448105389920744 | 2.5077283450020214 | 2.540285606486167 |
| user_002 | Jogging | 1.446034905469e12 | -15.55745 | -16.05561 | 1.76214 | -14.630792727272727 | -14.028118181818183 | 3.1346995454545454 | 242.0342505025 | 257.7826124721 | 3.1051373796000004 | 22.425922508432066 | 21.330167876689657 | 0.9266572727272724 | 2.0274918181818187 | 1.3725595454545454 | 4.255771157024793 | 3.5739233057851227 | 5.2546326280991735 | 0.8586937010983465 | 4.110723072794217 | 1.8839197058183883 | 27.394483158568065 | 25.26332975107513 | 37.281628055188655 | 5.233973935602666 | 5.026263995362274 | 6.105868329336022 |
| user_002 | Jogging | 1.446034905615e12 | -3.67815 | 2.60638 | -1.64837 | -6.73681 | -4.211151818181819 | -2.275431818181818 | 13.5287874225 | 6.793216704400001 | 2.7171236568999997 | 4.7999091432859435 | 8.70367169629476 | 3.05866 | 6.81753181818182 | 0.6270618181818182 | 4.062901983471075 | 4.511978595041322 | 1.0020288842975207 | 9.3554009956 | 46.47874009192151 | 0.3932065238214876 | 17.284075030573106 | 22.3073486315281 | 1.8275789039897425 | 4.157412059271141 | 4.72306559678437 | 1.3518797668393971 |
| user_002 | Jogging | 1.446034905647e12 | -1.30229 | 3.25783 | -5.36544 | -3.6154460909090913 | -0.4418272727272725 | -2.857203636363636 | 1.6959592440999998 | 10.613456308899998 | 28.787946393600006 | 6.410722420024127 | 6.502811399641062 | 2.313156090909091 | 3.699657272727272 | 2.5082363636363643 | 3.985945512396695 | 4.648158429752066 | 0.9136700826446282 | 5.3506911009098275 | 13.687463935643796 | 6.291249655867772 | 16.803033608622233 | 24.367466543648614 | 1.6582827814492298 | 4.099150352039095 | 4.936341412792334 | 1.2877432901977124 |
| user_002 | Jogging | 1.446034905727e12 | 1.07357 | -6.70546 | 1.53221 | -1.8213575454545456 | -2.3613268181818183 | 0.4035049090909093 | 1.1525525448999998 | 44.96319381160001 | 2.3476674841 | 6.96156690987022 | 5.193493556753845 | 2.8949275454545456 | 4.344133181818182 | 1.1287050909090908 | 1.1024227851239672 | 3.1625355371900823 | 3.0599230743801655 | 8.38060549343148 | 18.871493101373762 | 1.2739751822440988 | 1.9395304452291355 | 11.662849426100301 | 12.069842032723182 | 1.3926702571783227 | 3.4150914228026608 | 3.474167818733456 |
| user_002 | Jogging | 1.446034905857e12 | -17.20522 | -5.82409 | -6.20849 | -18.59172 | -10.513101818181818 | -8.033929090909092 | 296.01959524840004 | 33.9200243281 | 38.545348080100005 | 19.195962274827487 | 22.924337680223914 | 1.386499999999998 | 4.689011818181818 | 1.825439090909092 | 6.054610214876033 | 2.2665984297520656 | 3.3813718842975207 | 1.9223822499999947 | 21.98683183104876 | 3.3322278746190124 | 54.52274721195797 | 7.463359831681368 | 14.768475546288306 | 7.383952004987435 | 2.7319150484012797 | 3.842977432445877 |
| user_002 | Jogging | 1.446034905874e12 | -14.21624 | -3.90807 | -6.55337 | -18.072653636363636 | -8.962870909090908 | -8.103602727272728 | 202.10147973760002 | 15.2730111249 | 42.9466583569 | 16.134470837910985 | 21.862878092852522 | 3.8564136363636354 | 5.054800909090908 | 1.5502327272727277 | 4.436605834710743 | 2.4822690909090905 | 2.7169417520661163 | 14.871926134731398 | 25.551012230546267 | 2.403221508707439 | 30.28166977085812 | 9.45894439590338 | 10.4348205853482 | 5.50287831692271 | 3.0755396918107527 | 3.230297290552094 |
| user_002 | Jogging | 1.446034906043e12 | 4.79064 | -6.43721 | -3.52607 | 5.079782727272728 | -0.9121207272727273 | -0.6102407272727273 | 22.9502316096 | 41.43767258410001 | 12.4331696449 | 8.764763193526681 | 6.032297508238341 | 0.2891427272727283 | 5.525089272727273 | 2.9158292727272723 | 4.460991074380165 | 2.1611383471074377 | 1.8317732727272726 | 8.360351673471132e-2 | 30.52661147160599 | 8.502060347693254 | 26.557241211071098 | 7.379857479956052 | 4.511605649858295 | 5.1533718293046835 | 2.7165893101379996 | 2.1240540600131377 |
| user_002 | Jogging | 1.4460349061e12 | -8.69811 | -6.62882 | -5.67201 | -2.187142090909091 | -5.298055454545454 | -2.2510471818181816 | 75.65711757209999 | 43.9412545924 | 32.171697440100004 | 12.31949956794512 | 9.109836937026863 | 6.510967909090908 | 1.3307645454545458 | 3.4209628181818186 | 5.894360165289256 | 3.051057355371901 | 1.7427830826446282 | 42.39270311321163 | 1.770934275438844 | 11.70298660338249 | 49.28009459437685 | 11.136380565774072 | 4.236912042945018 | 7.019978247429036 | 3.337121598889389 | 2.0583760693675535 |
| user_002 | Jogging | 1.446034906132e12 | -15.74905 | -17.81835 | 0.344284 | -8.572700272727273 | -9.20672818181818 | -1.6274719090909096 | 248.0325759025 | 317.49359672249994 | 0.11853147265599999 | 23.78328623419514 | 13.337573546264622 | 7.1763497272727275 | 8.611621818181819 | 1.9717559090909096 | 7.523449586776858 | 4.22220267768595 | 1.909365611570248 | 51.49999540812735 | 74.16003033938513 | 3.8878213650349194 | 61.7491867891929 | 25.090541422510398 | 5.45840222276125 | 7.858065079215932 | 5.009045959313051 | 2.336322371326622 |
| user_002 | Jogging | 1.446034906319e12 | -3.56319 | -4.98104 | -1.22685 | -7.67043181818182 | -8.49606 | -1.3487758181818184 | 12.696322976100001 | 24.8107594816 | 1.5051609225 | 6.2459781764108016 | 11.641939368073643 | 4.107241818181819 | 3.51502 | 0.12192581818181836 | 5.061765454545454 | 2.8714898347107436 | 0.936471603305785 | 16.869435353021498 | 12.355365600399999 | 1.4865905139305828e-2 | 26.613912440978808 | 10.115667026524044 | 1.3641360147597592 | 5.158867360281596 | 3.1805136419333344 | 1.1679623344781969 |
| user_002 | Jogging | 1.446034906366e12 | -3.60151 | 4.33079 | -5.67201 | -4.806859090909091 | -2.3578414545454547 | -2.4461315454545454 | 12.970874280100002 | 18.7557420241 | 32.171697440100004 | 7.993642082574126 | 7.147612929840088 | 1.205349090909091 | 6.688631454545455 | 3.225878454545455 | 2.907910165289256 | 5.069999818181818 | 0.8607821404958678 | 1.452866430955372 | 44.737790734734844 | 10.406291803500572 | 11.57677391751818 | 27.53574990156487 | 1.5309432143551114 | 3.402465858391261 | 5.247451753143126 | 1.237312900747063 |
| user_002 | Jogging | 1.446034906408e12 | -3.37159 | -2.6435 | -0.537083 | -3.883689090909091 | 0.8924172727272727 | -3.6201266363636364 | 11.3676191281 | 6.98809225 | 0.28845814888899995 | 4.3178894760043365 | 6.298627808131697 | 0.512099090909091 | 3.5359172727272727 | 3.0830436363636364 | 1.0894361157024794 | 4.591786280991736 | 1.653474876033058 | 0.26224547890991745 | 12.502710959571074 | 9.505158063722314 | 1.5332669035144262 | 25.43087279685827 | 3.9924662865406377 | 1.2382515509840584 | 5.042903211133273 | 1.9981156839734375 |
| user_002 | Jogging | 1.446034906432e12 | -0.919089 | -6.01569 | 1.22565 | -3.0406417272727277 | -1.6819454545454447e-2 | -2.571543909090909 | 0.8447245899210001 | 36.188526176100005 | 1.5022179224999999 | 6.207694313392132 | 6.2486913175856165 | 2.1215527272727277 | 5.998870545454546 | 3.797193909090909 | 1.1369413223140497 | 4.950603520661157 | 2.6136990661157027 | 4.500985974598349 | 35.98644782112212 | 14.418681583237097 | 1.6017300063233662 | 28.800501000828568 | 8.60560462482137 | 1.2655947243582228 | 5.366609823792723 | 2.9335310846864004 |
| user_002 | Jogging | 1.44603490644e12 | -0.535886 | -4.44456 | 1.14901 | -2.6713731818181823 | -0.6369121818181819 | -2.2440793636363634 | 0.287173804996 | 19.7541135936 | 1.3202239801000002 | 4.621851509806 | 6.148168785920205 | 2.135487181818182 | 3.807647818181818 | 3.3930893636363635 | 1.2534859421487603 | 4.643724429752066 | 2.887324933884298 | 4.560305503709762 | 14.49818190730476 | 11.513055429622222 | 1.950079957320572 | 25.427603458128235 | 9.638896397270868 | 1.3964526333966978 | 5.0425790482776005 | 3.1046572109124813 |
| user_002 | Jogging | 1.446034906586e12 | -18.73803 | -8.77475 | -10.577 | -17.306248181818184 | -11.910050909090911 | -9.437845454545455 | 351.11376828089993 | 76.99623756249999 | 111.872929 | 23.237532890636217 | 23.287164041305836 | 1.4317818181818147 | 3.135300909090912 | 1.139154545454545 | 7.446174330578511 | 3.08209305785124 | 5.358825710743801 | 2.049999174876023 | 9.8301117905463 | 1.2976730784297512 | 71.52977110671443 | 15.74948846692201 | 31.739869182092267 | 8.457527481877575 | 3.968562518963511 | 5.633814798348652 |
| user_002 | Jogging | 1.44603490665e12 | -11.84038 | -1.8771 | -5.05888 | -16.773248181818182 | -5.029813636363637 | -8.096633636363636 | 140.19459854439998 | 3.52350441 | 25.592266854400002 | 13.011931824629269 | 19.655628983671996 | 4.932868181818183 | 3.152713636363637 | 3.037753636363636 | 1.9451499999999997 | 4.46225694214876 | 2.7549444214876035 | 24.333188499194225 | 9.939603272913228 | 9.227947155240493 | 5.12739384022141 | 23.162048677438687 | 8.607126122767712 | 2.2643749336674373 | 4.812696611821556 | 2.933790401983024 |
| user_002 | Jogging | 1.446034906675e12 | -7.28026 | -0.612526 | -1.95493 | -14.35906818181818 | -2.295136727272727 | -5.773029999999999 | 53.0021856676 | 0.375188100676 | 3.8217513049000003 | 7.563010318198436 | 15.684182358959674 | 7.0788081818181805 | 1.6826107272727273 | 3.8180999999999994 | 3.3383008264462815 | 4.440089074380165 | 2.8638885950413226 | 50.109525274976015 | 2.8311788595332565 | 14.577887609999996 | 16.27907204168686 | 22.948845464150352 | 8.696309456470926 | 4.034733205763035 | 4.790495325553544 | 2.9489505686720023 |
| user_002 | Jogging | 1.446034906691e12 | -3.56319 | -0.267643 | -0.575403 | -11.673162727272727 | -1.3859 | -4.44575390909091 | 12.696322976100001 | 7.163277544900001e-2 | 0.331088612409 | 3.6192601956695514 | 12.589850897609228 | 8.109972727272726 | 1.1182569999999998 | 3.87035090909091 | 4.516729669421488 | 3.7012356942148754 | 3.247091570247935 | 65.77165763710742 | 1.2504987180489997 | 14.979616159500832 | 26.88297084850962 | 17.91654209487913 | 10.707331798979867 | 5.184879058233626 | 4.232793651346488 | 3.2722059530200522 |
| user_002 | Jogging | 1.446034906796e12 | 3.48775 | -1.8771 | -0.15388 | 4.167061818181818 | -0.2641598181818182 | -1.0526639999999998 | 12.1644000625 | 3.52350441 | 2.3679054399999996e-2 | 3.9637839909485484 | 4.577401749353625 | 0.6793118181818176 | 1.6129401818181819 | 0.8987839999999998 | 4.622822 | 1.3089080247933884 | 0.7635545371900826 | 0.46146454632148687 | 2.6015760301236694 | 0.8078126786559997 | 29.624850336280137 | 2.1701642934302128 | 0.9407629565015231 | 5.4428715156872975 | 1.473147750033992 | 0.9699293564489752 |
| user_002 | Jogging | 1.446034906901e12 | -13.90968 | -14.82936 | 0.574206 | -9.422715454545456 | -7.56243818181818 | -1.536894909090909 | 193.4791977024 | 219.90991800959998 | 0.329712530436 | 20.34007935683723 | 12.62745717317743 | 4.486964545454544 | 7.266921818181819 | 2.1111009090909088 | 5.990321280991736 | 3.6831837438016533 | 1.5651153305785124 | 20.132850832166103 | 52.808152711566954 | 4.456747048364462 | 39.355071607140815 | 25.29494812891069 | 3.5116251320448835 | 6.27336206568223 | 5.029408327915988 | 1.8739330649852153 |
| user_002 | Jogging | 1.446034906942e12 | -13.25823 | -11.95534 | 0.957409 | -12.234032727272727 | -11.370082727272724 | 6.907418181818191e-2 | 175.78066273289997 | 142.93015451559998 | 0.9166319932809999 | 17.878127677186473 | 17.14557775538888 | 1.0241972727272728 | 0.5852572727272758 | 0.888334818181818 | 3.535603636363635 | 4.105973884297521 | 2.7207424297520664 | 1.0489800534619835 | 0.3425260752801689 | 0.7891387491941237 | 14.143379773647181 | 27.92088436341037 | 9.16121299243646 | 3.7607685083832507 | 5.284021608908348 | 3.0267495754416913 |
| user_002 | Jogging | 1.446034906982e12 | -18.20155 | -13.83303 | 0.114362 | -14.93735727272727 | -13.091012727272728 | 0.9992131818181815 | 331.2964224025 | 191.35271898090002 | 1.3078667044000002e-2 | 22.861807016297817 | 20.021431218737533 | 3.2641927272727305 | 0.7420172727272725 | 0.8848511818181816 | 2.679888842975208 | 2.0886154545454545 | 2.392960826446281 | 10.654954160780186 | 0.5505896330256195 | 0.7829616139650326 | 8.170243855914727 | 9.0530824903009 | 7.778509837779979 | 2.8583638424656033 | 3.008834074903583 | 2.7889979988841835 |
| user_002 | Jogging | 1.446034907039e12 | -10.49917 | -9.08132 | -1.49509 | -15.240435454545455 | -12.43260181818182 | -0.7321686363636364 | 110.23257068889998 | 82.47037294239999 | 2.2352941081 | 13.962028424960321 | 19.741622779485223 | 4.741265454545456 | 3.3512818181818194 | 0.7629213636363636 | 2.6935066115702493 | 1.4317850413223132 | 1.1613272644628099 | 22.479598110466128 | 11.23108982487604 | 0.5820490070927686 | 8.706909684042682 | 2.7814735594394424 | 2.090464572249903 | 2.9507473094188668 | 1.6677750326226384 | 1.4458438962245899 |
| user_002 | Jogging | 1.44603490712e12 | -3.06503 | 0.11556 | -2.33814 | -5.9286 | -3.9533609090909083 | -2.0037054545454542 | 9.3944089009 | 1.3354113599999998e-2 | 5.466898659600001 | 3.8567682940643455 | 7.647626963046448 | 2.86357 | 4.068920909090909 | 0.3344345454545459 | 4.9176669421487595 | 4.3891014049586765 | 0.6926151570247933 | 8.2000331449 | 16.556117364437185 | 0.11184646519338871 | 24.717201992823444 | 19.88589517058234 | 0.711008412292816 | 4.971639769012176 | 4.459360399270543 | 0.8432131476043385 |
| user_002 | Jogging | 1.446034907209e12 | -2.60518 | -2.91174 | -0.268841 | -3.305399090909091 | 1.7563679090909092 | -2.2754321818181817 | 6.7869628323999995 | 8.4782298276 | 7.2275483281e-2 | 3.9163079734976156 | 5.2126819531418045 | 0.7002190909090911 | 4.668107909090909 | 2.0065911818181816 | 0.514316033057851 | 3.7867450082644627 | 1.3510296363636363 | 0.49030677527355393 | 21.7912314509171 | 4.026408170950487 | 0.3872811612404208 | 17.10425060984232 | 2.3858715021441905 | 0.6223191795537245 | 4.135728546440436 | 1.5446266546140497 |
| user_002 | Jogging | 1.446034907257e12 | -4.9044 | -8.73643 | -1.03525 | -3.1416672727272728 | -3.047608181818182 | -0.3977359999999999 | 24.05313936 | 76.3252091449 | 1.0717425625 | 10.072243596508178 | 5.088328791502168 | 1.7627327272727271 | 5.688821818181818 | 0.6375140000000001 | 0.4753612396694214 | 3.6527827355371896 | 1.6974960330578512 | 3.1072266677983467 | 32.36269367902149 | 0.40642410019600017 | 0.44791110553456037 | 15.898295291524887 | 4.0854805384043535 | 0.6692616121776 | 3.9872666441467 | 2.0212571678053126 |
| user_002 | Jogging | 1.44603490729e12 | -14.5228 | -14.98264 | -4.714 | -6.297867272727273 | -8.168595454545455 | -1.5194766363636367 | 210.91171984000002 | 224.4795013696 | 22.221796000000005 | 21.39189138925308 | 10.659201066417712 | 8.224932727272726 | 6.814044545454545 | 3.194523363636364 | 2.833486611570248 | 5.709093859504131 | 2.4113304049586772 | 67.64951836816196 | 46.43120306743883 | 10.204979520818588 | 18.223977300926823 | 37.259208245871726 | 7.400173065667297 | 4.268955059604964 | 6.104032130147393 | 2.720325911663398 |
| user_002 | Jogging | 1.446034907403e12 | -16.32385 | -2.98839 | -6.70665 | -18.60913818181818 | -8.074537272727271 | -9.162636363636363 | 266.4680788225 | 8.9304747921 | 44.9791542225 | 17.899097961548232 | 22.465247190479637 | 2.2852881818181814 | 5.086147272727271 | 2.455986363636363 | 2.8151174380165287 | 3.4934809917355367 | 3.2850951074380172 | 5.222542073957849 | 25.868894079871062 | 6.0318690183677655 | 13.08808219941119 | 15.12350955082885 | 15.124677542114034 | 3.6177454580734656 | 3.888895672402237 | 3.8890458395490834 |
| user_002 | Jogging | 1.446034907476e12 | -1.72382 | -1.8771 | 7.6042e-2 | -9.690955454545456 | -2.1314045454545454 | -3.644511272727273 | 2.9715553923999996 | 3.52350441 | 5.782385764e-3 | 2.5496749181344667 | 10.660497360124252 | 7.967135454545456 | 0.2543045454545454 | 3.720553272727273 | 5.511791239669422 | 2.9132938842975205 | 2.9712496363636363 | 63.47524735107523 | 6.467080183884294e-2 | 13.842516655201623 | 34.147024354188275 | 11.661232288066863 | 9.041529347618932 | 5.843545529401502 | 3.4148546510893936 | 3.0069135916449166 |
| user_002 | Jogging | 1.446034907508e12 | 3.90927 | -1.18733 | 0.689167 | -3.514419181818181 | -1.7725872727272725 | -0.8575789090909095 | 15.282391932899998 | 1.4097525289 | 0.47495115388899994 | 4.143319395809234 | 5.937236157961707 | 7.423689181818181 | 0.5852572727272725 | 1.5467459090909095 | 7.149745727272728 | 1.24398520661157 | 3.22270714876033 | 55.1111610682443 | 0.342526075280165 | 2.392422907289464 | 51.78529644795792 | 2.918500147336363 | 10.751469564364557 | 7.196200139515153 | 1.708361831503023 | 3.278943360957087 |
| user_002 | Jogging | 1.44603490771e12 | -17.43514 | -8.54483 | 3.48655 | -12.686907272727273 | -8.206916181818181 | 0.26067618181818175 | 303.9841068196 | 73.01411972889998 | 12.1560309025 | 19.726993117325307 | 16.107928203941583 | 4.748232727272727 | 0.33791381818181776 | 3.2258738181818183 | 5.759764016528926 | 4.008115272727272 | 3.1717190578512398 | 22.5457140323438 | 0.11418574851821459 | 10.406261890830942 | 35.661160756290485 | 30.533416247583013 | 12.024295610812407 | 5.971696639673727 | 5.525705045293588 | 3.4676066113116706 |
| user_002 | Jogging | 1.446034907759e12 | -15.86401 | -13.71807 | 0.919089 | -16.041677272727274 | -11.666194545454545 | 0.6682655454545454 | 251.6668132801 | 188.18544452490002 | 0.8447245899210001 | 20.992784055358666 | 20.2523659330661 | 0.17766727272727323 | 2.0518754545454563 | 0.2508234545454546 | 3.3924554545454555 | 2.6558187107438016 | 2.6403015702479338 | 3.156565979834729e-2 | 4.210192880966122 | 6.291240535011575e-2 | 22.638652582823227 | 12.02031983930934 | 9.005259334218808 | 4.758009308820573 | 3.4670332907702717 | 3.000876427682221 |
| user_002 | Jogging | 1.446034907783e12 | -15.05928 | -13.10495 | -0.767005 | -15.623637272727272 | -12.209646363636363 | -3.1951000000000035e-2 | 226.78191411839998 | 171.73971450250002 | 0.5882966700250001 | 19.977735739841116 | 20.14726044376983 | 0.564357272727273 | 0.8953036363636375 | 0.735054 | 1.4406541322314053 | 1.6569572396694214 | 1.9061984958677685 | 0.3184991312801656 | 0.8015686012859525 | 0.540304382916 | 6.287423676600526 | 3.4110413267804827 | 6.197944188322239 | 2.5074735644868773 | 1.8469004647734764 | 2.489567068452312 |
| user_002 | Jogging | 1.446034907864e12 | -6.20729 | -2.56686 | -1.38013 | -12.718260000000003 | -7.496248181818183 | -2.3799413636363638 | 38.530449144100004 | 6.5887702596 | 1.9047588169000003 | 6.857403168882518 | 15.077684317245383 | 6.510970000000002 | 4.929388181818183 | 0.9998113636363637 | 2.894924958677685 | 3.608126859504132 | 1.1971138512396695 | 42.39273034090003 | 24.298867847048772 | 0.9996227628564051 | 11.904642383664612 | 15.582626079113071 | 2.082914951146491 | 3.4503104764157984 | 3.947483512202815 | 1.443230733855987 |
| user_002 | Jogging | 1.446034907913e12 | -3.63983 | 4.56072 | -1.34181 | -6.733325454545454 | -1.8318086363636361 | -2.6168318181818178 | 13.248362428899998 | 20.8001669184 | 1.8004540760999999 | 5.987402059608157 | 8.085788948485714 | 3.0934954545454545 | 6.392528636363636 | 1.2750218181818178 | 4.686161570247934 | 4.656393636363637 | 1.0932376942148758 | 9.569714127293388 | 40.86442236672913 | 1.6256806368396686 | 23.52487566892179 | 22.36712104831754 | 1.8064930810530717 | 4.850244908138329 | 4.729389077705231 | 1.3440584366213664 |
| user_002 | Jogging | 1.446034907945e12 | -3.06503 | 1.49509 | -3.25783 | -4.030002727272727 | 1.2512349999999997 | -2.5645781818181814 | 9.3944089009 | 2.2352941081 | 10.613456308899998 | 4.7162653994341746 | 5.7354608954132 | 0.9649727272727269 | 0.24385500000000038 | 0.6932518181818184 | 3.8944193388429755 | 4.135427107438016 | 0.7736911983471072 | 0.9311723643801645 | 5.946526102500018e-2 | 0.48059808341239696 | 18.571364015563038 | 19.700650420240237 | 0.7393206976024979 | 4.309450546828799 | 4.438541474430563 | 0.8598375995515071 |
| user_002 | Jogging | 1.446034908139e12 | -16.20889 | -13.83303 | -8.43107 | -5.102970909090908 | -0.7588395454545455 | -2.811918363636364 | 262.7281150321 | 191.35271898090002 | 71.08294134490001 | 22.91645206741 | 7.7895724282934165 | 11.105919090909092 | 13.074190454545455 | 5.619151636363636 | 3.8196790082644627 | 5.613133347107438 | 1.4023345041322315 | 123.34143885381903 | 170.9344560417275 | 31.57486511244813 | 22.279454978312547 | 45.07532239502894 | 3.9238172475664856 | 4.720111754854174 | 6.713815785008473 | 1.9808627533391823 |
| user_002 | Jogging | 1.446034908269e12 | -3.67815 | -1.76214 | 1.49389 | -5.963435454545455 | -1.3824163636363638 | -2.7317938181818175 | 13.5287874225 | 3.1051373796000004 | 2.2317073320999996 | 4.343458545237885 | 8.741728257991952 | 2.285285454545455 | 0.37972363636363626 | 4.2256838181818175 | 3.8149286776859497 | 5.324939834710744 | 1.9438858264462808 | 5.222529608757027 | 0.14419004001322305 | 17.856403731243663 | 23.576699133056877 | 43.9105411464654 | 6.288745057720135 | 4.855584324574837 | 6.626502934917134 | 2.5077370391889446 |
| user_002 | Jogging | 1.446034909046e12 | -5.05768 | -1.07237 | -0.652044 | -9.196275454545455 | -6.566109090909092 | -2.5088374545454544 | 25.580126982400003 | 1.1499774169 | 0.42516137793599995 | 5.211071461536101 | 12.418203811353674 | 4.138595454545455 | 5.4937390909090915 | 1.8567934545454543 | 4.934770495867768 | 5.036428710743802 | 2.7267596363636364 | 17.127972336384303 | 30.18116919898265 | 3.447681932842842 | 33.161018213630584 | 33.540879014491985 | 12.511976010260918 | 5.758560428929315 | 5.791448783723464 | 3.5372271640737067 |
| user_002 | Jogging | 1.446034910211e12 | -17.05194 | -11.6871 | -1.07357 | -8.475157272727273 | -6.458115909090908 | -2.310269909090909 | 290.76865776359995 | 136.58830640999997 | 1.1525525448999998 | 20.700471412953377 | 11.878641916477324 | 8.576782727272725 | 5.228984090909091 | 1.2366999090909092 | 6.322850239669421 | 4.93666985123967 | 2.176657570247934 | 73.56120195084377 | 27.342274622980376 | 1.529426665145463 | 49.513870967071085 | 29.244915742785835 | 8.943415354326099 | 7.036609337391915 | 5.407856853022816 | 2.9905543556882725 |
| user_002 | Jogging | 1.446034910923e12 | -15.0976 | -14.3312 | 1.49389 | -7.0747254545454545 | -5.541911818181819 | -2.4983859090909095 | 227.93752576 | 205.38329344000002 | 2.2317073320999996 | 20.869895220918096 | 10.728633853208215 | 8.022874545454545 | 8.789288181818183 | 3.9922759090909095 | 5.452567685950413 | 4.862880545454545 | 2.2213097768595045 | 64.36651597210248 | 77.25158674304878 | 15.938266934307647 | 42.44141270557199 | 30.712884458420195 | 8.367651130529412 | 6.514707415193103 | 5.541920647069948 | 2.892689255784211 |
| user_002 | Jogging | 1.446034911246e12 | -4.75112 | -6.39889 | 0.880768 | -6.914476363636364 | -5.099486363636363 | -1.561280909090909 | 22.573141254400003 | 40.945793232099994 | 0.775752269824 | 8.01839676969929 | 10.070073140367551 | 2.163356363636364 | 1.2994036363636363 | 2.442048909090909 | 5.080449173553718 | 4.724800603305787 | 1.8662933884297523 | 4.680110756085952 | 1.6884498101950411 | 5.963602874392099 | 38.99996173856371 | 29.48726106588099 | 4.7361626274450375 | 6.24499493503107 | 5.430217405029102 | 2.1762726454755246 |
| user_002 | Jogging | 1.446034912153e12 | -17.93331 | -10.53749 | -8.23947 | -8.192980727272726 | -5.761382454545454 | -2.0733795454545456 | 321.6036075561 | 111.0386955001 | 67.88886588090001 | 22.372553920755227 | 11.34494337446847 | 9.740329272727273 | 4.776107545454546 | 6.166090454545455 | 6.011221520661156 | 4.700413702479339 | 1.9571871735537187 | 94.8740143411478 | 22.811203285747848 | 38.02067149363658 | 47.0317002322874 | 26.73019534190938 | 7.5335368870795465 | 6.857966187747458 | 5.170125273328431 | 2.744728927795885 |
| user_002 | Jogging | 1.44603491306e12 | -0.957409 | 1.91661 | 1.22565 | -9.095248999999999 | -6.325737 | -2.470517545454545 | 0.9166319932809999 | 3.6733938920999996 | 1.5022179224999999 | 2.468247112402038 | 12.124781946634904 | 8.137839999999999 | 8.242347 | 3.696167545454545 | 4.828992140495868 | 4.8625619173553725 | 2.2665985950413226 | 66.22443986559998 | 67.93628406840901 | 13.661654524071476 | 33.47302909714301 | 31.912813317328006 | 7.9159821938666335 | 5.785588051109672 | 5.649142706404929 | 2.8135355327179776 |
| user_002 | Jogging | 1.446034913319e12 | -17.85667 | -13.41151 | -1.80165 | -8.078019 | -5.792736090909091 | -2.1569866363636367 | 318.86066348890006 | 179.86860048009999 | 3.2459427224999997 | 22.404803205819505 | 11.181984622796081 | 9.778651000000002 | 7.618773909090909 | 0.35533663636363677 | 5.500073561983472 | 5.078866123966942 | 2.0981160330578517 | 95.62201537980104 | 58.04571587784437 | 0.12626412514222343 | 41.78944792661123 | 32.60139135892502 | 6.684421911083967 | 6.464475843145462 | 5.7097628110916325 | 2.5854248995250213 |
| user_002 | Jogging | 1.446034914095e12 | -17.39682 | -10.49917 | -3.56439 | -8.861844909090909 | -5.841506090909092 | -1.6866936363636365 | 302.64934611240005 | 110.23257068889998 | 12.7048760721 | 20.629755036679423 | 11.92481561399505 | 8.534975090909093 | 4.657663909090908 | 1.8776963636363635 | 5.914944851239668 | 4.972139173553718 | 2.1484710495867767 | 72.84579980243868 | 21.693833090047995 | 3.5257436340132227 | 46.87801609592091 | 27.69571628107252 | 7.233094173347625 | 6.846752229774414 | 5.262671971638791 | 2.6894412381287727 |
| user_002 | Jogging | 1.446034914937e12 | -14.06296 | -9.31124 | 0.344284 | -10.049774545454547 | -6.2351611818181825 | -0.21658654545454545 | 197.76684396160002 | 86.6991903376 | 0.11853147265599999 | 16.86963442911126 | 12.252088776393206 | 4.013185454545454 | 3.0760788181818173 | 0.5608705454545454 | 5.10451879338843 | 4.026167958677686 | 2.1858417190082644 | 16.1056574925752 | 9.462260895666846 | 0.31457576875847926 | 34.7920575256337 | 20.439095374185214 | 5.985408657440974 | 5.898479255336388 | 4.520961775351038 | 2.446509484437159 |
| user_002 | Jogging | 1.446034915132e12 | -3.79311 | -0.650847 | 0.267643 | -8.910616363636363 | -4.698865454545454 | -0.1364618181818182 | 14.387683472099999 | 0.42360181740899994 | 7.163277544900001e-2 | 3.8578385224057783 | 10.584969232379587 | 5.1175063636363625 | 4.0480184545454545 | 0.4041048181818182 | 4.847044462809918 | 4.361232578512396 | 1.7323318264462806 | 26.188871381858664 | 16.38645340834057 | 0.16330070407776034 | 29.31876509625081 | 22.243799336105926 | 4.342268189926172 | 5.4146805165448875 | 4.716333251171499 | 2.083810977494401 |
| user_002 | Jogging | 1.446034915455e12 | 1.76333 | -1.72382 | -0.345482 | -10.067193636363639 | -5.4374033636363635 | -1.052664909090909 | 3.1093326889000004 | 2.9715553923999996 | 0.119357812324 | 2.4900292957360963 | 12.23813765690058 | 11.830523636363639 | 3.7135833636363635 | 0.707182909090909 | 5.288836694214875 | 4.095206685950413 | 1.8130885867768596 | 139.96128951055874 | 13.790701398676768 | 0.5001076669102809 | 38.75524016966116 | 20.051422205409718 | 7.297770799175755 | 6.22537068532157 | 4.4778814416428805 | 2.701438653602142 |
| user_002 | Jumping | 1.446035033798e12 | -1.30229 | 0.307161 | -0.920286 | -6.6575549999999994 | -7.12697975 | 0.7179085000000001 | 1.6959592440999998 | 9.434787992100001e-2 | 0.8469263217960001 | 1.6239561095722383 | 10.874421038711667 | 5.355264999999999 | 7.43414075 | 1.6381945 | 3.794508333333333 | 5.764013104166667 | 1.3292357083333333 | 28.678863220224994 | 55.26644869081056 | 2.68368121983025 | 20.47484638194861 | 50.37997567727834 | 2.93820038376659 | 4.524913964038278 | 7.0978852961483065 | 1.7141179608669266 |
| user_002 | Jumping | 1.446035033863e12 | -0.344284 | -0.535886 | 0.344284 | -5.394900799999999 | -5.8087610000000005 | 0.6431836000000001 | 0.11853147265599999 | 0.287173804996 | 0.11853147265599999 | 0.7240419534170655 | 8.844345221652747 | 5.050616799999999 | 5.272875000000001 | 0.2988996000000001 | 4.045730026666666 | 5.665785483333333 | 1.1231684866666667 | 25.508730060442232 | 27.80321076562501 | 8.934097088016006e-2 | 21.481623117647334 | 45.86462269494767 | 2.368428501189304 | 4.634827193935857 | 6.7723424821067395 | 1.5389699481111723 |
| user_002 | Jumping | 1.446035033927e12 | -0.842448 | -0.650847 | -1.26517 | -4.636158666666666 | -4.949108666666667 | 0.32512466666666673 | 0.7097186327039999 | 0.42360181740899994 | 1.6006551288999997 | 1.6534737914502906 | 7.645866649952336 | 3.793710666666666 | 4.298261666666667 | 1.5902946666666666 | 4.0037268 | 5.437864847222222 | 1.20102285 | 14.392240622380438 | 18.47505335513611 | 2.5290371268284444 | 20.300059368436184 | 41.29969447164574 | 2.3951966054624942 | 4.505558718786848 | 6.42648383423204 | 1.5476422730923622 |
| user_002 | Jumping | 1.446035034057e12 | -6.70546 | -11.6871 | 0.842448 | -4.46850775 | -5.37382525 | 0.20537325000000006 | 44.96319381160001 | 136.58830640999997 | 0.7097186327039999 | 13.50041550672808 | 7.712791209417686 | 2.2369522500000008 | 6.313274749999999 | 0.6370747499999999 | 3.647825774107143 | 5.225442836309524 | 1.1384730526785716 | 5.0039553687800655 | 39.85743806898755 | 0.40586423708756236 | 16.918744297309093 | 36.98160318013506 | 2.0470234572839994 | 4.11324012152331 | 6.081250133001854 | 1.4307422749342382 |
| user_002 | Jumping | 1.446035034445e12 | -1.76214 | -0.267643 | -0.996927 | -4.765055181818181 | -5.541912 | 0.2571926363636364 | 3.1051373796000004 | 7.163277544900001e-2 | 0.993863443329 | 2.042212917004003 | 7.982462080994015 | 3.002915181818181 | 5.274269 | 1.2541196363636364 | 4.781736989249638 | 6.35182557994228 | 1.1887342204971796 | 9.01749958919412 | 27.817913484361004 | 1.5728160623128598 | 31.74650931534741 | 50.318931638349575 | 2.334578496357329 | 5.634404078103327 | 7.093583835999231 | 1.5279327525638453 |
| user_002 | Jumping | 1.446035035417e12 | -7.1653 | -11.61046 | 0.995729 | -6.830867636363636 | -8.680694454545455 | 0.25719163636363634 | 51.34152409 | 134.8027814116 | 0.991476241441 | 13.679758102504628 | 11.519586108272428 | 0.3344323636363642 | 2.9297655454545453 | 0.7385373636363637 | 4.467641735537191 | 6.43369567768595 | 0.7800238347107438 | 0.11184500584740534 | 8.583526151332569 | 0.5454374374869505 | 27.654650666007146 | 49.408091581727376 | 0.6421275864403456 | 5.258768930653556 | 7.029088958160038 | 0.801328638225507 |
| user_002 | Jumping | 1.446035036064e12 | 0.652044 | 0.881966 | -1.11189 | -5.5419113636363635 | -6.719390818181818 | -0.2897428181818182 | 0.42516137793599995 | 0.7778640251560001 | 1.2362993721000002 | 1.561833786032304 | 9.428134272108517 | 6.193955363636364 | 7.601356818181818 | 0.8221471818181818 | 4.442940404958677 | 6.1952234876033065 | 0.7898413223140497 | 38.365083046719676 | 57.78062547731922 | 0.6759259885715785 | 26.80721992624767 | 44.61862266479724 | 0.8233101719703998 | 5.177568920472973 | 6.6797172593454315 | 0.907364409689073 |
| user_002 | Jumping | 1.446035036647e12 | -0.459245 | -2.14534 | -0.767005 | -5.242317090909091 | -6.984149545454546 | 4.4688181818181814e-2 | 0.210905970025 | 4.6024837156 | 0.5882966700250001 | 2.3241528253645454 | 9.347194647011769 | 4.783072090909091 | 4.838809545454546 | 0.8116931818181818 | 4.471758231404959 | 6.197756520661158 | 0.8256284876033056 | 22.87777862683346 | 23.41407781718203 | 0.658845821410124 | 24.532754052048745 | 43.48848930565539 | 0.8307662014794853 | 4.9530550221099645 | 6.594580297915508 | 0.9114637686049212 |
| user_002 | Jumping | 1.446035041373e12 | -0.535886 | -1.64717 | -1.03525 | -5.538427636363636 | -7.430056727272727 | -0.43257309090909096 | 0.287173804996 | 2.7131690089 | 1.0717425625 | 2.0179408753469463 | 9.764384790075978 | 5.002541636363636 | 5.782886727272727 | 0.602676909090909 | 4.196865702479339 | 5.813602826446282 | 0.5177994214876033 | 25.025422823551768 | 33.44177890046707 | 0.3632194567513718 | 22.45182419827358 | 38.34933714049541 | 0.40652773556788735 | 4.73833559367354 | 6.1926841628243405 | 0.6375952756787705 |
| user_002 | Jumping | 1.44603504215e12 | -1.14901 | -0.420925 | 0.344284 | -4.291276363636364 | -6.2247104545454555 | -0.509214 | 1.3202239801000002 | 0.177177855625 | 0.11853147265599999 | 1.2711936549483718 | 8.034460275069408 | 3.1422663636363644 | 5.803785454545455 | 0.8534980000000001 | 3.6496149421487605 | 6.059993644628099 | 0.4718784380165289 | 9.8738379000405 | 33.683925602393394 | 0.7284588360040002 | 18.037898103949008 | 41.549573370724985 | 0.2748523318328828 | 4.247104673062463 | 6.445895854784266 | 0.5242636091060325 |
| user_002 | Jumping | 1.446035042538e12 | -3.63983 | -3.86975 | -0.383802 | -5.1761277272727275 | -8.179046636363637 | -0.6102402727272728 | 13.248362428899998 | 14.974965062499999 | 0.147303975204 | 5.326408871519722 | 10.024669937451387 | 1.5362977272727276 | 4.309296636363637 | 0.2264382727272728 | 3.2996652644628095 | 5.7654663553719 | 0.49024671900826444 | 2.360210706823348 | 18.57003750017496 | 5.127429135571077e-2 | 14.209285799045562 | 38.99125749161312 | 0.2935062012731563 | 3.769520632526842 | 6.244297998303182 | 0.5417621260970135 |
| user_002 | Jumping | 1.446035042926e12 | -10.11596 | -15.25089 | 3.7722e-2 | -5.256252272727273 | -8.29400809090909 | -0.7739722727272725 | 102.33264672159999 | 232.58964579210001 | 1.422949284e-3 | 18.30092116432897 | 10.515775377709382 | 4.859707727272727 | 6.95688190909091 | 0.8116942727272726 | 3.8808028760330573 | 6.340904438016529 | 0.6865985123966943 | 23.616759194514252 | 48.39820589703639 | 0.658847592378256 | 18.500328994784294 | 45.368967180312275 | 0.8470754660074215 | 4.301200878218117 | 6.735648979891416 | 0.9203670278793246 |
| user_002 | Jumping | 1.446035043445e12 | -3.56319 | -7.81675 | -1.34181 | -4.611774090909091 | -6.754229181818181 | -1.1641431818181818 | 12.696322976100001 | 61.1015805625 | 1.8004540760999999 | 8.694731601073146 | 8.943475057995606 | 1.0485840909090913 | 1.0625208181818184 | 0.17766681818181818 | 3.6682985206611574 | 5.689458504132232 | 0.9301391074380163 | 1.0995285957076455 | 1.1289504890697608 | 3.156549828285124e-2 | 18.615440480806782 | 42.168303896146696 | 1.3272466957004447 | 4.314561447100595 | 6.493712643484211 | 1.1520619322330048 |
| user_002 | Jumping | 1.446035043574e12 | -14.36952 | -19.58108 | 0.420925 | -6.416312272727273 | -9.380912181818182 | -0.700815909090909 | 206.4831050304 | 383.4186939664 | 0.177177855625 | 24.29154126136143 | 12.046808337182016 | 7.953207727272726 | 10.200167818181818 | 1.121740909090909 | 4.3900503388429755 | 6.595844338842975 | 1.0026629669421487 | 63.25351315315061 | 104.04342351907202 | 1.2583026671280988 | 25.11940654493285 | 53.231245821914314 | 1.426878065493329 | 5.011926430518793 | 7.29597463139191 | 1.1945200146893016 |
| user_002 | Jumping | 1.446035043638e12 | -13.64143 | -17.24354 | -0.1922 | -6.736809545454546 | -9.562062181818181 | -0.721717909090909 | 186.08861244489998 | 297.3396717316 | 3.694084e-2 | 21.987842663992755 | 12.381983018969633 | 6.904620454545454 | 7.681477818181818 | 0.529517909090909 | 4.575951495867769 | 6.661716694214875 | 0.9770105702479338 | 47.67378362132747 | 59.00510147121931 | 0.2803892160480082 | 27.306408765552234 | 54.19550905593094 | 1.3924727585542156 | 5.225553441077054 | 7.3617599156676485 | 1.1800308294931179 |
| user_002 | Jumping | 1.446035044156e12 | -15.21256 | -18.89131 | -0.767005 | -6.479016363636364 | -8.948935545454544 | -0.46740981818181826 | 231.4219817536 | 356.88159351610005 | 0.5882966700250001 | 24.267094427222332 | 11.74871380185931 | 8.733543636363635 | 9.942374454545456 | 0.2995951818181818 | 4.947753066115702 | 6.188887628099172 | 0.6663303057851239 | 76.27478444826774 | 98.85080979439806 | 8.975727296866941e-2 | 29.89711555340259 | 46.60736227828711 | 0.736107912280498 | 5.467825486736257 | 6.826958493962528 | 0.8579673142261878 |
| user_002 | Jumping | 1.446035044545e12 | -0.650847 | -0.420925 | 0.267643 | -5.46527009090909 | -6.9109917272727275 | -0.8715146363636365 | 0.42360181740899994 | 0.177177855625 | 7.163277544900001e-2 | 0.8200075905032831 | 9.38892652315506 | 4.814423090909091 | 6.490066727272728 | 1.1391576363636364 | 4.232968380165289 | 6.326651388429752 | 0.7106671983471075 | 23.178669698278643 | 42.120966124452536 | 1.297680120485587 | 23.35106811043113 | 46.895908393370604 | 0.6108041687504081 | 4.8322942905447235 | 6.848058731740741 | 0.7815396143193306 |
| user_002 | Jumping | 1.446035045839e12 | -8.85139 | -11.72542 | 0.305964 | -5.521009272727273 | -7.200136545454544 | -0.35593254545454545 | 78.34710493210001 | 137.4854741764 | 9.361396929600001e-2 | 14.694427279679736 | 9.497917300387376 | 3.3303807272727273 | 4.525283454545455 | 0.6618965454545455 | 4.381500247933884 | 5.622635090909091 | 0.6425778429752066 | 11.091435788589619 | 20.478190343982853 | 0.4381070368846612 | 23.812058545512084 | 36.44259565766158 | 0.590843696719027 | 4.879760090979072 | 6.036770300223587 | 0.7686635783741982 |
| user_002 | Jumping | 1.446035046098e12 | -3.29495 | -4.59784 | -0.15388 | -5.844989272727273 | -8.078020181818182 | -0.742618909090909 | 10.856695502500001 | 21.140132665599996 | 2.3679054399999996e-2 | 5.658666558695608 | 10.38518386833713 | 2.5500392727272727 | 3.4801801818181826 | 0.588738909090909 | 4.263687842975207 | 5.87725885123967 | 0.7185846528925619 | 6.502700292451438 | 12.111654097920038 | 0.34661350307755356 | 20.709634302860838 | 39.065419343592374 | 0.8314188977151395 | 4.550783921794226 | 6.250233543123999 | 0.911821746678121 |
| user_002 | Jumping | 1.44603504681e12 | -0.229323 | 0.1922 | -0.498763 | -4.876532181818181 | -6.099299000000001 | -0.7286840000000001 | 5.2589038329e-2 | 3.694084e-2 | 0.24876453016900002 | 0.5816308180435421 | 8.625721908563833 | 4.647209181818181 | 6.291499000000001 | 0.2299210000000001 | 4.378649504132231 | 6.121748975206612 | 0.6143912975206612 | 21.59655317957521 | 39.58295966700101 | 5.286366624100004e-2 | 23.7805886174197 | 45.32605095967318 | 0.49155458285741943 | 4.876534488488695 | 6.732462473692162 | 0.7011095369893491 |
| user_002 | Jumping | 1.446035046875e12 | -0.842448 | -1.03405 | -0.345482 | -4.9636238181818175 | -6.322253545454546 | -0.6276578181818181 | 0.7097186327039999 | 1.0692594024999997 | 0.119357812324 | 1.377801091423577 | 8.565835701140356 | 4.121175818181817 | 5.288203545454547 | 0.2821758181818181 | 4.2956750578512395 | 5.860474157024794 | 0.5868385950413223 | 16.984090124366574 | 27.96509673815804 | 7.962319236657846e-2 | 23.020951708247193 | 41.81179228320366 | 0.4676545045738995 | 4.798015392664679 | 6.466203854132938 | 0.6838526921595758 |
| user_002 | Jumping | 1.446035047198e12 | -13.21991 | -16.63042 | 0.420925 | -7.189685909090908 | -9.31123781818182 | -0.6172074545454546 | 174.76602040810002 | 276.5708693764 | 0.177177855625 | 21.248860384503566 | 12.192681806384348 | 6.030224090909092 | 7.3191821818181815 | 1.0381324545454547 | 5.007293181818182 | 6.497985338842975 | 0.5244497603305786 | 36.36360258658039 | 53.57042781064475 | 1.0777189931805704 | 30.092576058743646 | 51.20800629042747 | 0.39204926912054333 | 5.485670064699812 | 7.1559769626814385 | 0.6261383785718164 |
| user_002 | Jumping | 1.446035047263e12 | -4.06135 | -5.90073 | -3.8919e-2 | -6.175939545454545 | -8.067569636363636 | -0.5301158181818182 | 16.4945638225 | 34.8186145329 | 1.514688561e-3 | 7.163427464835601 | 10.58790123368122 | 2.114589545454545 | 2.166839636363636 | 0.4911968181818182 | 4.47302656198347 | 5.761348685950413 | 0.5618199338842975 | 4.4714889457456595 | 4.695194009716494 | 0.24127431419194217 | 24.693217809402338 | 42.04669015402345 | 0.4133996612237356 | 4.969227083702489 | 6.48434192143069 | 0.6429616327773654 |
| user_002 | Jumping | 1.446035048687e12 | -0.650847 | -1.95374 | 7.6042e-2 | -4.9009182727272735 | -6.210774181818182 | -0.8749986363636364 | 0.42360181740899994 | 3.8170999876000002 | 5.782385764e-3 | 2.0606999273967572 | 8.582328109355055 | 4.250071272727274 | 4.257034181818182 | 0.9510406363636363 | 4.732400454545455 | 5.830070454545455 | 0.7724236694214877 | 18.06310582326163 | 18.1223400251684 | 0.9044782920149503 | 26.822176913056808 | 41.98446965925354 | 0.8800927343924942 | 5.179013121537423 | 6.479542395821911 | 0.938132578259861 |
| user_002 | Jumping | 1.446035049399e12 | -13.52647 | -17.3585 | -0.958607 | -6.747261090909091 | -8.673726545454544 | -0.18174954545454547 | 182.9653906609 | 301.31752224999997 | 0.918927380449 | 22.027297616624445 | 11.464334396480872 | 6.779208909090909 | 8.684773454545455 | 0.7768574545454545 | 4.79384026446281 | 5.962767528925619 | 0.6299100495867768 | 45.957673433097554 | 75.4252899567774 | 0.603507504682843 | 28.706956726588132 | 41.384309342338895 | 0.6132338470071389 | 5.357887337989492 | 6.433063760164273 | 0.7830924894334889 |
| user_002 | Jumping | 1.4460350505e12 | -6.24561 | -9.73276 | -1.15021 | -5.1587095454545455 | -7.287228 | -0.35593318181818184 | 39.0076442721 | 94.72661721760001 | 1.3229830441 | 11.62141319004707 | 9.455231050646873 | 1.0869004545454546 | 2.445532000000001 | 0.7942768181818181 | 3.9922799008264462 | 5.749314008264463 | 0.5428184214876032 | 1.1813525980911157 | 5.980626763024005 | 0.6308756639010329 | 21.690321855347296 | 39.86753617989176 | 0.427005639025329 | 4.657286962958938 | 6.314074451563884 | 0.6534566848883934 |
| user_002 | Sitting | 1.44603456478e12 | -0.765807 | -0.114362 | 9.4262 | -0.765807 | -0.114362 | 9.4262 | 0.586460361249 | 1.3078667044000002e-2 | 88.85324643999999 | 9.457948269487044 | 9.457948269487044 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| user_002 | Sitting | 1.446034565169e12 | -0.842448 | -0.152682 | 9.4262 | -0.8205507142857142 | -0.1417335714285714 | 9.431674285714285 | 0.7097186327039999 | 2.3311793124000002e-2 | 88.85324643999999 | 9.465002739874299 | 9.468437272237447 | 2.189728571428573e-2 | 1.0948428571428609e-2 | 5.474285714285543e-3 | 1.6631628911564604e-2 | 1.86257112244898e-2 | 1.611004081632699e-2 | 4.7949112165306194e-4 | 1.198680881836743e-4 | 2.9967804081630776e-5 | 5.057707334111117e-4 | 4.989748108321516e-4 | 4.2989052794818944e-4 | 2.2489347109489678e-2 | 2.233774408556405e-2 | 2.073380157974387e-2 |
| user_002 | Sitting | 1.446034565688e12 | -0.919089 | -3.7722e-2 | 9.4262 | -0.8389643636363636 | -0.13874772727272727 | 9.433167272727273 | 0.8447245899210001 | 1.422949284e-3 | 88.85324643999999 | 9.470976400519906 | 9.471666852469868 | 8.012463636363643e-2 | 0.10102572727272727 | 6.967272727273155e-3 | 3.203548127705629e-2 | 4.438884426406926e-2 | 1.5275845992391623e-2 | 6.4199573524049685e-3 | 1.020619757098347e-2 | 4.854288925620431e-5 | 1.4169701993062044e-3 | 2.985819414044147e-3 | 4.3332102977010464e-4 | 3.764266461485165e-2 | 5.464265196752576e-2 | 2.0816364470533866e-2 |
| user_002 | Sitting | 1.446034566658e12 | -0.727487 | -0.305964 | 9.54116 | -0.7692910909090908 | -0.16313345454545455 | 9.447101818181817 | 0.529237335169 | 9.361396929600001e-2 | 91.0337341456 | 9.57374458872102 | 9.480753256798605 | 4.180409090909076e-2 | 0.14283054545454546 | 9.405818181818226e-2 | 8.455804958677687e-2 | 5.732220661157026e-2 | 4.180363636363635e-2 | 1.747582016735525e-3 | 2.0400564714842976e-2 | 8.846941566942232e-3 | 1.325132104521563e-2 | 4.601724310780617e-3 | 2.299167754770868e-3 | 0.1151143824429234 | 6.783601042794761e-2 | 4.794963769175809e-2 |
| user_002 | Sitting | 1.446034567047e12 | -0.765807 | -0.152682 | 9.46452 | -0.7832257272727273 | -0.18055172727272728 | 9.436650909090906 | 0.586460361249 | 2.3311793124000002e-2 | 89.5771388304 | 9.496678945019307 | 9.471570465860033 | 1.7418727272727308e-2 | 2.7869727272727268e-2 | 2.7869090909094396e-2 | 5.763904132231403e-2 | 6.555633884297521e-2 | 3.705322314049604e-2 | 3.034120598016541e-4 | 7.76721698256198e-4 | 7.766862280993679e-4 | 7.076373251673926e-3 | 6.461827551674681e-3 | 1.9317863429000853e-3 | 8.412118194410921e-2 | 8.03854934156324e-2 | 4.3952091450806814e-2 |
| user_002 | Sitting | 1.446034567111e12 | -0.765807 | -0.114362 | 9.4262 | -0.75884 | -0.1666170909090909 | 9.436650909090908 | 0.586460361249 | 1.3078667044000002e-2 | 88.85324643999999 | 9.457948269487044 | 9.468972667311892 | 6.9670000000000565e-3 | 5.2255090909090904e-2 | 1.0450909090907956e-2 | 3.578690082644627e-2 | 6.143933057851239e-2 | 3.546975206611594e-2 | 4.853908900000079e-5 | 2.7305945259173546e-3 | 1.0922150082642256e-4 | 1.519209039241922e-3 | 5.845110941862509e-3 | 1.8711077313298444e-3 | 3.897703220156612e-2 | 7.645332525052465e-2 | 4.325630279311726e-2 |
| user_002 | Sitting | 1.446034567241e12 | -0.804128 | -0.114362 | 9.46452 | -0.7727747272727273 | -0.17358445454545457 | 9.440134545454544 | 0.646621840384 | 1.3078667044000002e-2 | 89.5771388304 | 9.499307308316117 | 9.473686950322545 | 3.135327272727262e-2 | 5.9222454545454564e-2 | 2.4385454545456042e-2 | 3.3253355371900814e-2 | 6.0172553719008266e-2 | 3.515305785123989e-2 | 9.83027710710737e-4 | 3.507299122388432e-3 | 5.946503933885027e-4 | 1.3095898869752052e-3 | 5.8164262194012025e-3 | 1.7949836549962473e-3 | 3.618825620246443e-2 | 7.626549822430326e-2 | 4.23672474323769e-2 |
| user_002 | Sitting | 1.446034568082e12 | -0.689167 | -0.191003 | 9.4262 | -0.7553561818181819 | -0.156166 | 9.429683636363636 | 0.47495115388899994 | 3.6482146009e-2 | 88.85324643999999 | 9.45328936084673 | 9.461340420717299 | 6.61891818181819e-2 | 3.483700000000001e-2 | 3.4836363636365775e-3 | 2.0585446280991692e-2 | 3.64201570247934e-2 | 2.406876033057886e-2 | 4.381007789760341e-3 | 1.2136165690000004e-3 | 1.2135722314051077e-5 | 7.68973975536438e-4 | 1.973744353509392e-3 | 7.92131692862547e-4 | 2.77303800106749e-2 | 4.442684271371748e-2 | 2.8144834212738702e-2 |
| user_002 | Sitting | 1.446034569054e12 | -0.765807 | -0.191003 | 9.38788 | -0.7553561818181819 | -0.14223136363636366 | 9.433167272727273 | 0.586460361249 | 3.6482146009e-2 | 88.13229089439999 | 9.420999596733777 | 9.464589357092626 | 1.0450818181818144e-2 | 4.877163636363635e-2 | 4.528727272727373e-2 | 2.7235760330578507e-2 | 3.4520107438016534e-2 | 4.1803636363636994e-2 | 1.0921960066942071e-4 | 2.3786725135867756e-3 | 2.050937071074471e-3 | 1.3459775338459804e-3 | 2.280452508866266e-3 | 2.2395923906837365e-3 | 3.6687566474842406e-2 | 4.775408368785088e-2 | 4.732433190953399e-2 |
| user_002 | Sitting | 1.446034569312e12 | -0.727487 | -0.152682 | 9.4262 | -0.7379379090909091 | -0.1352639090909091 | 9.429683636363634 | 0.529237335169 | 2.3311793124000002e-2 | 88.85324643999999 | 9.455463794457307 | 9.459572879689434 | 1.0450909090909066e-2 | 1.741809090909091e-2 | 3.483636363634801e-3 | 2.153520661157021e-2 | 2.7869371900826445e-2 | 2.4702148760331447e-2 | 1.0922150082644576e-4 | 3.0338989091735545e-4 | 1.21357223140387e-5 | 6.707759668279468e-4 | 1.524711514772352e-3 | 1.0194006743802228e-3 | 2.589934298062302e-2 | 3.904755452998756e-2 | 3.192805466012959e-2 |
| user_002 | Sitting | 1.446034569443e12 | -0.727487 | -0.152682 | 9.4262 | -0.7449052727272728 | -0.14571481818181817 | 9.429683636363636 | 0.529237335169 | 2.3311793124000002e-2 | 88.85324643999999 | 9.455463794457307 | 9.460267960270265 | 1.741827272727281e-2 | 6.9671818181818446e-3 | 3.4836363636365775e-3 | 2.3752140495867744e-2 | 1.741833884297521e-2 | 1.8051570247934832e-2 | 3.033962248016558e-4 | 4.8541622487603674e-5 | 1.2135722314051077e-5 | 8.925379239271204e-4 | 6.343758824335087e-4 | 5.858244135237135e-4 | 2.9875373201470143e-2 | 2.5186819617282145e-2 | 2.4203809896867757e-2 |
| user_002 | Sitting | 1.446034569896e12 | -0.765807 | -0.152682 | 9.4262 | -0.7518726363636362 | -0.14919836363636363 | 9.436650909090908 | 0.586460361249 | 2.3311793124000002e-2 | 88.85324643999999 | 9.458489234247349 | 9.467781289695454 | 1.39343636363638e-2 | 3.483636363636383e-3 | 1.0450909090907956e-2 | 2.4068917355371876e-2 | 1.2034322314049604e-2 | 1.74181818181824e-2 | 1.9416648995041779e-4 | 1.2135722314049723e-5 | 1.0922150082642256e-4 | 8.362799605987959e-4 | 1.8313804574830977e-4 | 4.909451299774928e-4 | 2.8918505504240636e-2 | 1.353285061427598e-2 | 2.2157281646842258e-2 |
| user_002 | Sitting | 1.446034570996e12 | -0.765807 | -0.114362 | 9.4262 | -0.7623234545454545 | -0.15964954545454543 | 9.429683636363636 | 0.586460361249 | 1.3078667044000002e-2 | 88.85324643999999 | 9.457948269487044 | 9.461895649839656 | 3.4835454545455447e-3 | 4.528754545454543e-2 | 3.4836363636365775e-3 | 1.266781818181819e-2 | 3.166985123966942e-2 | 2.6919008264463008e-2 | 1.2135088933884925e-5 | 2.050961773297518e-3 | 1.2135722314051077e-5 | 3.552524841622832e-4 | 1.5004470374380165e-3 | 1.1639260946656388e-3 | 1.884814272447774e-2 | 3.873560426065426e-2 | 3.411636109941444e-2 |
| user_002 | Sitting | 1.446034571061e12 | -0.765807 | -0.152682 | 9.46452 | -0.7623234545454545 | -0.15268218181818183 | 9.433167272727273 | 0.586460361249 | 2.3311793124000002e-2 | 89.5771388304 | 9.496678945019307 | 9.465226755939694 | 3.4835454545455447e-3 | 1.8181818181584042e-7 | 3.135272727272742e-2 | 1.266782644628101e-2 | 2.5335917355371895e-2 | 2.691900826446317e-2 | 1.2135088933884925e-5 | 3.3057851238818004e-14 | 9.829935074380258e-4 | 3.5525254174079574e-4 | 1.0591388312321563e-3 | 1.1639260946656493e-3 | 1.8848144251909674e-2 | 3.25444132107518e-2 | 3.411636109941459e-2 |
| user_002 | Sitting | 1.446034571579e12 | -0.727487 | -0.229323 | 9.4262 | -0.7623234545454545 | -0.1666169090909091 | 9.440134545454544 | 0.529237335169 | 5.2589038329e-2 | 88.85324643999999 | 9.45701183321127 | 9.472428728452309 | 3.4836454545454476e-2 | 6.27060909090909e-2 | 1.3934545454544534e-2 | 1.1401074380165273e-2 | 2.7236082644628092e-2 | 2.8185785123967057e-2 | 1.213578565297516e-3 | 3.932053837099173e-3 | 1.941715570247677e-4 | 3.5966524376709197e-4 | 1.3084813420150261e-3 | 1.1551001148009435e-3 | 1.8964842307994337e-2 | 3.617293659650853e-2 | 3.398676381771209e-2 |
| user_002 | Sitting | 1.44603457242e12 | -0.727487 | -0.152682 | 9.46452 | -0.7449052727272726 | -0.15964963636363638 | 9.429683636363633 | 0.529237335169 | 2.3311793124000002e-2 | 89.5771388304 | 9.493665675527708 | 9.460525402885743 | 1.7418272727272588e-2 | 6.96763636363637e-3 | 3.483636363636755e-2 | 3.0719429752066107e-2 | 2.4069231404958678e-2 | 2.8819173553719324e-2 | 3.0339622480164805e-4 | 4.854795649586786e-5 | 1.2135722314052313e-3 | 1.4662266727175069e-3 | 7.303726936025546e-4 | 1.2874898127723882e-3 | 3.829133939571071e-2 | 2.7025408296685448e-2 | 3.588160828018148e-2 |
| user_002 | Sitting | 1.446034572485e12 | -0.727487 | -7.6042e-2 | 9.46452 | -0.7449052727272728 | -0.14919863636363637 | 9.436650909090908 | 0.529237335169 | 5.782385764e-3 | 89.5771388304 | 9.492742414673064 | 9.46732361206275 | 1.741827272727281e-2 | 7.315663636363637e-2 | 2.786909090909262e-2 | 2.976934710743803e-2 | 2.9136322314049588e-2 | 2.6285619834711227e-2 | 3.033962248016558e-4 | 5.351893444041323e-3 | 7.766862280992689e-4 | 1.4231998481344878e-3 | 1.1893258346273481e-3 | 1.075666296018078e-3 | 3.772532104746741e-2 | 3.448660369806438e-2 | 3.279735196655483e-2 |
| user_002 | Sitting | 1.446034573133e12 | -0.765807 | -0.152682 | 9.4262 | -0.7518726363636364 | -0.1491987272727273 | 9.436650909090908 | 0.586460361249 | 2.3311793124000002e-2 | 88.85324643999999 | 9.458489234247349 | 9.467899249887088 | 1.3934363636363578e-2 | 3.4832727272727237e-3 | 1.0450909090907956e-2 | 2.6919157024793385e-2 | 3.705350413223141e-2 | 1.551801652892512e-2 | 1.941664899504116e-4 | 1.2133188892561959e-5 | 1.0922150082642256e-4 | 1.007281305137491e-3 | 1.8832758142344102e-3 | 2.7470862329075655e-4 | 3.173769533437315e-2 | 4.339672584693931e-2 | 1.6574336285075084e-2 |
| user_002 | Sitting | 1.446034573262e12 | -0.765807 | -0.114362 | 9.46452 | -0.7518725454545454 | -0.1491987272727273 | 9.440134545454544 | 0.586460361249 | 1.3078667044000002e-2 | 89.5771388304 | 9.496140155805042 | 9.471356114557238 | 1.3934454545454611e-2 | 3.483672727272728e-2 | 2.4385454545456042e-2 | 2.3435413223140508e-2 | 3.7686917355371906e-2 | 1.678479338842949e-2 | 1.9416902347934065e-4 | 1.2135975670743808e-3 | 5.946503933885027e-4 | 8.208248797753583e-4 | 1.9229953147746053e-3 | 3.1883852261458203e-4 | 2.8650041531826063e-2 | 4.385197047767187e-2 | 1.785605002834003e-2 |
| user_002 | Sitting | 1.446034573392e12 | -0.765807 | -0.152682 | 9.38788 | -0.7518725454545454 | -0.145715 | 9.433167272727273 | 0.586460361249 | 2.3311793124000002e-2 | 88.13229089439999 | 9.420300581657306 | 9.46434959120047 | 1.3934454545454611e-2 | 6.967000000000001e-3 | 4.528727272727373e-2 | 2.3435413223140508e-2 | 3.483659504132232e-2 | 1.931834710743775e-2 | 1.9416902347934065e-4 | 4.8539089000000014e-5 | 2.050937071074471e-3 | 8.119984968407226e-4 | 1.7674321221953415e-3 | 4.732931702479435e-4 | 2.849558732226312e-2 | 4.204083874276703e-2 | 2.1755302118057186e-2 |
| user_002 | Sitting | 1.446034573455e12 | -0.689167 | -0.152682 | 9.38788 | -0.7518725454545454 | -0.14223127272727273 | 9.4262 | 0.47495115388899994 | 2.3311793124000002e-2 | 88.13229089439999 | 9.414380162358698 | 9.457338697442692 | 6.270554545454543e-2 | 1.0450727272727278e-2 | 3.8320000000000576e-2 | 2.3435404958677686e-2 | 3.1669578512396705e-2 | 2.0585123966941798e-2 | 3.931985430752063e-3 | 1.0921770052892573e-4 | 1.4684224000000442e-3 | 8.119974603846732e-4 | 1.590906939531931e-3 | 5.52726989030811e-4 | 2.849556913600206e-2 | 3.988617479192422e-2 | 2.351014651232125e-2 |
| user_002 | Sitting | 1.446034573521e12 | -0.727487 | -0.114362 | 9.46452 | -0.7483889090909092 | -0.13178027272727272 | 9.429683636363636 | 0.529237335169 | 1.3078667044000002e-2 | 89.5771388304 | 9.493126715293176 | 9.460346873567627 | 2.0901909090909165e-2 | 1.7418272727272713e-2 | 3.4836363636364e-2 | 2.4068818181818195e-2 | 2.628573553719008e-2 | 2.2485289256198113e-2 | 4.368898036446312e-4 | 3.033962248016524e-4 | 1.213572231404984e-3 | 8.340632161750568e-4 | 1.0845090320075132e-3 | 6.453997776108307e-4 | 2.8880152634206364e-2 | 3.293188473208773e-2 | 2.540471959323367e-2 |
| user_002 | Sitting | 1.44603457378e12 | -0.804128 | -0.114362 | 9.4262 | -0.748388909090909 | -0.13874763636363638 | 9.426199999999996 | 0.646621840384 | 1.3078667044000002e-2 | 88.85324643999999 | 9.461128206901542 | 9.4570153963809 | 5.5739090909090905e-2 | 2.4385636363636373e-2 | 3.552713678800501e-15 | 2.5018917355371924e-2 | 3.325310743801653e-2 | 2.121851239669487e-2 | 3.1068462553719e-3 | 5.946592608595046e-4 | 1.2621774483536189e-29 | 9.057764906649138e-4 | 1.7641233586235911e-3 | 7.160076165289551e-4 | 3.0096120857428018e-2 | 4.20014685293692e-2 | 2.6758318641666466e-2 |
| user_002 | Sitting | 1.446034574557e12 | -0.727487 | -0.191003 | 9.38788 | -0.7449053636363636 | -0.16661700000000002 | 9.436650909090908 | 0.529237335169 | 3.6482146009e-2 | 88.13229089439999 | 9.417962113725983 | 9.467609675926727 | 1.741836363636362e-2 | 2.438599999999999e-2 | 4.877090909090853e-2 | 3.198629752066117e-2 | 2.6919380165289254e-2 | 2.8185785123967865e-2 | 3.033993917685945e-4 | 5.946769959999995e-4 | 2.3786015735536644e-3 | 1.3537044120946666e-3 | 1.1264441829564242e-3 | 1.1948170241924013e-3 | 3.679272227077886e-2 | 3.356254136617822e-2 | 3.456612538588034e-2 |
| user_002 | Sitting | 1.446034574622e12 | -0.765807 | -0.114362 | 9.46452 | -0.7483889999999999 | -0.16661700000000002 | 9.44361818181818 | 0.586460361249 | 1.3078667044000002e-2 | 89.5771388304 | 9.496140155805042 | 9.474829731901304 | 1.7418000000000156e-2 | 5.225500000000001e-2 | 2.0901818181819465e-2 | 3.1986272727272766e-2 | 2.913626446280992e-2 | 2.7552396694216084e-2 | 3.033867240000054e-4 | 2.730585025000001e-3 | 4.368860033058388e-4 | 1.3537035483854264e-3 | 1.304070425015778e-3 | 1.1639260946657436e-3 | 3.679271053327583e-2 | 3.611191527758917e-2 | 3.411636109941597e-2 |
| user_002 | Sitting | 1.446034576823e12 | -0.727487 | -0.191003 | 9.4262 | -0.7518726363636364 | -0.1666169090909091 | 9.422716363636361 | 0.529237335169 | 3.6482146009e-2 | 88.85324643999999 | 9.456160210211014 | 9.454272305984539 | 2.4385636363636443e-2 | 2.4386090909090913e-2 | 3.483636363638354e-3 | 2.7869264462809906e-2 | 2.7869404958677693e-2 | 2.7552396694216084e-2 | 5.94659260859508e-4 | 5.946814298264465e-4 | 1.2135722314063454e-5 | 1.0657548036927113e-3 | 1.4430797806025554e-3 | 1.042568871525224e-3 | 3.2645900258573224e-2 | 3.7987889920375356e-2 | 3.228883509086731e-2 |
| user_002 | Sitting | 1.446034577018e12 | -0.727487 | -0.114362 | 9.4262 | -0.7553562727272727 | -0.16661700000000002 | 9.408781818181815 | 0.529237335169 | 1.3078667044000002e-2 | 88.85324643999999 | 9.454922656596032 | 9.440658106002388 | 2.7869272727272687e-2 | 5.225500000000001e-2 | 1.7418181818184664e-2 | 2.4385636363636352e-2 | 3.51535041322314e-2 | 2.4385454545455074e-2 | 7.766963623471052e-4 | 2.730585025000001e-3 | 3.033930578513388e-4 | 7.822206036093154e-4 | 1.9318294136048094e-3 | 8.10886900075132e-4 | 2.7968207014560577e-2 | 4.395258142140015e-2 | 2.847607592480277e-2 |
| user_002 | Sitting | 1.446034577212e12 | -0.765807 | -0.191003 | 9.38788 | -0.75884 | -0.16661700000000002 | 9.408781818181815 | 0.586460361249 | 3.6482146009e-2 | 88.13229089439999 | 9.420999596733777 | 9.440918817313054 | 6.9670000000000565e-3 | 2.438599999999999e-2 | 2.0901818181815912e-2 | 2.6919239669421488e-2 | 2.9136363636363637e-2 | 2.3752066115702967e-2 | 4.853908900000079e-5 | 5.946769959999995e-4 | 4.3688600330569024e-4 | 9.455071044012013e-4 | 1.3239312405514654e-3 | 8.175063849737142e-4 | 3.074909924536329e-2 | 3.6385865944779515e-2 | 2.8592068567589057e-2 |
| user_002 | Sitting | 1.446034577795e12 | -0.727487 | -0.152682 | 9.4262 | -0.7483890000000002 | -0.16313327272727274 | 9.426199999999998 | 0.529237335169 | 2.3311793124000002e-2 | 88.85324643999999 | 9.455463794457307 | 9.45736017391277 | 2.0902000000000198e-2 | 1.0451272727272726e-2 | 1.7763568394002505e-15 | 2.6919165289256218e-2 | 1.9002099173553715e-2 | 2.2801983471075295e-2 | 4.3689360400000826e-4 | 1.0922910161983468e-4 | 3.1554436208840472e-30 | 1.0315537862246422e-3 | 4.5014236270323057e-4 | 1.081182533433542e-3 | 3.211781104347932e-2 | 2.121655869134367e-2 | 3.28813402012987e-2 |
| user_002 | Sitting | 1.446034578183e12 | -0.765807 | -0.191003 | 9.46452 | -0.7414216363636363 | -0.15268236363636364 | 9.429683636363636 | 0.586460361249 | 3.6482146009e-2 | 89.5771388304 | 9.497372338581762 | 9.460134033566653 | 2.4385363636363677e-2 | 3.832063636363636e-2 | 3.4836363636364e-2 | 2.7235842975206627e-2 | 2.8502917355371894e-2 | 1.9635041322314608e-2 | 5.94645959677688e-4 | 1.4684711713140496e-3 | 1.213572231404984e-3 | 9.598359052314046e-4 | 1.2091844044785874e-3 | 6.509160150263041e-4 | 3.09812185885482e-2 | 3.477332892431479e-2 | 2.5513055775941543e-2 |
| user_002 | Sitting | 1.446034578766e12 | -0.765807 | -0.191003 | 9.4262 | -0.7623234545454545 | -0.1666170909090909 | 9.4262 | 0.586460361249 | 3.6482146009e-2 | 88.85324643999999 | 9.459185427258417 | 9.45851288519757 | 3.4835454545455447e-3 | 2.4385909090909097e-2 | 0.0 | 1.7101537190082703e-2 | 2.501930578512396e-2 | 2.7552396694215112e-2 | 1.2135088933884925e-5 | 5.94672562190083e-4 | 0.0 | 4.6778447599248973e-4 | 8.528350068084148e-4 | 1.2411534184823567e-3 | 2.162832577876729e-2 | 2.9203338966775953e-2 | 3.523000735853395e-2 |
| user_002 | Sitting | 1.446034581033e12 | -0.727487 | -0.191003 | 9.4262 | -0.7623234545454545 | -0.17010063636363637 | 9.478454545454545 | 0.529237335169 | 3.6482146009e-2 | 88.85324643999999 | 9.456160210211014 | 9.510648892733103 | 3.4836454545454476e-2 | 2.0902363636363636e-2 | 5.225454545454511e-2 | 1.741826446280995e-2 | 2.4068983471074386e-2 | 2.4702148760330965e-2 | 1.213578565297516e-3 | 4.369088055867768e-4 | 2.730537520661121e-3 | 4.4682294655447097e-4 | 1.0150141718414728e-3 | 1.1650293421487457e-3 | 2.1138186926850443e-2 | 3.185928705795961e-2 | 3.413252616125483e-2 |
| user_002 | Sitting | 1.446309384445e12 | -0.535886 | 3.2195 | 9.04299 | -0.7309707272727273 | 0.7809393636363636 | 9.339108181818183 | 0.287173804996 | 10.36518025 | 81.7756681401 | 9.613949354718693 | 9.520764338343959 | 0.1950847272727273 | 2.4385606363636363 | 0.2961181818181835 | 7.220688429752066e-2 | 0.7740065371900826 | 9.595917355371855e-2 | 3.805805081507439e-2 | 5.946577977222223 | 8.768597760330678e-2 | 8.956297980089407e-3 | 2.098633624418471 | 2.3968541014275056e-2 | 9.4637719647556e-2 | 1.4486661535421028 | 0.15481776711435627 |
| user_002 | Sitting | 1.44630938464e12 | -0.574206 | 3.2195 | 9.04299 | -0.6891668181818181 | 1.6901753636363637 | 9.234597272727274 | 0.329712530436 | 10.36518025 | 81.7756681401 | 9.616161444180104 | 9.565677478020099 | 0.11496081818181814 | 1.5293246363636364 | 0.19160727272727485 | 9.627590082644626e-2 | 1.260769123966942 | 0.14789826446281007 | 1.3215989717033047e-2 | 2.3388338433887688 | 3.6713346961984285e-2 | 1.2208727644145751e-2 | 3.044968591590844 | 3.636520440157808e-2 | 0.11049311129724672 | 1.744983837057193 | 0.19069662923496597 |
| user_002 | Sitting | 1.446309385353e12 | -0.574206 | 3.2195 | 9.15796 | -0.5602713636363635 | 3.2334381818181814 | 9.098737272727274 | 0.329712530436 | 10.36518025 | 83.86823136159998 | 9.724357261127132 | 9.672577064669866 | 1.3934636363636455e-2 | 1.3938181818181405e-2 | 5.9222727272725706e-2 | 4.877124793388429e-2 | 0.2900964710743801 | 5.162198347107379e-2 | 1.941740905867794e-4 | 1.942729123966827e-4 | 3.507331425619649e-3 | 3.8702471030300487e-3 | 0.2611054662973247 | 3.4476748824943267e-3 | 6.221131008932418e-2 | 0.5109848004562608 | 5.871690457180391e-2 |
| user_002 | Sitting | 1.446309386194e12 | -0.574206 | 3.25783 | 9.15796 | -0.5707223636363637 | 3.216019090909091 | 9.154476363636364 | 0.329712530436 | 10.613456308899998 | 83.86823136159998 | 9.73711457265118 | 9.719867683131518 | 3.4836363636362444e-3 | 4.181090909090868e-2 | 3.483636363634801e-3 | 2.2485289256198335e-2 | 3.674115702479346e-2 | 2.9135950413221225e-2 | 1.2135722314048756e-5 | 1.74815211900823e-3 | 1.21357223140387e-5 | 7.138006609226137e-4 | 1.8670350664162334e-3 | 1.1142885951163555e-3 | 2.6717048132655182e-2 | 4.320920117771484e-2 | 3.338096156668282e-2 |
| user_002 | Sitting | 1.446309386519e12 | -0.612526 | 3.2195 | 9.15796 | -0.5776896363636363 | 3.2125354545454545 | 9.161443636363638 | 0.375188100676 | 10.36518025 | 83.86823136159998 | 9.726695210207627 | 9.725675974401412 | 3.4836363636363665e-2 | 6.964545454545501e-3 | 3.483636363638354e-3 | 2.2801975206611588e-2 | 3.674016528925614e-2 | 2.6919008264462363e-2 | 1.2135722314049607e-3 | 4.85048933884304e-5 | 1.2135722314063454e-5 | 7.347622479376416e-4 | 1.7588897474079622e-3 | 1.2764573379413628e-3 | 2.7106498260336793e-2 | 4.1939119535440446e-2 | 3.572754312769579e-2 |
| user_002 | Sitting | 1.446309386648e12 | -0.574206 | 3.18118 | 9.15796 | -0.5776896363636363 | 3.1951163636363633 | 9.164927272727274 | 0.329712530436 | 10.1199061924 | 83.86823136159998 | 9.711737747923179 | 9.72320484422079 | 3.4836363636363554e-3 | 1.3936363636363414e-2 | 6.967272727274931e-3 | 2.3752057851239683e-2 | 3.610619834710726e-2 | 2.6285619834710904e-2 | 1.213572231404953e-5 | 1.9422223140495246e-4 | 4.854288925622906e-5 | 8.042663787250196e-4 | 1.7125032538692574e-3 | 1.2764573379413818e-3 | 2.8359590595158805e-2 | 4.1382402707784594e-2 | 3.5727543127696056e-2 |
| user_002 | Sitting | 1.44630938775e12 | -0.612526 | 3.2195 | 9.15796 | -0.6090425454545454 | 3.2299545454545453 | 9.185829090909092 | 0.375188100676 | 10.36518025 | 83.86823136159998 | 9.726695210207627 | 9.756244658044482 | 3.483454545454623e-3 | 1.0454545454545272e-2 | 2.786909090909262e-2 | 2.2168702479338845e-2 | 2.217214876033062e-2 | 2.913586776859457e-2 | 1.2134455570248473e-5 | 1.0929752066115321e-4 | 7.766862280992689e-4 | 7.612562527084889e-4 | 8.453236761081932e-4 | 1.0061617045830013e-3 | 2.7590872634052167e-2 | 2.9074450572765656e-2 | 3.1720052089853214e-2 |
| user_002 | Sitting | 1.446309387944e12 | -0.612526 | 3.2195 | 9.19628 | -0.6055588181818181 | 3.2299545454545453 | 9.178861818181819 | 0.375188100676 | 10.36518025 | 84.57156583839999 | 9.762783116974175 | 9.749457085970963 | 6.9671818181819e-3 | 1.0454545454545272e-2 | 1.741818181818111e-2 | 1.6784818181818206e-2 | 2.058909090909091e-2 | 2.6602314049586958e-2 | 4.8541622487604446e-5 | 1.0929752066115321e-4 | 3.0339305785121503e-4 | 4.1151804822614626e-4 | 7.051859090909096e-4 | 8.693590166792054e-4 | 2.0285907626383055e-2 | 2.6555336734654857e-2 | 2.948489472050401e-2 |
| user_002 | Sitting | 1.446309388852e12 | -0.650847 | 3.2195 | 9.19628 | -0.605558909090909 | 3.1985981818181823 | 9.178861818181819 | 0.42360181740899994 | 10.36518025 | 84.57156583839999 | 9.765262306042219 | 9.739133143241268 | 4.528809090909092e-2 | 2.090181818181769e-2 | 1.741818181818111e-2 | 2.1218752066115697e-2 | 2.09031404958675e-2 | 3.198611570247953e-2 | 2.0510111781900835e-3 | 4.368860033057645e-4 | 3.0339305785121503e-4 | 9.432911661134501e-4 | 5.847724156273373e-4 | 1.7001043714500528e-3 | 3.0713045536277416e-2 | 2.4182068059356243e-2 | 4.123232192649418e-2 |
| user_002 | Sitting | 1.446309389111e12 | -0.612526 | 3.06622 | 9.19628 | -0.6020752727272728 | 3.1776963636363633 | 9.178861818181817 | 0.375188100676 | 9.4017050884 | 84.57156583839999 | 9.713313493729933 | 9.73214500267062 | 1.0450727272727223e-2 | 0.11147636363636337 | 1.7418181818182887e-2 | 2.5969099173553722e-2 | 3.388677685950394e-2 | 2.7869090909091166e-2 | 1.0921770052892458e-4 | 1.2426979649586719e-2 | 3.0339305785127694e-4 | 1.1209155655725032e-3 | 1.863416669120946e-3 | 1.3945048186326265e-3 | 3.3480077144064396e-2 | 4.316731019094132e-2 | 3.734306921816452e-2 |
| user_002 | Sitting | 1.446309389241e12 | -0.574206 | 3.18118 | 9.15796 | -0.5985916363636363 | 3.1811800000000003 | 9.16841090909091 | 0.329712530436 | 10.1199061924 | 83.86823136159998 | 9.711737747923179 | 9.723228537958677 | 2.438563636363633e-2 | 4.440892098500626e-16 | 1.0450909090911509e-2 | 2.8819330578512402e-2 | 3.1036198347107306e-2 | 2.6285619834711064e-2 | 5.946592608595026e-4 | 1.9721522630525295e-31 | 1.0922150082649681e-4 | 1.183801075178814e-3 | 1.7762370850488212e-3 | 1.298522287603321e-3 | 3.4406410379154845e-2 | 4.214542780716339e-2 | 3.603501474404196e-2 |
| user_002 | Sitting | 1.44630938937e12 | -0.650847 | 3.2195 | 9.15796 | -0.605559 | 3.1881472727272726 | 9.164927272727274 | 0.42360181740899994 | 10.36518025 | 83.86823136159998 | 9.72918359519487 | 9.722676512480279 | 4.5287999999999995e-2 | 3.135272727272742e-2 | 6.967272727274931e-3 | 3.0402834710743808e-2 | 3.23028099173553e-2 | 2.4385454545455234e-2 | 2.0510029439999994e-3 | 9.829935074380258e-4 | 4.854288925622906e-5 | 1.2533105034477844e-3 | 1.8512492766340983e-3 | 1.249979398347126e-3 | 3.5402125691090705e-2 | 4.3026146430212624e-2 | 3.535504770675788e-2 |
| user_002 | Sitting | 1.446309389629e12 | -0.612526 | 3.2195 | 9.19628 | -0.6125262727272726 | 3.2020827272727272 | 9.178861818181819 | 0.375188100676 | 10.36518025 | 84.57156583839999 | 9.762783116974175 | 9.740828570831246 | 2.727272725433494e-7 | 1.741727272727278e-2 | 1.741818181818111e-2 | 2.565241322314048e-2 | 3.7053966942148756e-2 | 1.995173553719034e-2 | 7.438016518893438e-14 | 3.0336138925620023e-4 | 3.0339305785121503e-4 | 1.1727739554079622e-3 | 2.5872794606310967e-3 | 5.262490494365031e-4 | 3.424578741112492e-2 | 5.086530704351539e-2 | 2.294011877555352e-2 |
| user_002 | Sitting | 1.446309390018e12 | -0.574206 | 3.25783 | 9.15796 | -0.5985916363636364 | 3.2195027272727272 | 9.171894545454547 | 0.329712530436 | 10.613456308899998 | 83.86823136159998 | 9.73711457265118 | 9.739064553708165 | 2.4385636363636443e-2 | 3.832727272727254e-2 | 1.3934545454548086e-2 | 2.6285809917355386e-2 | 3.388867768595041e-2 | 2.058512396694293e-2 | 5.94659260859508e-4 | 1.4689798347107296e-3 | 1.9417155702486672e-4 | 1.1484991710375648e-3 | 1.78206255048835e-3 | 5.615529688955879e-4 | 3.388951417529565e-2 | 4.2214482710183125e-2 | 2.3697108872087917e-2 |
| user_002 | Sitting | 1.446309391119e12 | -0.574206 | 3.10454 | 9.15796 | -0.5567876363636364 | 3.1846645454545457 | 9.144025454545456 | 0.329712530436 | 9.638168611600001 | 83.86823136159998 | 9.686904175413112 | 9.698858991586967 | 1.741836363636362e-2 | 8.012454545454561e-2 | 1.3934545454542757e-2 | 3.293638842975204e-2 | 2.2486859504132376e-2 | 1.5201322314048905e-2 | 3.033993917685945e-4 | 6.419942784297547e-3 | 1.9417155702471822e-4 | 1.478373393021036e-3 | 1.071374233433505e-3 | 3.6848465935387405e-4 | 3.844962149385916e-2 | 3.273185349828978e-2 | 1.9195954244420204e-2 |
| user_002 | Sitting | 1.446309392026e12 | -0.535886 | 3.2195 | 9.11964 | -0.5533039999999999 | 3.1846645454545457 | 9.144025454545456 | 0.287173804996 | 10.36518025 | 83.1678337296 | 9.686082168998775 | 9.698729922258241 | 1.7417999999999934e-2 | 3.4835454545454336e-2 | 2.4385454545456042e-2 | 3.515319008264461e-2 | 3.1037024793388455e-2 | 4.3070413223140716e-2 | 3.033867239999977e-4 | 1.2135088933884152e-3 | 5.946503933885027e-4 | 1.567734366244927e-3 | 1.279874187678435e-3 | 2.2837222900075043e-3 | 3.959462547170925e-2 | 3.577532931614236e-2 | 4.77883070427014e-2 |
| user_002 | Sitting | 1.44630939248e12 | -0.612526 | 3.18118 | 9.15796 | -0.5672386363636364 | 3.163761818181818 | 9.14054181818182 | 0.375188100676 | 10.1199061924 | 83.86823136159998 | 9.714078734222612 | 9.689318632790359 | 4.528736363636365e-2 | 1.7418181818182e-2 | 1.7418181818179335e-2 | 3.103615702479341e-2 | 2.1218677685950326e-2 | 3.0402644628098455e-2 | 2.050945305132233e-3 | 3.03393057851246e-4 | 3.033930578511532e-4 | 1.2621281915815193e-3 | 5.747948180315491e-4 | 1.5489594662659756e-3 | 3.552644355380256e-2 | 2.397487889503405e-2 | 3.9356822359865075e-2 |
| user_002 | Sitting | 1.446309392932e12 | -0.497565 | 3.14286 | 9.15796 | -0.5672386363636364 | 3.1672463636363632 | 9.133574545454547 | 0.24757092922499999 | 9.877568979600001 | 83.86823136159998 | 9.695017858179787 | 9.68388161177369 | 6.967363636363638e-2 | 2.438636363636304e-2 | 2.438545454545249e-2 | 2.3752181818181853e-2 | 2.8186694214875872e-2 | 3.325289256198293e-2 | 4.854415604132234e-3 | 5.946947314049296e-4 | 5.946503933883295e-4 | 9.89626005675433e-4 | 1.2677851063110273e-3 | 1.459596420135237e-3 | 3.1458321723757496e-2 | 3.5605970093665854e-2 | 3.8204664900182504e-2 |
| user_002 | Sitting | 1.446309393711e12 | -0.535886 | 3.14286 | 9.15796 | -0.556787909090909 | 3.1742136363636364 | 9.137058181818183 | 0.287173804996 | 9.877568979600001 | 83.86823136159998 | 9.697060077476884 | 9.688865473566398 | 2.0901909090909054e-2 | 3.1353636363636195e-2 | 2.0901818181815912e-2 | 2.7552495867768565e-2 | 3.452090909090896e-2 | 2.8502479338842302e-2 | 4.368898036446266e-4 | 9.830505132231299e-4 | 4.3688600330569024e-4 | 1.119810475468068e-3 | 1.5546162080390586e-3 | 1.0547045938392187e-3 | 3.3463569377280546e-2 | 3.9428621685763485e-2 | 3.2476215817721414e-2 |
| user_002 | Sitting | 1.446309393905e12 | -0.574206 | 3.18118 | 9.11964 | -0.5637551818181818 | 3.1776972727272725 | 9.13009090909091 | 0.329712530436 | 10.1199061924 | 83.1678337296 | 9.67561121854511 | 9.683824991940147 | 1.0450818181818144e-2 | 3.4827272727273595e-3 | 1.0450909090909732e-2 | 2.3752148760330553e-2 | 3.198719008264464e-2 | 2.2485289256198113e-2 | 1.0921960066942071e-4 | 1.2129389256198951e-5 | 1.092215008264597e-4 | 9.477042135897816e-4 | 1.4509074897821168e-3 | 6.983556567994071e-4 | 3.07848049139471e-2 | 3.8090779590107066e-2 | 2.6426419674246585e-2 |
| user_002 | Sitting | 1.446309394877e12 | -0.574206 | 3.18118 | 9.19628 | -0.5811733636363636 | 3.1881472727272726 | 9.157960000000001 | 0.329712530436 | 10.1199061924 | 84.57156583839999 | 9.747881029292264 | 9.714549440349064 | 6.967363636363633e-3 | 6.967272727272711e-3 | 3.83199999999988e-2 | 2.628568595041322e-2 | 1.741818181818192e-2 | 3.2302809917355096e-2 | 4.854415604132226e-5 | 4.854288925619812e-5 | 1.468422399999908e-3 | 1.1705577291855754e-3 | 5.174230695717485e-4 | 1.2047462515401587e-3 | 3.4213414462540497e-2 | 2.2746935388569346e-2 | 3.470945478598243e-2 |
| user_002 | Sitting | 1.446309396108e12 | -0.574206 | 3.2195 | 9.15796 | -0.5707222727272727 | 3.1951154545454545 | 9.130090909090912 | 0.329712530436 | 10.36518025 | 83.86823136159998 | 9.724357261127132 | 9.689957950948695 | 3.4837272727272772e-3 | 2.4384545454545492e-2 | 2.786909090908729e-2 | 2.47022479338843e-2 | 2.5653553719008248e-2 | 3.1352727272726776e-2 | 1.2136355710743833e-5 | 5.946060570247952e-4 | 7.766862280989719e-4 | 1.059130078879039e-3 | 8.617503026296023e-4 | 1.2985222876033054e-3 | 3.254427874264598e-2 | 2.935558384072104e-2 | 3.6035014744041735e-2 |
| user_002 | Sitting | 1.44630939669e12 | -0.574206 | 3.2195 | 9.11964 | -0.5637550909090909 | 3.2020827272727272 | 9.130090909090912 | 0.329712530436 | 10.36518025 | 83.1678337296 | 9.688277788649332 | 9.69178806671138 | 1.0450909090909066e-2 | 1.741727272727278e-2 | 1.0450909090911509e-2 | 1.8368264462809904e-2 | 1.9952644628099234e-2 | 1.900165289256186e-2 | 1.0922150082644576e-4 | 3.0336138925620023e-4 | 1.0922150082649681e-4 | 6.023727802885044e-4 | 5.52808184748308e-4 | 5.758951861757681e-4 | 2.4543283812246974e-2 | 2.3511873271781388e-2 | 2.3997816279315253e-2 |
| user_002 | Sitting | 1.446309397792e12 | -0.650847 | 3.18118 | 9.11964 | -0.5811733636363636 | 3.181180909090909 | 9.137058181818183 | 0.42360181740899994 | 10.1199061924 | 83.1678337296 | 9.680461855686897 | 9.692544633867705 | 6.967363636363633e-2 | 9.0909090921798e-7 | 1.7418181818182887e-2 | 2.0901900826446305e-2 | 2.2486611570248005e-2 | 2.8185785123966575e-2 | 4.854415604132226e-3 | 8.264462812227735e-13 | 3.0339305785127694e-4 | 9.620433214876044e-4 | 1.0404994267468033e-3 | 1.2786638329075584e-3 | 3.1016823201088863e-2 | 3.225677334679963e-2 | 3.575840926142491e-2 |
| user_002 | Sitting | 1.446309397857e12 | -0.574206 | 3.18118 | 9.11964 | -0.5776897272727273 | 3.181180909090909 | 9.14054181818182 | 0.329712530436 | 10.1199061924 | 83.1678337296 | 9.67561121854511 | 9.695612912433567 | 3.4837272727272772e-3 | 9.0909090921798e-7 | 2.0901818181819465e-2 | 1.741827272727274e-2 | 2.1536528925619927e-2 | 2.6602314049586472e-2 | 1.2136355710743833e-5 | 8.264462812227735e-13 | 4.368860033058388e-4 | 8.042789889864763e-4 | 1.0305684719759548e-3 | 1.184887796844449e-3 | 2.835981292227571e-2 | 3.210246831594036e-2 | 3.4422199186636075e-2 |
| user_002 | Sitting | 1.446309398115e12 | -0.612526 | 3.10454 | 9.11964 | -0.584657 | 3.1776972727272734 | 9.133574545454548 | 0.375188100676 | 9.638168611600001 | 83.1678337296 | 9.653040476548103 | 9.688387501624 | 2.7869000000000033e-2 | 7.315727272727335e-2 | 1.3934545454548086e-2 | 3.008621487603306e-2 | 2.407008264462823e-2 | 2.4385454545455234e-2 | 7.766811610000018e-4 | 5.351986552892653e-3 | 1.9417155702486672e-4 | 1.797227750449286e-3 | 1.3549278384673222e-3 | 8.550167993989645e-4 | 4.239372300764921e-2 | 3.680934444495476e-2 | 2.924067029667693e-2 |
| user_002 | Sitting | 1.446309398569e12 | -0.612526 | 3.2195 | 9.11964 | -0.5951078181818181 | 3.1846636363636365 | 9.109188181818183 | 0.375188100676 | 10.36518025 | 83.1678337296 | 9.690624442226413 | 9.668323338838917 | 1.7418181818181888e-2 | 3.4836363636363554e-2 | 1.0451818181817174e-2 | 2.786923966942151e-2 | 3.1036363636363692e-2 | 2.945347107438097e-2 | 3.033930578512421e-4 | 1.213572231404953e-3 | 1.0924050330576405e-4 | 1.326128349746056e-3 | 1.646059640571004e-3 | 1.2214061022539973e-3 | 3.6416045223857794e-2 | 4.057166055969368e-2 | 3.494862089201801e-2 |
| user_002 | Sitting | 1.446309399282e12 | -0.497565 | 3.2195 | 9.11964 | -0.563755 | 3.198599090909091 | 9.119639090909093 | 0.24757092922499999 | 10.36518025 | 83.1678337296 | 9.684037634624568 | 9.680833551221772 | 6.619000000000003e-2 | 2.0900909090908915e-2 | 9.090909074416231e-7 | 2.5969000000000016e-2 | 1.7419008264462865e-2 | 2.565413223140357e-2 | 4.381116100000004e-3 | 4.368480008264389e-4 | 8.264462779930337e-13 | 1.1915174160338085e-3 | 5.52832369797143e-4 | 1.0603619127722878e-3 | 3.451836346111745e-2 | 2.3512387581807657e-2 | 3.256319874908311e-2 |
| user_002 | Sitting | 1.446309399346e12 | -0.574206 | 3.18118 | 9.08132 | -0.563755 | 3.198599090909091 | 9.123123636363637 | 0.329712530436 | 10.1199061924 | 82.47037294239999 | 9.639501629505334 | 9.684115539772451 | 1.0450999999999988e-2 | 1.7419090909091217e-2 | 4.180363636363715e-2 | 2.501892561983473e-2 | 1.8685867768595103e-2 | 2.4069917355370472e-2 | 1.0922340099999975e-4 | 3.034247280991843e-4 | 1.7475440132232066e-3 | 1.1617299067332827e-3 | 5.793131885048824e-4 | 9.003029230652933e-4 | 3.40841591759762e-2 | 2.4068925786268118e-2 | 3.000504829300052e-2 |
| user_002 | Sitting | 1.446309399411e12 | -0.574206 | 3.2195 | 9.19628 | -0.5672386363636365 | 3.2020827272727272 | 9.13009090909091 | 0.329712530436 | 10.36518025 | 84.57156583839999 | 9.760453812135786 | 9.692028365975302 | 6.967363636363522e-3 | 1.741727272727278e-2 | 6.618909090908964e-2 | 2.1218611570247944e-2 | 1.995256198347112e-2 | 2.8820165289254716e-2 | 4.8544156041320715e-5 | 3.0336138925620023e-4 | 4.380995755371733e-3 | 9.49907311271224e-4 | 6.057882491359869e-4 | 1.2809186979713656e-3 | 3.0820566368436903e-2 | 2.4612765978979018e-2 | 3.578992453151258e-2 |
| user_002 | Sitting | 1.446309399929e12 | -0.574206 | 3.14286 | 9.08132 | -0.5672386363636365 | 3.195115454545454 | 9.109189090909092 | 0.329712530436 | 9.877568979600001 | 82.47037294239999 | 9.626923415735476 | 9.670031425217148 | 6.967363636363522e-3 | 5.225545454545388e-2 | 2.786909090909262e-2 | 1.3301322314049503e-2 | 2.7553388429752038e-2 | 2.94526446280997e-2 | 4.8544156041320715e-5 | 2.730632529751997e-3 | 7.766862280992689e-4 | 4.5896321775356784e-4 | 1.0139892420736236e-3 | 1.2610118732532096e-3 | 2.142342684431153e-2 | 3.18431977363082e-2 | 3.5510728987915886e-2 |
| user_002 | Sitting | 1.446309400059e12 | -0.574206 | 3.18118 | 9.11964 | -0.5742060000000001 | 3.195115454545454 | 9.116156363636366 | 0.329712530436 | 10.1199061924 | 83.1678337296 | 9.67561121854511 | 9.676979535721001 | 1.1102230246251565e-16 | 1.3935454545454196e-2 | 3.483636363634801e-3 | 6.3339586776858855e-3 | 2.7553388429752e-2 | 3.0085950413223535e-2 | 1.232595164407831e-32 | 1.9419689338842001e-4 | 1.21357223140387e-5 | 5.075053584447656e-5 | 1.0184010803906773e-3 | 1.2896963077385455e-3 | 7.123941033197605e-3 | 3.191239697031042e-2 | 3.5912341997404534e-2 |
| user_002 | Standing | 1.446034733761e12 | -1.45557 | -9.4262 | -5.99e-4 | -1.46515 | -9.40704 | 0.1718425 | 2.1186840249000003 | 88.85324643999999 | 3.5880100000000004e-7 | 9.537920676106559 | 9.522599287107857 | 9.579999999999922e-3 | 1.91599999999994e-2 | 0.1724415 | 1.0378333333333323e-2 | 4.470666666666645e-2 | 5.7480375e-2 | 9.177639999999851e-5 | 3.67105599999977e-4 | 2.973607092225e-2 | 1.5551001111111117e-4 | 5.119083644444456e-3 | 7.8928997305625e-3 | 1.2470365315864294e-2 | 7.154777176435655e-2 | 8.884199305825202e-2 |
| user_002 | Standing | 1.446034734278e12 | -1.49389 | -9.38788 | 0.191003 | -1.4625372727272727 | -9.41574909090909 | 0.15616609090909092 | 2.2317073320999996 | 88.13229089439999 | 3.6482146009e-2 | 9.507916720949389 | 9.530694554162094 | 3.13527272727272e-2 | 2.7869090909090843e-2 | 3.4836909090909085e-2 | 4.5657754689754686e-2 | 4.277432926669323e-2 | 7.490013344155845e-2 | 9.82993507438012e-4 | 7.766862280991699e-4 | 1.213610235008264e-3 | 3.2685541165727136e-3 | 3.033036308729061e-3 | 8.322194092951866e-3 | 5.717127002763463e-2 | 5.507300889482125e-2 | 9.122606038272105e-2 |
| user_002 | Standing | 1.44603473512e12 | -1.34061 | -9.38788 | 0.344284 | -1.4381518181818183 | -9.401814545454544 | 0.13178045454545456 | 1.7972351721000002 | 88.13229089439999 | 0.11853147265599999 | 9.489365497184519 | 9.512845362386004 | 9.754181818181817e-2 | 1.3934545454544534e-2 | 0.21250354545454542 | 4.750413223140511e-2 | 3.325289256198293e-2 | 8.012428099173553e-2 | 9.514406294214874e-3 | 1.941715570247677e-4 | 4.515775683075205e-2 | 3.3494593586777003e-3 | 1.41546652081137e-3 | 1.0916814534482344e-2 | 5.787451389582206e-2 | 3.7622686251932755e-2 | 0.1044835610729379 |
| user_002 | Standing | 1.446034735768e12 | -1.41725 | -9.38788 | 0.114362 | -1.3963481818181818 | -9.408781818181815 | 0.1631333636363637 | 2.0085975625 | 88.13229089439999 | 1.3078667044000002e-2 | 9.494944292829947 | 9.514008835017863 | 2.0901818181818133e-2 | 2.0901818181815912e-2 | 4.877136363636368e-2 | 3.451966942148756e-2 | 3.483636363636384e-2 | 9.437555371900828e-2 | 4.3688600330578305e-4 | 4.3688600330569024e-4 | 2.3786459109504175e-3 | 2.1303708898572473e-3 | 1.804912882344121e-3 | 1.3693708602697223e-2 | 4.6155941002835675e-2 | 4.248426629170052e-2 | 0.11702012050368613 |
| user_002 | Standing | 1.446034736479e12 | -1.34061 | -9.46452 | 3.7722e-2 | -1.3754463636363636 | -9.422716363636363 | 0.1666170909090909 | 1.7972351721000002 | 89.5771388304 | 1.422949284e-3 | 9.559068832882417 | 9.524670474321272 | 3.4836363636363554e-2 | 4.180363636363715e-2 | 0.1288950909090909 | 6.27054545454545e-2 | 3.420297520661189e-2 | 4.085400826446281e-2 | 1.213572231404953e-3 | 1.7475440132232066e-3 | 1.661394446046281e-2 | 8.300834062809898e-3 | 1.7232725685950516e-3 | 2.6687947015815176e-3 | 9.110891319080641e-2 | 4.1512318275363176e-2 | 5.166037844984798e-2 |
| user_002 | Standing | 1.446034737063e12 | -1.37893 | -9.4262 | 0.191003 | -1.37893 | -9.4262 | 0.16313336363636363 | 1.9014479449 | 88.85324643999999 | 3.6482146009e-2 | 9.528440403912331 | 9.528531738553587 | 0.0 | 0.0 | 2.7869636363636374e-2 | 5.732165289256191e-2 | 2.913586776859586e-2 | 4.750463636363636e-2 | 0.0 | 0.0 | 7.767166310413229e-4 | 6.5786647416979545e-3 | 1.5268945166040599e-3 | 3.506169387605559e-3 | 8.110896831854018e-2 | 3.9075497650113936e-2 | 5.921291571613037e-2 |
| user_002 | Standing | 1.446034737451e12 | -1.37893 | -9.46452 | 0.191003 | -1.3824136363636361 | -9.422716363636363 | 0.18403536363636366 | 1.9014479449 | 89.5771388304 | 3.6482146009e-2 | 9.56635086756225 | 9.525611176471477 | 3.4836363636361334e-3 | 4.180363636363715e-2 | 6.9676363636363425e-3 | 4.623735537190076e-2 | 3.166942148760428e-2 | 2.7236190082644628e-2 | 1.2135722314047982e-5 | 1.7475440132232066e-3 | 4.8547956495867476e-5 | 3.252373580165281e-3 | 1.456286677685994e-3 | 1.25110982419459e-3 | 5.7029585130573074e-2 | 3.816132437017869e-2 | 3.5371030861350225e-2 |
| user_002 | Standing | 1.446034737969e12 | -1.37893 | -9.46452 | 0.305964 | -1.3649954545454546 | -9.40529818181818 | 0.19796990909090909 | 1.9014479449 | 89.5771388304 | 9.361396929600001e-2 | 9.569336484030437 | 9.506087003781976 | 1.3934545454545422e-2 | 5.922181818182004e-2 | 0.10799409090909093 | 2.4068760330578517e-2 | 3.483636363636335e-2 | 3.863722314049587e-2 | 1.9417155702479248e-4 | 3.507223748760551e-3 | 1.1662723671280996e-2 | 1.383472343801649e-3 | 1.4761451323816782e-3 | 2.290394691998498e-3 | 3.7195058056167206e-2 | 3.84206342006698e-2 | 4.785806820169926e-2 |
| user_002 | Standing | 1.446034738293e12 | -1.37893 | -9.38788 | 0.229323 | -1.3545445454545457 | -9.40529818181818 | 0.2014535454545455 | 1.9014479449 | 88.13229089439999 | 5.2589038329e-2 | 9.491381768616675 | 9.50469796488644 | 2.4385454545454266e-2 | 1.741818181818111e-2 | 2.7869454545454503e-2 | 2.850247933884299e-2 | 2.7869090909090843e-2 | 4.4654388429752064e-2 | 5.946503933884161e-4 | 3.0339305785121503e-4 | 7.767064966611546e-4 | 1.7034141138993234e-3 | 1.1032474830954312e-3 | 2.5540784987310283e-3 | 4.127243770241011e-2 | 3.321516947262849e-2 | 5.0537891712367944e-2 |
| user_002 | Standing | 1.446034738941e12 | -1.37893 | -9.4262 | 0.229323 | -1.368479090909091 | -9.401814545454544 | 0.22235563636363634 | 1.9014479449 | 88.85324643999999 | 5.2589038329e-2 | 9.529285567304036 | 9.503629742042433 | 1.0450909090909066e-2 | 2.4385454545456042e-2 | 6.96736363636366e-3 | 3.4519669421487584e-2 | 3.1036033057851044e-2 | 2.4385677685950416e-2 | 1.0922150082644576e-4 | 5.946503933885027e-4 | 4.8544156041322646e-5 | 1.8920694335086369e-3 | 1.6195673051840817e-3 | 7.424993755995493e-4 | 4.349792447357272e-2 | 4.0243848041459473e-2 | 2.7248841729503828e-2 |
| user_002 | Standing | 1.446034740236e12 | -1.45557 | -9.38788 | 0.229323 | -1.37893 | -9.422716363636363 | 0.22583945454545454 | 2.1186840249000003 | 88.13229089439999 | 5.2589038329e-2 | 9.502818737491998 | 9.526054521087183 | 7.664000000000004e-2 | 3.4836363636364e-2 | 3.4835454545454614e-3 | 3.8953388429751996e-2 | 3.0402644628099426e-2 | 4.592111570247934e-2 | 5.873689600000006e-3 | 1.213572231404984e-3 | 1.2135088933884345e-5 | 2.088447485499624e-3 | 1.2532891407964408e-3 | 2.9600787547911333e-3 | 4.569953484992625e-2 | 3.5401823975558674e-2 | 5.4406605801052624e-2 |
| user_002 | Standing | 1.446034741725e12 | -1.41725 | -9.38788 | 0.191003 | -1.3963481818181818 | -9.419232727272725 | 0.2188720909090909 | 2.0085975625 | 88.13229089439999 | 3.6482146009e-2 | 9.496176630776672 | 9.524973651272083 | 2.0901818181818133e-2 | 3.1352727272725645e-2 | 2.78690909090909e-2 | 3.990347107438013e-2 | 2.3752066115702967e-2 | 4.212066942148761e-2 | 4.3688600330578305e-4 | 9.829935074379145e-4 | 7.76686228099173e-4 | 2.33667816919609e-3 | 8.395713346356199e-4 | 2.8342982346739294e-3 | 4.83391990955176e-2 | 2.8975357368557508e-2 | 5.3238127640572876e-2 |
| user_002 | Standing | 1.446034742407e12 | -1.41725 | -9.34956 | 7.6042e-2 | -1.361511818181818 | -9.363494545454543 | 0.18403545454545453 | 2.0085975625 | 87.41427219360001 | 5.782385764e-3 | 9.456672360924006 | 9.464532622864683 | 5.573818181818191e-2 | 1.3934545454542757e-2 | 0.10799345454545453 | 7.220628099173551e-2 | 6.302214876033024e-2 | 7.125661157024793e-2 | 3.1067449123967045e-3 | 1.9417155702471822e-4 | 1.1662586224661153e-2 | 7.835263624943653e-3 | 5.865966867618297e-3 | 7.194372536848986e-3 | 8.85170244921487e-2 | 7.658960025759566e-2 | 8.481964711580087e-2 |
| user_002 | Standing | 1.446034742488e12 | -1.37893 | -9.38788 | 0.152682 | -1.330159090909091 | -9.440134545454544 | 0.19448627272727273 | 1.9014479449 | 88.13229089439999 | 2.3311793124000002e-2 | 9.489839336491634 | 9.536282590827007 | 4.8770909090908976e-2 | 5.225454545454511e-2 | 4.180427272727272e-2 | 7.790677685950409e-2 | 8.044033057851264e-2 | 6.33395123966942e-2 | 2.3786015735537077e-3 | 2.730537520661121e-3 | 1.7475972182561974e-3 | 9.165780089556715e-3 | 1.159733754229913e-2 | 7.4580968808392175e-3 | 9.57380806657242e-2 | 0.10769093528379782 | 8.636027374226657e-2 |
| user_002 | Standing | 1.446034742608e12 | -1.30229 | -9.31124 | 0.305964 | -1.6750418181818179 | -8.290524545454545 | 0.27809427272727266 | 1.6959592440999998 | 86.6991903376 | 9.361396929600001e-2 | 9.406846631629326 | 9.239319575440327 | 0.3727518181818179 | 1.0207154545454546 | 2.786972727272735e-2 | 0.3971372727272726 | 1.2180159504132235 | 9.500909917355373e-2 | 0.13894391795785102 | 1.041860039147934 | 7.767216982562027e-4 | 1.0335963619219386 | 12.500306028839441 | 1.8598867660080395e-2 | 1.0166594129411965 | 3.5355771846813697 | 0.13637766554711367 |
| user_002 | Standing | 1.446034742649e12 | -1.18733 | -9.46452 | 3.7722e-2 | -1.6576236363636363 | -8.26962272727273 | 0.2432577272727273 | 1.4097525289 | 89.5771388304 | 1.422949284e-3 | 9.538779497848978 | 9.215532139109781 | 0.4702936363636363 | 1.194897272727271 | 0.2055357272727273 | 0.5472515702479337 | 1.7076297520661157 | 0.12129477685950416 | 0.22117610440413218 | 1.4277794923710705 | 4.224493518552893e-2 | 1.1067117965771598 | 13.112466736772495 | 2.4742881798826447e-2 | 1.052003705590983 | 3.6211140187478903 | 0.15729870247025704 |
| user_002 | Standing | 1.446034742673e12 | -1.37893 | -9.27292 | 0.229323 | -1.6506563636363638 | -8.245237272727271 | 0.2397741818181818 | 1.9014479449 | 85.98704532639998 | 5.2589038329e-2 | 9.377690670395829 | 9.190260620809141 | 0.2717263636363638 | 1.0276827272727278 | 1.0451181818181804e-2 | 0.59729 | 1.986640082644628 | 0.11306066115702479 | 7.383521669504142e-2 | 1.0561317879347119 | 1.0922720139669392e-4 | 1.1242780968574755 | 13.443876168523282 | 2.3032821136954172e-2 | 1.0603198087640706 | 3.666589173676713 | 0.15176567838926616 |
| user_002 | Standing | 1.446034742722e12 | -1.45557 | -9.27292 | 0.152682 | -1.3545445454545453 | -9.41574909090909 | 0.19797018181818185 | 2.1186840249000003 | 85.98704532639998 | 2.3311793124000002e-2 | 9.387706916197585 | 9.515513032021708 | 0.10102545454545475 | 0.1428290909090908 | 4.528818181818184e-2 | 0.20458595041322322 | 0.6919823140495869 | 6.903980991735535e-2 | 1.0206142466115745e-2 | 2.0400149209917322e-2 | 2.051019412396696e-3 | 6.655770397761085e-2 | 0.7849239226030054 | 9.76386200915327e-3 | 0.25798779811768396 | 0.8859593233343196 | 9.88122563711267e-2 |
| user_002 | Standing | 1.446034742786e12 | -1.41725 | -9.54116 | 0.344284 | -1.4033154545454543 | -9.360010909090908 | 0.1770680909090909 | 2.0085975625 | 91.0337341456 | 0.11853147265599999 | 9.651987524896414 | 9.467182792168602 | 1.3934545454545644e-2 | 0.18114909090909137 | 0.16721590909090908 | 5.3521322314049644e-2 | 7.537322314049581e-2 | 9.595921487603305e-2 | 1.9417155702479866e-4 | 3.281499313719025e-2 | 2.796116025309917e-2 | 4.667840100976716e-3 | 9.441591960330594e-3 | 1.3405794349181821e-2 | 6.832159322627596e-2 | 9.716785456276471e-2 | 0.11578339409942093 |
| user_002 | Standing | 1.446034742868e12 | -1.30229 | -9.4262 | 0.267643 | -1.30229 | -9.461036363636362 | 0.21190472727272727 | 1.6959592440999998 | 88.85324643999999 | 7.163277544900001e-2 | 9.519497805007836 | 9.55316189549893 | 0.0 | 3.483636363636222e-2 | 5.573827272727275e-2 | 7.283966942148755e-2 | 8.455735537190069e-2 | 6.33393388429752e-2 | 0.0 | 1.2135722314048601e-3 | 3.106755046619837e-3 | 8.856870794289986e-3 | 1.0267924325168999e-2 | 5.675195743163036e-3 | 9.411094938576481e-2 | 0.1013307669228305 | 7.533389504839795e-2 |
| user_002 | Standing | 1.446034742989e12 | -1.34061 | -9.34956 | 0.229323 | -1.2326170909090908 | -9.422716363636363 | 0.17358436363636362 | 1.7972351721000002 | 87.41427219360001 | 5.2589038329e-2 | 9.447967845205074 | 9.506061671875052 | 0.1079929090909093 | 7.31563636363628e-2 | 5.573863636363638e-2 | 0.1326951157024794 | 6.428892561983429e-2 | 7.379039669421486e-2 | 1.1662468413917403e-2 | 5.351853540495745e-3 | 3.1067955836776876e-3 | 2.2827393172450052e-2 | 5.6673823206610735e-3 | 7.139237571364387e-3 | 0.15108736933460074 | 7.52820185745645e-2 | 8.449400908564102e-2 |
| user_002 | Standing | 1.446034743013e12 | -1.30229 | -9.27292 | 0.191003 | -1.2953226363636363 | -9.391363636363634 | 0.17358445454545454 | 1.6959592440999998 | 85.98704532639998 | 3.6482146009e-2 | 9.365868177404003 | 9.482930913178338 | 6.967363636363633e-3 | 0.11844363636363475 | 1.7418545454545464e-2 | 0.11084315702479342 | 5.9855206611569886e-2 | 6.1122528925619826e-2 | 4.854415604132226e-5 | 1.402889499504094e-2 | 3.0340572575206643e-4 | 1.5827245627852756e-2 | 4.978955891209522e-3 | 6.21910965048084e-3 | 0.1258063815068725 | 7.056171689527914e-2 | 7.886133178231801e-2 |
| user_002 | Standing | 1.446034743062e12 | -1.34061 | -9.69444 | 7.6042e-2 | -1.3127409090909092 | -9.468003636363637 | 0.14919881818181818 | 1.7972351721000002 | 93.9821669136 | 5.782385764e-3 | 9.78699057276873 | 9.56029440563364 | 2.7869090909090843e-2 | 0.22643636363636332 | 7.315681818181818e-2 | 7.030617355371901e-2 | 0.10577586776859524 | 5.922237190082646e-2 | 7.766862280991699e-4 | 5.1273426776859365e-2 | 5.351920046487603e-3 | 7.744808789910596e-3 | 1.5544757036814388e-2 | 5.9786087816852e-3 | 8.800459527723876e-2 | 0.12467861499396915 | 7.732146391323175e-2 |
| user_002 | Standing | 1.446034743119e12 | -1.34061 | -9.50284 | 0.305964 | -1.3406099999999999 | -9.44710181818182 | 0.22235572727272726 | 1.7972351721000002 | 90.30396806560002 | 9.361396929600001e-2 | 9.60181322495892 | 9.545211176307818 | 2.220446049250313e-16 | 5.573818181818169e-2 | 8.360827272727275e-2 | 7.885685950413222e-2 | 0.1285778512396694 | 7.347359504132234e-2 | 4.930380657631324e-32 | 3.1067449123966797e-3 | 6.9903432684380205e-3 | 9.559639441021782e-3 | 2.279750599068361e-2 | 6.198137410960935e-3 | 9.77734086601351e-2 | 0.15098842998946513 | 7.872825039946547e-2 |
| user_002 | Standing | 1.446034743127e12 | -1.30229 | -9.65612 | 0.114362 | -1.3440936363636362 | -9.461036363636364 | 0.20493736363636364 | 1.6959592440999998 | 93.24065345439999 | 1.3078667044000002e-2 | 9.744213224552508 | 9.559102111218815 | 4.1803636363636265e-2 | 0.1950836363636359 | 9.057536363636363e-2 | 7.632330578512397e-2 | 0.13902876033057848 | 6.935651239669423e-2 | 1.7475440132231322e-3 | 3.8057625176859324e-2 | 8.203896497859504e-3 | 9.277208085349355e-3 | 2.5673672179113358e-2 | 5.2658708311149535e-3 | 9.631826454701806e-2 | 0.16023006016073688 | 7.256632022581105e-2 |
| user_002 | Standing | 1.446034743142e12 | -1.18733 | -9.27292 | 0.114362 | -1.3440936363636362 | -9.443618181818183 | 0.20493727272727275 | 1.4097525289 | 85.98704532639998 | 1.3078667044000002e-2 | 9.349324923348423 | 9.541753629559041 | 0.15676363636363622 | 0.17069818181818341 | 9.057527272727274e-2 | 7.759008264462806e-2 | 0.14821289256198353 | 7.284019834710745e-2 | 2.4574837685950368e-2 | 2.9137869276033602e-2 | 8.203880029619836e-3 | 9.162470347107427e-3 | 2.6116074419834715e-2 | 5.6045778851179584e-3 | 9.572079370287015e-2 | 0.16160468563700348 | 7.486372876846276e-2 |
| user_002 | Standing | 1.446034743167e12 | -1.18733 | -9.31124 | 0.535886 | -1.3162245454545451 | -9.384396363636363 | 0.23629027272727277 | 1.4097525289 | 86.6991903376 | 0.287173804996 | 9.401920903278011 | 9.48030257103568 | 0.12889454545454515 | 7.31563636363628e-2 | 0.2995957272727272 | 7.442314049586768e-2 | 0.13269487603305777 | 8.804171900826446e-2 | 1.6613803847933806e-2 | 5.351853540495745e-3 | 8.975759980007435e-2 | 7.977582550262945e-3 | 2.1754937119158522e-2 | 1.2731709043015776e-2 | 8.931731383255402e-2 | 0.14749554948932703 | 0.11283487511853671 |
| user_002 | Standing | 1.446034743224e12 | -1.26397 | -9.69444 | 0.191003 | -1.26397 | -9.384396363636363 | 0.18403545454545453 | 1.5976201609 | 93.9821669136 | 3.6482146009e-2 | 9.778357184134205 | 9.472280420387362 | 0.0 | 0.31004363636363763 | 6.967545454545476e-3 | 7.663999999999994e-2 | 0.12002710743801646 | 9.025855371900825e-2 | 0.0 | 9.612705644958756e-2 | 4.8546689661157324e-5 | 1.2069527465063866e-2 | 2.0082413934785937e-2 | 1.4370043589776855e-2 | 0.10986140116102591 | 0.14171243394559963 | 0.11987511664134828 |
| user_002 | Standing | 1.446034743402e12 | -1.30229 | -9.50284 | 0.191003 | -1.2500354545454544 | -9.380912727272728 | 0.21538845454545452 | 1.6959592440999998 | 90.30396806560002 | 3.6482146009e-2 | 9.593560832960252 | 9.467103454007024 | 5.225454545454555e-2 | 0.1219272727272731 | 2.4385454545454516e-2 | 7.410644628099176e-2 | 7.949024793388433e-2 | 8.075765289256197e-2 | 2.7305375206611673e-3 | 1.4866259834710837e-2 | 5.946503933884283e-4 | 8.232435713062368e-3 | 9.36326138903078e-3 | 8.233667366213372e-3 | 9.073277088826488e-2 | 9.676394674170116e-2 | 9.073955789077535e-2 |
| user_002 | Standing | 1.446034743467e12 | -1.11069 | -9.34956 | 0.267643 | -1.204748181818182 | -9.443618181818183 | 0.281578 | 1.2336322760999998 | 87.41427219360001 | 7.163277544900001e-2 | 9.419104906791782 | 9.524990489698748 | 9.405818181818204e-2 | 9.405818181818226e-2 | 1.3934999999999975e-2 | 5.5421487603305675e-2 | 7.473983471074339e-2 | 9.374233057851239e-2 | 8.84694156694219e-3 | 8.846941566942232e-3 | 1.9418422499999931e-4 | 3.59548354740795e-3 | 9.260659373102885e-3 | 1.225509052579865e-2 | 5.996235108305836e-2 | 9.623231979487393e-2 | 0.11070271236875205 |
| user_002 | Standing | 1.446034743499e12 | -1.41725 | -9.31124 | 0.344284 | -1.236100909090909 | -9.454069090909089 | 0.271127 | 2.0085975625 | 86.6991903376 | 0.11853147265599999 | 9.4247715819937 | 9.539520739618162 | 0.18114909090909093 | 0.14282909090908902 | 7.315699999999997e-2 | 9.12079338842974e-2 | 6.840595041322234e-2 | 9.595928099173552e-2 | 3.2814993137190086e-2 | 2.0400149209916816e-2 | 5.351946648999996e-3 | 1.1244298347708476e-2 | 8.139759930277884e-3 | 1.2311364151235911e-2 | 0.10603913592494271 | 9.022061809962224e-2 | 0.11095658678616566 |
| user_002 | Standing | 1.446034743531e12 | -1.22565 | -9.54116 | 0.267643 | -1.2813881818181818 | -9.440134545454548 | 0.23977390909090912 | 1.5022179224999999 | 91.0337341456 | 7.163277544900001e-2 | 9.623283475173585 | 9.530636047867155 | 5.573818181818191e-2 | 0.10102545454545186 | 2.78690909090909e-2 | 8.899107438016535e-2 | 5.8905123966941565e-2 | 7.379038842975204e-2 | 3.1067449123967045e-3 | 1.020614246611516e-2 | 7.76686228099173e-4 | 1.2260389279639376e-2 | 5.87589609496609e-3 | 7.918149123489853e-3 | 0.11072664214017951 | 7.665439383992342e-2 | 8.898398239846232e-2 |
| user_002 | Standing | 1.446034743548e12 | -1.26397 | -9.34956 | 0.344284 | -1.2988063636363636 | -9.433167272727273 | 0.25719227272727274 | 1.5976201609 | 87.41427219360001 | 0.11853147265599999 | 9.440891050486496 | 9.526412109411659 | 3.4836363636363554e-2 | 8.360727272727253e-2 | 8.709172727272724e-2 | 8.772429752066122e-2 | 6.397223140495824e-2 | 6.777309917355369e-2 | 1.213572231404953e-3 | 6.990176052892529e-3 | 7.584968959347101e-3 | 1.203312029812172e-2 | 6.44075880631095e-3 | 6.451895272498118e-3 | 0.10969558012117772 | 8.025433823981698e-2 | 8.032369060556242e-2 |
| user_002 | Standing | 1.446034743589e12 | -1.14901 | -9.46452 | 0.305964 | -1.253519090909091 | -9.468003636363637 | 0.2641596363636364 | 1.3202239801000002 | 89.5771388304 | 9.361396929600001e-2 | 9.538919057199092 | 9.554780962492048 | 0.10450909090909088 | 3.4836363636365775e-3 | 4.180436363636364e-2 | 6.238876033057861e-2 | 7.378975206611571e-2 | 5.383840495867767e-2 | 1.0922150082644622e-2 | 1.2135722314051077e-5 | 1.7476048190413227e-3 | 6.439655558827958e-3 | 9.127166427648326e-3 | 4.695493552251689e-3 | 8.024746450092961e-2 | 9.553620480031812e-2 | 6.852367147381763e-2 |
| user_002 | Standing | 1.446034743669e12 | -1.18733 | -9.4262 | 0.382604 | -1.22565 | -9.433167272727273 | 0.23977418181818183 | 1.4097525289 | 88.85324643999999 | 0.146385820816 | 9.508384972734117 | 9.516067499453998 | 3.831999999999991e-2 | 6.967272727273155e-3 | 0.14282981818181817 | 5.890512396694219e-2 | 5.985520661157053e-2 | 6.080573553719005e-2 | 1.468422399999993e-3 | 4.854288925620431e-5 | 2.0400356961851236e-2 | 6.0921326016528995e-3 | 5.59677448174311e-3 | 5.5957542085063846e-3 | 7.805211465202529e-2 | 7.481159323088307e-2 | 7.480477396868722e-2 |
| user_002 | Standing | 1.446034743685e12 | -1.18733 | -9.4262 | 0.152682 | -1.2500354545454544 | -9.433167272727271 | 0.22583945454545457 | 1.4097525289 | 88.85324643999999 | 2.3311793124000002e-2 | 9.50191090055174 | 9.519018404063804 | 6.27054545454544e-2 | 6.967272727271379e-3 | 7.315745454545455e-2 | 6.618909090909093e-2 | 5.383801652892586e-2 | 6.112245454545453e-2 | 3.931974029752048e-3 | 4.8542889256179554e-5 | 5.3520131555702495e-3 | 7.812095427798655e-3 | 5.114655331630422e-3 | 5.658646282371899e-3 | 8.838605901271226e-2 | 7.151681852285113e-2 | 7.522397411977048e-2 |
| user_002 | Standing | 1.446034743742e12 | -1.18733 | -9.50284 | 0.191003 | -1.2813881818181818 | -9.419232727272727 | 0.26415954545454545 | 1.4097525289 | 90.30396806560002 | 3.6482146009e-2 | 9.578632613296588 | 9.510274138582078 | 9.405818181818182e-2 | 8.36072727272743e-2 | 7.315654545454545e-2 | 5.542148760330579e-2 | 8.20238016528926e-2 | 5.3838338842975196e-2 | 8.846941566942148e-3 | 6.990176052892826e-3 | 5.351880142842974e-3 | 5.27021322674681e-3 | 1.3544569349962465e-2 | 4.91833946714425e-3 | 7.25962342463217e-2 | 0.11638113829123027 | 7.013087385128072e-2 |
| user_002 | Standing | 1.446034743944e12 | -1.37893 | -9.38788 | -5.99e-4 | -1.2221663636363638 | -9.391363636363636 | 0.18055181818181823 | 1.9014479449 | 88.13229089439999 | 3.5880100000000004e-7 | 9.488611025756141 | 9.472946131104779 | 0.15676363636363622 | 3.4836363636365775e-3 | 0.18115081818181822 | 7.727335537190078e-2 | 6.0171900826446585e-2 | 7.284036363636363e-2 | 2.4574837685950368e-2 | 1.2135722314051077e-5 | 3.2815618927942165e-2 | 8.172852230090151e-3 | 5.555954324868545e-3 | 7.718468353941397e-3 | 9.040382862517578e-2 | 7.453827422786595e-2 | 8.785481406241434e-2 |
| user_002 | Standing | 1.446034743977e12 | -1.14901 | -9.4262 | 0.344284 | -1.2500354545454546 | -9.391363636363634 | 0.18403545454545453 | 1.3202239801000002 | 88.85324643999999 | 0.11853147265599999 | 9.502210368790832 | 9.476779695648814 | 0.10102545454545453 | 3.4836363636365775e-2 | 0.16024854545454545 | 7.188958677685946e-2 | 6.492231404958657e-2 | 9.310895867768593e-2 | 1.02061424661157e-2 | 1.2135722314051078e-3 | 2.567959632029752e-2 | 6.651479939298266e-3 | 6.682370005108915e-3 | 1.0884841720114202e-2 | 8.155660573674131e-2 | 8.174576444751688e-2 | 0.10433044483809222 |
| user_002 | Standing | 1.446034744098e12 | -1.22565 | -9.46452 | 0.305964 | -1.180362727272727 | -9.440134545454544 | 0.23977409090909088 | 1.5022179224999999 | 89.5771388304 | 9.361396929600001e-2 | 9.548453839349907 | 9.51709107277025 | 4.528727272727284e-2 | 2.4385454545456042e-2 | 6.618990909090913e-2 | 5.953851239669416e-2 | 6.270545454545468e-2 | 6.2389462809917355e-2 | 2.050937071074391e-3 | 5.946503933885027e-4 | 4.381104065462815e-3 | 4.190133940796387e-3 | 5.386054212471849e-3 | 4.8775717667821185e-3 | 6.473124393055016e-2 | 7.33897418749504e-2 | 6.98396145950285e-2 |
| user_002 | Standing | 1.446034744179e12 | -1.11069 | -9.50284 | 0.229323 | -1.1942972727272727 | -9.4262 | 0.2676434545454545 | 1.2336322760999998 | 90.30396806560002 | 5.2589038329e-2 | 9.570276348153643 | 9.505986245872176 | 8.360727272727275e-2 | 7.664000000000115e-2 | 3.832045454545452e-2 | 7.85401652892562e-2 | 5.637157024793444e-2 | 5.605547107438018e-2 | 6.990176052892566e-3 | 5.873689600000177e-3 | 1.4684572365702459e-3 | 8.300834062809922e-3 | 4.691008298121736e-3 | 4.023622975179566e-3 | 9.110891319080654e-2 | 6.849093588294539e-2 | 6.34320342979757e-2 |
| user_002 | Standing | 1.446034744195e12 | -1.26397 | -9.38788 | 7.6042e-2 | -1.2117154545454545 | -9.429683636363636 | 0.2502250909090909 | 1.5976201609 | 88.13229089439999 | 5.782385764e-3 | 9.47289255935398 | 9.511304498180628 | 5.225454545454555e-2 | 4.180363636363715e-2 | 0.1741830909090909 | 7.949024793388429e-2 | 5.3838016528926017e-2 | 6.42895785123967e-2 | 2.7305375206611673e-3 | 1.7475440132232066e-3 | 3.0339749158644624e-2 | 8.403436078737796e-3 | 4.293839204207379e-3 | 6.256626801931633e-3 | 9.167025732885119e-2 | 6.552739277742843e-2 | 7.909884197592043e-2 |
| user_002 | Standing | 1.446034744219e12 | -1.26397 | -9.4262 | 0.191003 | -1.2012645454545454 | -9.415749090909088 | 0.23629036363636358 | 1.5976201609 | 88.85324643999999 | 3.6482146009e-2 | 9.512483836880302 | 9.495637494109273 | 6.270545454545462e-2 | 1.0450909090911509e-2 | 4.528736363636357e-2 | 6.460561983471073e-2 | 5.16211570247943e-2 | 6.0172413223140486e-2 | 3.931974029752075e-3 | 1.0922150082649681e-4 | 2.0509453051322252e-3 | 5.7677778416228434e-3 | 4.504559473478679e-3 | 5.913502439409467e-3 | 7.594588758861696e-2 | 6.711601502978763e-2 | 7.689930064317534e-2 |
| user_002 | Standing | 1.446034744228e12 | -1.57053 | -9.4262 | 0.267643 | -1.2465518181818183 | -9.422716363636361 | 0.23280663636363635 | 2.4665644809 | 88.85324643999999 | 7.163277544900001e-2 | 9.559887221947182 | 9.508733561792187 | 0.3239781818181817 | 3.483636363638354e-3 | 3.4836363636363665e-2 | 8.265719008264463e-2 | 4.75041322314061e-2 | 5.953897520661156e-2 | 0.10496186229421481 | 1.2135722314063454e-5 | 1.2135722314049607e-3 | 1.3879956584823444e-2 | 4.289426214275079e-3 | 5.864955404311043e-3 | 0.11781322754607584 | 6.549371125745646e-2 | 7.65829968355316e-2 |
| user_002 | Standing | 1.446034744503e12 | -1.26397 | -9.46452 | 0.152682 | -1.243068181818182 | -9.447101818181817 | 0.26764327272727273 | 1.5976201609 | 89.5771388304 | 2.3311793124000002e-2 | 9.54976810108099 | 9.532705592206456 | 2.0901818181818133e-2 | 1.7418181818182887e-2 | 0.11496127272727272 | 5.7004958677685895e-2 | 5.003768595041403e-2 | 5.890569421487604e-2 | 4.3688600330578305e-4 | 3.0339305785127694e-4 | 1.3216094227074378e-2 | 4.053331252892559e-3 | 3.6164452495868597e-3 | 4.927201550427498e-3 | 6.366577772157157e-2 | 6.013688759477713e-2 | 7.019402788291536e-2 |
| user_002 | Standing | 1.446034744527e12 | -1.30229 | -9.34956 | 0.191003 | -1.2570027272727275 | -9.419232727272728 | 0.22235572727272726 | 1.6959592440999998 | 87.41427219360001 | 3.6482146009e-2 | 9.441753734540475 | 9.505965099520607 | 4.52872727272724e-2 | 6.9672727272728e-2 | 3.1352727272727254e-2 | 4.782082644628092e-2 | 6.112198347107474e-2 | 8.044102479338842e-2 | 2.0509370710743505e-3 | 4.854288925619936e-3 | 9.829935074380154e-4 | 2.7812869048835388e-3 | 5.221670337490657e-3 | 9.563103558231404e-3 | 5.273790766501397e-2 | 7.226112604637888e-2 | 9.779112208289362e-2 |
| user_002 | Standing | 1.446034744568e12 | -1.26397 | -9.50284 | 0.344284 | -1.30229 | -9.429683636363634 | 0.2293229090909091 | 1.5976201609 | 90.30396806560002 | 0.11853147265599999 | 9.59271180111005 | 9.523035990951529 | 3.831999999999991e-2 | 7.315636363636635e-2 | 0.11496109090909087 | 5.668826446280986e-2 | 5.985520661157102e-2 | 0.10894367768595038 | 1.468422399999993e-3 | 5.351853540496265e-3 | 1.3216052423008256e-2 | 5.508514683095412e-3 | 5.303310651239775e-3 | 1.5701674011886546e-2 | 7.421936865195912e-2 | 7.282383298920604e-2 | 0.12530632071801703 |
| user_002 | Standing | 1.44603474464e12 | -1.53221 | -9.34956 | 0.114362 | -1.229133636363636 | -9.481938181818181 | 0.239774 | 2.3476674841 | 87.41427219360001 | 1.3078667044000002e-2 | 9.474967986475944 | 9.566116910347588 | 0.30307636363636403 | 0.13237818181818106 | 0.12541199999999997 | 0.12224396694214877 | 6.967272727272751e-2 | 0.11052728925619833 | 9.185528219504156e-2 | 1.7523983021487402e-2 | 1.572816974399999e-2 | 2.093301774425246e-2 | 7.592549178662661e-3 | 1.564765070072727e-2 | 0.14468247213899982 | 8.71352350009034e-2 | 0.12509056999121584 |
| user_002 | Standing | 1.446034744786e12 | -1.14901 | -9.38788 | 7.6042e-2 | -1.257002727272727 | -9.433167272727273 | 0.2537086363636364 | 1.3202239801000002 | 88.13229089439999 | 5.782385764e-3 | 9.458239649124142 | 9.520822783672006 | 0.10799272727272702 | 4.528727272727373e-2 | 0.1776666363636364 | 8.107371900826443e-2 | 7.315636363636457e-2 | 8.772486776859503e-2 | 1.1662429143801598e-2 | 2.050937071074471e-3 | 3.15654336767686e-2 | 8.854664299323807e-3 | 7.567174486551606e-3 | 1.0089337290975957e-2 | 9.409922581681428e-2 | 8.698950791073373e-2 | 0.10044569324254753 |
| user_002 | Standing | 1.446034744924e12 | -1.41725 | -9.19628 | 0.344284 | -1.2395845454545453 | -9.454069090909092 | 0.26415972727272724 | 2.0085975625 | 84.57156583839999 | 0.11853147265599999 | 9.311213394265861 | 9.54034444189835 | 0.17766545454545457 | 0.2577890909090925 | 8.012427272727274e-2 | 0.11179305785123973 | 9.342479338843016e-2 | 8.772514876033057e-2 | 3.156501373884298e-2 | 6.645521539173636e-2 | 6.419899080074382e-3 | 1.7350773166641645e-2 | 1.2785535081592858e-2 | 1.0413754814553715e-2 | 0.1317223335909353 | 0.11307314040740558 | 0.1020478065151511 |
| user_002 | Standing | 1.44603474494e12 | -1.26397 | -9.4262 | 0.344284 | -1.246551818181818 | -9.436650909090908 | 0.25719245454545453 | 1.5976201609 | 88.85324643999999 | 0.11853147265599999 | 9.516795578006075 | 9.523606955107557 | 1.7418181818182e-2 | 1.0450909090907956e-2 | 8.709154545454545e-2 | 9.595834710743807e-2 | 8.01236363636366e-2 | 9.405902479338843e-2 | 3.03393057851246e-4 | 1.0922150082642256e-4 | 7.584937289661156e-3 | 1.3732121422088666e-2 | 1.0861471471074448e-2 | 1.093449079350563e-2 | 0.11718413468592354 | 0.10421838355623468 | 0.10456811556830137 |
| user_002 | Standing | 1.446034744989e12 | -1.41725 | -9.19628 | 0.191003 | -1.34061 | -9.412265454545455 | 0.2711271818181818 | 2.0085975625 | 84.57156583839999 | 3.6482146009e-2 | 9.306806409661103 | 9.512400694854856 | 7.663999999999982e-2 | 0.21598545454545537 | 8.012418181818179e-2 | 8.265719008264466e-2 | 9.08912396694219e-2 | 0.11242739669421485 | 5.873689599999972e-3 | 4.6649716575206966e-2 | 6.419884512033053e-3 | 1.154327841562735e-2 | 1.4186659385124128e-2 | 1.8352836216098418e-2 | 0.1074396501093863 | 0.11910776374831376 | 0.13547264010160287 |
| user_002 | Standing | 1.446034745069e12 | -1.14901 | -9.4262 | 0.382604 | -1.3162245454545454 | -9.440134545454546 | 0.3164147272727273 | 1.3202239801000002 | 88.85324643999999 | 0.146385820816 | 9.50367593307537 | 9.537335764246192 | 0.16721454545454528 | 1.393454545454631e-2 | 6.618927272727271e-2 | 6.460561983471075e-2 | 9.722512396694262e-2 | 6.270590909090909e-2 | 2.796070421157019e-2 | 1.9417155702481723e-4 | 4.381019824165286e-3 | 6.482682210668667e-3 | 1.4374211457250293e-2 | 5.569287497270473e-3 | 8.051510548132361e-2 | 0.11989249958713136 | 7.462765906331562e-2 |
| user_002 | Standing | 1.446034745312e12 | -1.34061 | -9.34956 | 0.420925 | -1.246551818181818 | -9.41574909090909 | 0.28157790909090913 | 1.7972351721000002 | 87.41427219360001 | 0.177177855625 | 9.454558964929301 | 9.502762210177798 | 9.405818181818204e-2 | 6.618909090908964e-2 | 0.13934709090909086 | 7.600664462809915e-2 | 8.867438016528904e-2 | 5.1938297520661154e-2 | 8.84694156694219e-3 | 4.380995755371733e-3 | 1.9417611744826434e-2 | 8.091224411377905e-3 | 1.2680726570698658e-2 | 4.6888999789346355e-3 | 8.99512335178229e-2 | 0.11260873221335306 | 6.847554292544628e-2 |
| user_002 | Standing | 1.446034745417e12 | -1.03405 | -9.31124 | 0.382604 | -1.2570027272727275 | -9.4262 | 0.22235563636363634 | 1.0692594024999997 | 86.6991903376 | 0.146385820816 | 9.376291141006448 | 9.513699984452389 | 0.22295272727272764 | 0.11495999999999995 | 0.16024836363636366 | 0.11147636363636365 | 6.397223140495889e-2 | 0.10799378512396694 | 4.970791859834727e-2 | 1.3215801599999988e-2 | 2.567953804813224e-2 | 1.5727896119008274e-2 | 5.2051216252441725e-3 | 1.6874486719385424e-2 | 0.12541090909090913 | 7.214652885097225e-2 | 0.12990183493463603 |
| user_002 | Standing | 1.44603474545e12 | -1.18733 | -9.38788 | 0.152682 | -1.2012645454545454 | -9.40529818181818 | 0.20842090909090907 | 1.4097525289 | 88.13229089439999 | 2.3311793124000002e-2 | 9.463897464386646 | 9.48483192792895 | 1.3934545454545422e-2 | 1.741818181818111e-2 | 5.573890909090906e-2 | 8.26571900826447e-2 | 4.4653884297521144e-2 | 9.722603305785121e-2 | 1.9417155702479248e-4 | 3.0339305785121503e-4 | 3.1068259866446246e-3 | 1.1960305964237434e-2 | 3.1343260994741193e-3 | 1.4745169326966191e-2 | 0.10936318376966461 | 5.5985052464690245e-2 | 0.12142968882018182 |
| user_002 | Standing | 1.446034745458e12 | -1.11069 | -9.31124 | 0.114362 | -1.197780909090909 | -9.391363636363634 | 0.19100254545454545 | 1.2336322760999998 | 86.6991903376 | 1.3078667044000002e-2 | 9.377947604926357 | 9.470198159540521 | 8.709090909090911e-2 | 8.012363636363418e-2 | 7.664054545454545e-2 | 7.568991735537196e-2 | 4.877090909090934e-2 | 0.10387662809917354 | 7.584826446280995e-3 | 6.419797104131881e-3 | 5.873773207570247e-3 | 1.0212761951014294e-2 | 3.6076192697220193e-3 | 1.5278045231654396e-2 | 0.1010582107055844 | 6.0063460354212186e-2 | 0.1236043900177271 |
| user_002 | Standing | 1.446034745523e12 | -1.18733 | -9.46452 | 0.267643 | -1.2639699999999998 | -9.433167272727273 | 0.20493736363636364 | 1.4097525289 | 89.5771388304 | 7.163277544900001e-2 | 9.542459019285804 | 9.520695509724966 | 7.663999999999982e-2 | 3.135272727272742e-2 | 6.270563636363638e-2 | 8.329057851239673e-2 | 7.220628099173529e-2 | 7.569057851239669e-2 | 5.873689599999972e-3 | 9.829935074380258e-4 | 3.931996831768597e-3 | 1.132483541397447e-2 | 9.218735968745291e-3 | 8.224858660748308e-3 | 0.10641820997354949 | 9.601424877977899e-2 | 9.069100650421909e-2 |
| user_002 | Standing | 1.446034745563e12 | -1.18733 | -9.31124 | 0.267643 | -1.312740909090909 | -9.44361818181818 | 0.23280663636363635 | 1.4097525289 | 86.6991903376 | 7.163277544900001e-2 | 9.390451301292659 | 9.538040096804464 | 0.12541090909090902 | 0.13237818181818106 | 3.4836363636363665e-2 | 9.659173553719014e-2 | 8.265719008264485e-2 | 5.41550909090909e-2 | 1.5727896119008246e-2 | 1.7523983021487402e-2 | 1.2135722314049607e-3 | 1.1587408314951177e-2 | 1.0879123430803916e-2 | 5.431383807558227e-3 | 0.10764482484054297 | 0.10430303653683298 | 7.36979226814313e-2 |
| user_002 | Standing | 1.446034745571e12 | -1.41725 | -9.34956 | 0.459245 | -1.312740909090909 | -9.4262 | 0.2537086363636364 | 2.0085975625 | 87.41427219360001 | 0.210905970025 | 9.467511591021424 | 9.521580158050632 | 0.10450909090909088 | 7.663999999999938e-2 | 0.20553636363636363 | 8.86743801652893e-2 | 7.695669421487607e-2 | 7.062333057851237e-2 | 1.0922150082644622e-2 | 5.8736895999999044e-3 | 4.22451967768595e-2 | 9.24300741337341e-3 | 9.647899239669385e-3 | 9.21779590594365e-3 | 9.614056070864893e-2 | 9.822372035139672e-2 | 9.600935322115055e-2 |
| user_002 | Standing | 1.44603474566e12 | -1.14901 | -9.69444 | 0.191003 | -1.1942972727272727 | -9.41574909090909 | 0.2467412727272727 | 1.3202239801000002 | 93.9821669136 | 3.6482146009e-2 | 9.764162690149575 | 9.495286842248932 | 4.528727272727262e-2 | 0.2786909090909102 | 5.573827272727269e-2 | 8.360727272727261e-2 | 8.582413223140523e-2 | 8.487476033057852e-2 | 2.0509370710743704e-3 | 7.766862280991797e-2 | 3.106755046619831e-3 | 1.089125915311792e-2 | 1.2984119628550029e-2 | 8.934242761645379e-3 | 0.10436119562901681 | 0.11394788119377222 | 9.452112336216376e-2 |
| user_002 | Standing | 1.446034746211e12 | -1.22565 | -9.34956 | 0.229323 | -1.2291336363636363 | -9.433167272727271 | 0.250225 | 1.5022179224999999 | 87.41427219360001 | 5.2589038329e-2 | 9.432342188153958 | 9.51676625630396 | 3.4836363636363554e-3 | 8.360727272727075e-2 | 2.0901999999999976e-2 | 8.360727272727274e-2 | 6.1121983471074254e-2 | 6.460625619834709e-2 | 1.213572231404953e-5 | 6.990176052892232e-3 | 4.36893603999999e-4 | 1.064633821187077e-2 | 7.6267498506385895e-3 | 6.597537471873022e-3 | 0.10318109425602526 | 8.733126502369348e-2 | 8.122522681946183e-2 |
| user_002 | Standing | 1.446034748024e12 | -1.26397 | -9.34956 | 0.305964 | -1.22565 | -9.426199999999996 | 0.2711270909090909 | 1.5976201609 | 87.41427219360001 | 9.361396929600001e-2 | 9.43957129978878 | 9.509700619608166 | 3.832000000000013e-2 | 7.663999999999582e-2 | 3.483690909090914e-2 | 3.515305785123967e-2 | 3.641983471074394e-2 | 4.908799173553719e-2 | 1.4684224000000102e-3 | 5.87368959999936e-3 | 1.213610235008268e-3 | 1.6405290073628856e-3 | 2.1656748093162987e-3 | 3.4432827263538705e-3 | 4.0503444388877416e-2 | 4.653681133593382e-2 | 5.86794915311463e-2 |
| user_002 | Standing | 1.446034748412e12 | -1.26397 | -9.4262 | 0.191003 | -1.2361009090909092 | -9.419232727272725 | 0.27461081818181815 | 1.5976201609 | 88.85324643999999 | 3.6482146009e-2 | 9.512483836880302 | 9.50422673863462 | 2.7869090909090843e-2 | 6.967272727274931e-3 | 8.360781818181814e-2 | 4.370380165289258e-2 | 2.3118677685950703e-2 | 4.243754545454546e-2 | 7.766862280991699e-4 | 4.854288925622906e-5 | 6.990267261123961e-3 | 2.367569098722766e-3 | 9.984389722013142e-4 | 2.4360166227340345e-3 | 4.86576725576015e-2 | 3.159808494515631e-2 | 4.935601911351881e-2 |
| user_002 | Standing | 1.446034749448e12 | -1.26397 | -9.46452 | 0.267643 | -1.2430681818181817 | -9.44361818181818 | 0.27809436363636364 | 1.5976201609 | 89.5771388304 | 7.163277544900001e-2 | 9.552297721844154 | 9.529247951373268 | 2.0901818181818355e-2 | 2.0901818181819465e-2 | 1.045136363636362e-2 | 2.3435371900826493e-2 | 2.565223140495815e-2 | 2.311914876033059e-2 | 4.3688600330579237e-4 | 4.368860033058388e-4 | 1.0923100185950379e-4 | 1.1562033622839983e-3 | 9.212116483846524e-4 | 7.40301517880542e-4 | 3.4002990490308325e-2 | 3.0351468636371656e-2 | 2.7208482461918784e-2 |
| user_002 | Standing | 1.446034749902e12 | -1.26397 | -9.4262 | 0.305964 | -1.2500354545454544 | -9.436650909090908 | 0.29899645454545454 | 1.5976201609 | 88.85324643999999 | 9.361396929600001e-2 | 9.51548635489516 | 9.52386574249788 | 1.3934545454545644e-2 | 1.0450909090907956e-2 | 6.967545454545476e-3 | 2.9135867768595072e-2 | 1.4567933884297285e-2 | 1.995216528925621e-2 | 1.9417155702479866e-4 | 1.0922150082642256e-4 | 4.8546689661157324e-5 | 1.125312432757327e-3 | 2.4050795131480226e-4 | 5.461251240390691e-4 | 3.354567681173428e-2 | 1.5508318777830248e-2 | 2.336932014499072e-2 |
| user_002 | Standing | 1.446146629807e12 | -3.83143 | -8.69811 | -1.30349 | -1.9432845454545455 | -9.224146363636363 | -0.1260110909090909 | 14.6798558449 | 75.65711757209999 | 1.6990861801000001 | 9.593542598909956 | 9.525586852701506 | 1.8881454545454546 | 0.5260363636363632 | 1.177478909090909 | 0.5865221487603306 | 0.1710171074380165 | 0.37306916528925615 | 3.5650932575206613 | 0.2767142558677681 | 1.3864565813539171 | 1.2136749636270474 | 9.244811132719769e-2 | 0.42548822749008636 | 1.1016691715878444 | 0.3040528100958741 | 0.6522945864332207 |
| user_002 | Standing | 1.446146630131e12 | -4.09967 | -8.73643 | -0.958607 | -3.1974027272727272 | -8.907130909090908 | -0.6694627272727273 | 16.8072941089 | 76.3252091449 | 0.918927380449 | 9.698011684579937 | 9.587718584746405 | 0.9022672727272725 | 0.17070090909090752 | 0.2891442727272727 | 1.1758942148760327 | 0.3018138842975207 | 0.567837438016529 | 0.8140862314347103 | 2.9138800364462272e-2 | 8.360441045098346e-2 | 2.0436872127891808 | 0.13667532388369658 | 0.5365504326603726 | 1.4295758856350302 | 0.3696962589528011 | 0.7324960291089452 |
| user_002 | Standing | 1.446146631166e12 | -4.09967 | -8.62147 | -1.03525 | -3.9707763636363635 | -8.691142727272727 | -0.996927909090909 | 16.8072941089 | 74.3297449609 | 1.0717425625 | 9.602540373895858 | 9.616297935504827 | 0.12889363636363615 | 6.967272727272622e-2 | 3.8322090909091e-2 | 0.21313595041322309 | 8.994140495867792e-2 | 0.20236987603305776 | 1.661356949504127e-2 | 4.854288925619688e-3 | 1.4685826516446351e-3 | 9.500073981960924e-2 | 1.3845801978136778e-2 | 5.8809805061572476e-2 | 0.3082219002919962 | 0.11766818592184031 | 0.24250732991308216 |
| user_002 | Standing | 1.446146632203e12 | -3.10335 | -9.38788 | -1.11189 | -3.7338900000000006 | -8.816554545454544 | -0.9829934545454548 | 9.6307812225 | 88.13229089439999 | 1.2362993721000002 | 9.949842787149956 | 9.633812047910272 | 0.6305400000000008 | 0.5713254545454554 | 0.1288965454545452 | 0.27267628099173585 | 0.1789328925619839 | 7.98085537190083e-2 | 0.397580691600001 | 0.3264127750115712 | 1.661431943011564e-2 | 0.11091659914207376 | 6.172432608730298e-2 | 1.119388742651014e-2 | 0.33304143757507676 | 0.24844380871195598 | 0.10580116930596815 |
| user_002 | Standing | 1.446146632332e12 | -3.71647 | -8.96635 | -1.49509 | -3.6746672727272727 | -8.833972727272727 | -1.0491828181818181 | 13.812149260900002 | 80.3954323225 | 2.2352941081 | 9.820533371029295 | 9.633098330877985 | 4.180272727272749e-2 | 0.13237727272727362 | 0.4459071818181819 | 0.2568404958677688 | 0.1450466115702482 | 0.12889628099173558 | 1.7474680074380348e-3 | 1.752374233471098e-2 | 0.19883321479703311 | 0.10328141370202867 | 4.936462438091678e-2 | 3.491716672898422e-2 | 0.3213742579952985 | 0.22218151223924276 | 0.1868613569708414 |
| user_002 | Standing | 1.446146632591e12 | -4.75112 | -7.97003 | -0.920286 | -4.078772727272727 | -8.628437272727274 | -1.0979536363636366 | 22.573141254400003 | 63.5213782009 | 0.8469263217960001 | 9.324239688955663 | 9.645194785304184 | 0.672347272727273 | 0.6584072727272732 | 0.17766763636363658 | 0.49183024793388447 | 0.3455149586776864 | 0.16658295041322313 | 0.45205085514380206 | 0.43350013678016586 | 3.15657890110414e-2 | 0.44089569586581534 | 0.2401217145223895 | 4.4869705995755814e-2 | 0.6639997709832552 | 0.49002215717494807 | 0.21182470582006202 |
| user_002 | Standing | 1.44614663285e12 | -4.44456 | -9.15796 | -0.881966 | -4.664029090909091 | -8.384581818181818 | -1.3557447272727272 | 19.7541135936 | 83.86823136159998 | 0.7778640251560001 | 10.217642046008265 | 9.751354221095463 | 0.21946909090909106 | 0.7733781818181811 | 0.4737787272727272 | 0.7730566115702483 | 0.5374344628099171 | 0.32873110743801653 | 4.816668186446287e-2 | 0.5981138121123956 | 0.2244662824161652 | 0.8926250418875287 | 0.4177421818151013 | 0.1757677490628174 | 0.9447883582514809 | 0.6463297779114786 | 0.41924664466494826 |
| user_002 | Standing | 1.446146633563e12 | 0.307161 | -8.42987 | 3.83143 | -3.9220080909090913 | -8.68417909090909 | -8.420636363636369e-2 | 9.434787992100001e-2 | 71.06270821689999 | 14.6798558449 | 9.264821203980194 | 9.868962821249822 | 4.2291690909090915 | 0.25430909090909104 | 3.915636363636364 | 1.309858099173554 | 0.6036228099173553 | 1.1698770247933885 | 17.885871199500833 | 6.467311371900833e-2 | 15.332208132231406 | 3.174886053125245 | 0.5472165834318563 | 2.9135858326633977 | 1.7818209935695688 | 0.7397408893875317 | 1.706922913509394 |
| user_002 | Standing | 1.446146633821e12 | 0.805325 | -9.69444 | -2.03158 | -2.3961624545454545 | -9.231113636363636 | 6.907500000000001e-2 | 0.6485483556249999 | 93.9821669136 | 4.127317296399999 | 9.937707611196105 | 9.916000812201998 | 3.2014874545454544 | 0.46332636363636404 | 2.100655 | 1.859644537190083 | 0.7474038016528933 | 1.4048668264462811 | 10.249521921611933 | 0.21467131924049623 | 4.4127514290250005 | 5.011452262465596 | 0.8069646739427511 | 3.38240286656267 | 2.23862731656379 | 0.8983121250115413 | 1.8391310085370944 |
| user_002 | Standing | 1.44614663531e12 | -1.68549 | -8.92803 | 1.26397 | -6.367999999999808e-3 | -9.708374545454546 | 1.121141 | 2.8408765400999996 | 79.7097196809 | 1.5976201609 | 9.173233692755243 | 9.850388442713218 | 1.6791220000000002 | 0.7803445454545468 | 0.1428290000000001 | 0.9054352396694214 | 0.7879416528925623 | 0.5947563636363636 | 2.819450690884001 | 0.6089376096206632 | 2.0400123241000028e-2 | 1.0586213222613254 | 1.0029801162580023 | 0.6618157154490096 | 1.028893251149664 | 1.0014889496434807 | 0.813520568546985 |
| user_002 | Standing | 1.446146636087e12 | -1.41725 | -9.04299 | 1.41725 | -1.434669 | -9.144022727272727 | 1.5914354545454545 | 2.0085975625 | 81.7756681401 | 2.0085975625 | 9.262443698349804 | 9.402852673801274 | 1.7419000000000073e-2 | 0.10103272727272739 | 0.17418545454545464 | 0.4547756280991736 | 0.2530417355371902 | 0.3363332148760331 | 3.0342156100000254e-4 | 1.0207611980165312e-2 | 3.0340572575206646e-2 | 0.30754998365910374 | 0.10993550811472588 | 0.27250323172102253 | 0.5545718922367989 | 0.33156523960561046 | 0.5220184208636919 |
| user_002 | Standing | 1.446146636411e12 | -1.26397 | -9.2346 | 1.60885 | -1.298806272727273 | -9.185827272727273 | 1.5147927272727273 | 1.5976201609 | 85.27783716 | 2.5883983225 | 9.458533482702274 | 9.40201223008114 | 3.4836272727272855e-2 | 4.877272727272697e-2 | 9.40572727272726e-2 | 0.15739753719008268 | 0.11496322314049562 | 9.722600826446276e-2 | 1.2135658975289344e-3 | 2.378778925619805e-3 | 8.846770552892538e-3 | 5.141716259284153e-2 | 2.2442123278512357e-2 | 1.2644633745004498e-2 | 0.22675352829193535 | 0.14980695337170555 | 0.11244836034822606 |
| user_002 | Standing | 1.446146638612e12 | 0.843646 | -10.1926 | 2.49022 | 0.20961881818181816 | -9.109183636363637 | 1.9223843636363638 | 0.711738573316 | 103.88909476 | 6.2011956484 | 10.526254271188588 | 9.36654512772446 | 0.6340271818181819 | 1.0834163636363634 | 0.5678356363636361 | 0.7971253140495868 | 0.7556363636363634 | 0.6384612727272727 | 0.4019904672843058 | 1.1737910169950407 | 0.32243730992449554 | 0.772237746696704 | 0.7814514966276483 | 0.5705717780425935 | 0.8787705882064465 | 0.8839974528400228 | 0.7553620178712943 |
| user_002 | Standing | 1.446146639259e12 | -0.497565 | -9.19628 | 1.76214 | -0.23977409090909088 | -9.366977272727274 | 1.598401818181818 | 0.24757092922499999 | 84.57156583839999 | 3.1051373796000004 | 9.376794449449395 | 9.519829806157274 | 0.25779090909090907 | 0.1706972727272742 | 0.163738181818182 | 0.5260335371900827 | 0.38922008264462815 | 0.3996728760330578 | 6.645615280991735e-2 | 2.9137558916529426e-2 | 2.6810192185124027e-2 | 0.3264686576048926 | 0.21898335570706234 | 0.2096612856533523 | 0.5713743585469098 | 0.46795657459540235 | 0.45788785270342375 |
| user_002 | Standing | 1.446146639583e12 | -0.344284 | -9.38788 | 2.03038 | -0.396539 | -9.20673 | 1.709881818181818 | 0.11853147265599999 | 88.13229089439999 | 4.1224429444 | 9.611101149787988 | 9.376979023936917 | 5.2254999999999996e-2 | 0.1811499999999988 | 0.320498181818182 | 0.4113893884297521 | 0.14061272727272775 | 0.28787975206611577 | 2.7305850249999997e-3 | 3.281532249999957e-2 | 0.10271908454876046 | 0.24823389758578668 | 3.009893257986488e-2 | 0.1276876912033809 | 0.49823076740180017 | 0.17349043944801362 | 0.35733414502868444 |
| user_002 | Standing | 1.446146640101e12 | -0.191003 | -9.15796 | 2.14534 | -0.177068 | -9.192796363636365 | 1.9223872727272728 | 3.6482146009e-2 | 83.86823136159998 | 4.6024837156 | 9.407826381434182 | 9.394355999014406 | 1.3935000000000003e-2 | 3.4836363636365775e-2 | 0.2229527272727272 | 0.12731222314049587 | 6.080545454545539e-2 | 0.12604595041322328 | 1.9418422500000007e-4 | 1.2135722314051078e-3 | 4.970791859834707e-2 | 2.599075022934561e-2 | 6.286337555822744e-3 | 2.371156496318561e-2 | 0.1612164700933053 | 7.928642731150612e-2 | 0.15398559985656324 |
| user_002 | Standing | 1.446146641202e12 | -7.6042e-2 | -9.00468 | 2.10702 | -0.1666170909090909 | -9.112672727272729 | 2.10702 | 5.782385764e-3 | 81.0842619024 | 4.439533280399999 | 9.248220237892477 | 9.35506650966221 | 9.057509090909091e-2 | 0.10799272727272857 | 0.0 | 7.569043801652892e-2 | 4.655404958677794e-2 | 3.45196694214877e-2 | 8.203847093190083e-3 | 1.1662429143801934e-2 | 0.0 | 7.256156008209616e-3 | 3.9573487218633316e-3 | 1.6846589066867126e-3 | 8.518307348417065e-2 | 6.290746157542308e-2 | 4.1044596558946864e-2 |
| user_002 | Standing | 1.446146641525e12 | -0.267643 | -9.08132 | 2.14534 | -0.14223136363636363 | -9.119640000000002 | 2.127921818181818 | 7.163277544900001e-2 | 82.47037294239999 | 4.6024837156 | 9.335121286488409 | 9.366200339151453 | 0.1254116363636364 | 3.832000000000235e-2 | 1.7418181818182e-2 | 9.722576859504133e-2 | 5.4788099173553366e-2 | 3.8320000000000014e-2 | 1.5728078535404966e-2 | 1.4684224000001804e-3 | 3.03393057851246e-4 | 1.0720399860847486e-2 | 4.665633606010519e-3 | 2.0487305761081945e-3 | 0.10353936382288374 | 6.830544345812066e-2 | 4.5262905078090096e-2 |
| user_002 | Standing | 1.446146642043e12 | -7.6042e-2 | -9.15796 | 2.22198 | -0.17358436363636362 | -9.137057272727274 | 2.194110909090909 | 5.782385764e-3 | 83.86823136159998 | 4.937195120399999 | 9.423969910168642 | 9.398903411353272 | 9.754236363636362e-2 | 2.090272727272513e-2 | 2.7869090909090843e-2 | 7.062344628099174e-2 | 3.515396694214814e-2 | 5.2254545454545394e-2 | 9.514512703768591e-3 | 4.3692400743792695e-4 | 7.766862280991699e-4 | 6.6537959533966955e-3 | 2.3644125287002114e-3 | 4.283909976859498e-3 | 8.15708033146462e-2 | 4.862522523032887e-2 | 6.545158498355481e-2 |
| user_002 | Standing | 1.446146644761e12 | 3.56439 | -8.58315 | 1.18733 | 3.867466363636363 | -8.33232818181818 | 1.90845 | 12.7048760721 | 73.67046392249999 | 1.4097525289 | 9.36936991069837 | 9.391053458142947 | 0.30307636363636314 | 0.25082181818181937 | 0.72112 | 0.24797165289256215 | 0.1133766115702486 | 0.3094122314049587 | 9.185528219504102e-2 | 6.291158447603365e-2 | 0.5200140543999999 | 7.199465958354628e-2 | 1.7020939596919755e-2 | 0.14594144823771596 | 0.2683182058369247 | 0.1304643230807555 | 0.38202283732483316 |
| user_002 | Standing | 1.446146645861e12 | 3.90927 | -8.50651 | 1.30229 | 3.6445127272727276 | -8.499542727272727 | 1.295322727272727 | 15.282391932899998 | 72.36071238010001 | 1.6959592440999998 | 9.45193438176017 | 9.340642711649064 | 0.26475727272727223 | 6.967272727273155e-3 | 6.967272727272933e-3 | 0.16563198347107458 | 3.3569586776859464e-2 | 8.075710743801658e-2 | 7.009641346198321e-2 | 4.854288925620431e-5 | 4.854288925620121e-5 | 5.16384289590534e-2 | 1.921857115552206e-3 | 1.162056965447032e-2 | 0.2272409051184522 | 4.383899081356921e-2 | 0.1077987460709554 |
| user_002 | Standing | 1.446309439349e12 | -2.41358 | -9.15796 | -1.22685 | 2.6586354545454545 | -8.621471818181819 | 0.7832245454545453 | 5.8253684164 | 83.86823136159998 | 1.5051609225 | 9.549804223150336 | 9.421158271488634 | 5.0722154545454545 | 0.5364881818181804 | 2.0100745454545454 | 1.1182553719008264 | 0.14821446280991732 | 0.4478087603305785 | 25.727369617329753 | 0.28781956923057694 | 4.040399678284298 | 5.350911544834711 | 6.64813328893311e-2 | 0.7877228821965438 | 2.313203740450614 | 0.25783974264905535 | 0.8875375384717786 |
| user_002 | Standing | 1.446309439358e12 | -2.56686 | -9.15796 | -1.30349 | 2.052478181818182 | -8.66676 | 0.56027 | 6.5887702596 | 83.86823136159998 | 1.6990861801000001 | 9.599796237488585 | 9.416562161867116 | 4.619338181818183 | 0.4911999999999992 | 1.86376 | 1.5011419008264466 | 0.17956785123966928 | 0.6042570247933884 | 21.338285238003312 | 0.2412774399999992 | 3.4736013376000003 | 7.275653235214801 | 8.646951705642343e-2 | 1.1016502629592788 | 2.697341883264856 | 0.29405699627185106 | 1.0495952853168113 |
| user_002 | Standing | 1.446309439365e12 | -2.75846 | -9.08132 | -1.11189 | 1.4567718181818181 | -8.715531818181818 | 0.34428272727272735 | 7.609101571599999 | 82.47037294239999 | 1.2362993721000002 | 9.55592872964737 | 9.427607217433433 | 4.2152318181818185 | 0.36578818181818207 | 1.4561727272727274 | 1.8767440495867773 | 0.21060446280991726 | 0.7347361983471075 | 17.7681792810124 | 0.13380099395785142 | 2.120439011652893 | 8.890306776391661 | 9.85791846531928e-2 | 1.294377728927423 | 2.981661747481035 | 0.31397322282830553 | 1.1377072246089601 |
| user_002 | Standing | 1.446309439406e12 | -2.8351 | -9.11964 | -0.958607 | -1.5496845454545456 | -9.03260090909091 | -0.6694087272727273 | 8.03779201 | 83.1678337296 | 0.918927380449 | 9.598153630779672 | 9.558002976551343 | 1.2854154545454546 | 8.703909090909079e-2 | 0.2891982727272727 | 2.9196666942148766 | 0.2993195041322308 | 1.020040347107438 | 1.6522928907842975 | 7.575803346280971e-3 | 8.3635640948438e-2 | 11.95888436007566 | 0.12769190776318512 | 1.5646831823807708 | 3.458161991589703 | 0.3573400450036143 | 1.2508729681229709 |
| user_002 | Standing | 1.446309439414e12 | -2.8351 | -8.96635 | -1.15021 | -2.1384236363636364 | -9.074404545454547 | -0.8819123636363635 | 8.03779201 | 80.3954323225 | 1.3229830441 | 9.473975267890454 | 9.571195588051067 | 0.6966763636363638 | 0.10805454545454651 | 0.26829763636363646 | 2.975400330578513 | 0.3078758677685945 | 1.0371470743801652 | 0.48535795564958695 | 1.1675784793388658e-2 | 7.19836216783141e-2 | 12.002372353858306 | 0.12873569078467273 | 1.5706435300693327 | 3.464444018000335 | 0.358797562400684 | 1.253253178758918 |
| user_002 | Standing | 1.44630943943e12 | -2.95007 | -8.88971 | -0.996927 | -2.78987 | -9.09879 | -1.0491280909090908 | 8.702913004900001 | 79.02694388409998 | 0.993863443329 | 9.419326957502271 | 9.57699232493614 | 0.16020000000000012 | 0.20908000000000015 | 5.220109090909075e-2 | 2.4864117355371897 | 0.2717823140495863 | 0.8417354876033056 | 2.5664040000000037e-2 | 4.3714446400000065e-2 | 2.724953892099157e-3 | 9.03017986804523 | 9.63240158082641e-2 | 1.1578888306481607 | 3.005025768283066 | 0.31036110550174306 | 1.0760524293212486 |
| user_002 | Standing | 1.446309439438e12 | -2.91174 | -9.00468 | -1.15021 | -2.835157272727273 | -9.084855454545455 | -1.042160818181818 | 8.4782298276 | 81.0842619024 | 1.3229830441 | 9.533387371448828 | 9.57549988387237 | 7.658272727272708e-2 | 8.017545454545427e-2 | 0.1080491818181819 | 2.032263305785124 | 0.23029933884297485 | 0.668824090909091 | 5.8649141165288965e-3 | 6.428103511570204e-3 | 1.1674625691578532e-2 | 6.691861258662209 | 7.074297347017257e-2 | 0.7916410985942772 | 2.586863208339824 | 0.2659755129145775 | 0.8897421528703006 |
| user_002 | Standing | 1.446309439447e12 | -3.02671 | -8.96635 | -0.996927 | -2.8769618181818184 | -9.067436363636363 | -1.0142914545454544 | 9.1609734241 | 80.3954323225 | 0.993863443329 | 9.51579051839252 | 9.567863000318182 | 0.1497481818181816 | 0.10108636363636236 | 1.7364454545454433e-2 | 1.6259369421487604 | 0.19483446280991695 | 0.5009699504132231 | 2.242451795785118e-2 | 1.0218452913222884e-2 | 3.015242816611531e-4 | 4.754055738658079 | 4.973761100773835e-2 | 0.475886570110792 | 2.180379723501867 | 0.22301930635650885 | 0.6898453233231288 |
| user_002 | Standing | 1.446309439479e12 | -2.95007 | -9.00468 | -1.30349 | -2.915229090909091 | -9.04648 | -1.0979539090909094 | 8.702913004900001 | 81.0842619024 | 1.6990861801000001 | 9.564845063428889 | 9.569223294629118 | 3.48409090909092e-2 | 4.180000000000028e-2 | 0.20553609090909064 | 0.43384776859504137 | 0.10800570247933873 | 0.19035499999999994 | 1.2138889462809993e-3 | 1.7472400000000236e-3 | 4.224508466618997e-2 | 0.5615326180894062 | 1.6430564487828706e-2 | 4.903380208257624e-2 | 0.749354801205281 | 0.12818176347604485 | 0.2214357741706977 |
| user_002 | Standing | 1.446309439511e12 | -2.91174 | -9.00468 | -0.920286 | -2.95355 | -9.015127272727273 | -1.0979539090909092 | 8.4782298276 | 81.0842619024 | 0.8469263217960001 | 9.508386721825948 | 9.55133171916616 | 4.18099999999999e-2 | 1.044727272727286e-2 | 0.17766790909090913 | 8.296809917355369e-2 | 6.778289256198326e-2 | 0.14284077685950408 | 1.748076099999992e-3 | 1.0914550743801931e-4 | 3.156588592073555e-2 | 8.785557995642373e-3 | 8.222870272652123e-3 | 2.737519115496168e-2 | 9.373130744656437e-2 | 9.068004340896692e-2 | 0.16545449874500748 |
| user_002 | Standing | 1.446309439593e12 | -2.6435 | -8.92803 | -1.03525 | -2.7480109090909095 | -9.001192727272725 | -0.9934444545454544 | 6.98809225 | 79.7097196809 | 1.0717425625 | 9.368540681098631 | 9.46560887552769 | 0.10451090909090954 | 7.316272727272555e-2 | 4.180554545454562e-2 | 0.1415662809917357 | 3.610330578512395e-2 | 0.15454909917355375 | 1.0922530119008358e-2 | 5.352784661983218e-3 | 1.74770363075208e-3 | 2.8163932671224665e-2 | 2.3986276106686684e-3 | 3.2164896790317817e-2 | 0.167821132969673 | 4.897578596274559e-2 | 0.1793457465074592 |
| user_002 | Standing | 1.446309439616e12 | -2.75846 | -9.04299 | -0.920286 | -2.6853045454545454 | -8.997706363636365 | -1.017830090909091 | 7.609101571599999 | 81.7756681401 | 0.8469263217960001 | 9.49903658449087 | 9.446218153980428 | 7.315545454545447e-2 | 4.528363636363508e-2 | 9.754409090909089e-2 | 0.14884958677685975 | 3.958702479338815e-2 | 0.11242859504132229 | 5.351720529752055e-3 | 2.0506077223139334e-3 | 9.514849671280988e-3 | 2.8778300438317098e-2 | 2.593859808640096e-3 | 2.0548568995271223e-2 | 0.16964168249082268 | 5.092995001607695e-2 | 0.14334772057926565 |
| user_002 | Standing | 1.446309439624e12 | -2.6435 | -9.19628 | -0.805325 | -2.6574345454545454 | -9.015124545454547 | -1.000411727272727 | 6.98809225 | 84.57156583839999 | 0.6485483556249999 | 9.602510424051879 | 9.453000156778382 | 1.3934545454545422e-2 | 0.18115545454545234 | 0.19508672727272713 | 0.1491659504132233 | 5.3205619834710276e-2 | 0.12446316528925618 | 1.9417155702479248e-4 | 3.281729871156945e-2 | 3.805883115798342e-2 | 2.8786016259804703e-2 | 5.487897918707642e-3 | 2.3650999151300523e-2 | 0.16966442249276867 | 7.408034772264263e-2 | 0.1537888134790711 |
| user_002 | Standing | 1.446309439641e12 | -2.87342 | -8.92803 | -0.958607 | -2.6818199999999996 | -9.001189090909092 | -1.0004115454545455 | 8.2565424964 | 79.7097196809 | 0.918927380449 | 9.427894227119278 | 9.446675907091665 | 0.19160000000000021 | 7.315909090909223e-2 | 4.180454545454548e-2 | 0.13808049586776872 | 6.0806280991735046e-2 | 0.11622894214876026 | 3.671056000000008e-2 | 5.352252582644821e-3 | 1.7476200206611595e-3 | 2.421915496558982e-2 | 6.044008924868438e-3 | 1.9127603799277224e-2 | 0.15562504607417735 | 7.774322430198298e-2 | 0.13830258059514733 |
| user_002 | Standing | 1.446309439682e12 | -2.87342 | -9.15796 | -1.18853 | -2.744525454545455 | -9.077831818181819 | -0.9272538181818182 | 8.2565424964 | 83.86823136159998 | 1.4126035609000003 | 9.671472350107814 | 9.530021680868458 | 0.12889454545454493 | 8.012818181818027e-2 | 0.2612761818181819 | 9.595851239669423e-2 | 8.519438016528837e-2 | 0.10482755371900822 | 1.6613803847933747e-2 | 6.420525521487355e-3 | 6.826524318548766e-2 | 1.2397225940646135e-2 | 1.0105455614575334e-2 | 1.8697265879660405e-2 | 0.11134283066567931 | 0.1005258952438392 | 0.13673794601229172 |
| user_002 | Standing | 1.446309439779e12 | -3.14167 | -8.92803 | -1.03525 | -3.1904399999999997 | -8.837456363636363 | -1.1362751818181818 | 9.8700903889 | 79.7097196809 | 1.0717425625 | 9.521110892763511 | 9.466807733922074 | 4.876999999999976e-2 | 9.057363636363647e-2 | 0.10102518181818176 | 0.20078768595041332 | 0.18590322314049648 | 0.10704351239669427 | 2.378512899999976e-3 | 8.20358360413225e-3 | 1.0206087361396683e-2 | 4.9067638348234466e-2 | 4.669208395063887e-2 | 1.500442246723141e-2 | 0.22151216298035298 | 0.21608351151959482 | 0.12249254045545553 |
| user_002 | Standing | 1.446309440013e12 | -2.91174 | -9.11964 | -1.18853 | -3.002320909090909 | -9.060413636363636 | -0.8924176363636362 | 8.4782298276 | 83.1678337296 | 1.4126035609000003 | 9.646692029815195 | 9.588635056175285 | 9.058090909090888e-2 | 5.9226363636364354e-2 | 0.29611236363636384 | 7.44266115702479e-2 | 0.1254145454545451 | 0.15423192561983476 | 8.204901091735498e-3 | 3.5077621495868618e-3 | 8.768253189831417e-2 | 6.577076111945887e-3 | 2.27942267930878e-2 | 3.7267546187075146e-2 | 8.10991745454039e-2 | 0.1509775704967059 | 0.19304804113762758 |
| user_002 | Standing | 1.446309440094e12 | -3.17999 | -9.00468 | -0.728685 | -3.065028181818182 | -9.018611818181817 | -1.073569 | 10.1123364001 | 81.0842619024 | 0.530981829225 | 9.577451651234007 | 9.587080246425797 | 0.11496181818181794 | 1.393181818181688e-2 | 0.34488399999999997 | 8.012586776859494e-2 | 6.777396694214909e-2 | 0.1513813140495867 | 1.3216219639669367e-2 | 1.9409555785120339e-4 | 0.11894497345599998 | 7.150541952441762e-3 | 7.041439655522178e-3 | 3.746930176322839e-2 | 8.45608771976838e-2 | 8.391328652556863e-2 | 0.1935698885757503 |
| user_002 | Standing | 1.446309440167e12 | -3.33327 | -8.85139 | -0.613724 | -3.1799899999999997 | -8.955903636363637 | -0.8436462727272727 | 11.1106888929 | 78.34710493210001 | 0.37665714817600005 | 9.478103764634358 | 9.542767424351403 | 0.15328000000000053 | 0.10451363636363631 | 0.22992227272727261 | 8.804140495867778e-2 | 5.5741404958677035e-2 | 0.15961630578512392 | 2.3494758400000162e-2 | 1.0923100185950402e-2 | 5.286425149607433e-2 | 9.90503651697974e-3 | 6.0904046268218555e-3 | 3.276510562810293e-2 | 9.952404994261306e-2 | 7.804104450109478e-2 | 0.18101134115878742 |
| user_002 | Standing | 1.446309440208e12 | -3.21831 | -8.92803 | -0.690364 | -3.246179090909091 | -8.941968181818181 | -0.6694626363636363 | 10.357519256099998 | 79.7097196809 | 0.476602452496 | 9.515452768496935 | 9.539032890870038 | 2.7869090909091288e-2 | 1.3938181818181405e-2 | 2.0901363636363635e-2 | 0.10007537190082677 | 7.189330578512373e-2 | 0.1862184545454545 | 7.766862280991947e-4 | 1.942729123966827e-4 | 4.3686700185950405e-4 | 1.291020204718265e-2 | 7.161748909316253e-3 | 4.620502815389105e-2 | 0.11362307004821974 | 8.462711686756351e-2 | 0.21495354882832488 |
| user_002 | Standing | 1.446309440255e12 | -3.14167 | -8.88971 | -0.767005 | -3.21831 | -8.924548181818182 | -0.641592909090909 | 9.8700903889 | 79.02694388409998 | 0.5882966700250001 | 9.45966864869087 | 9.510356482993483 | 7.663999999999982e-2 | 3.4838181818182434e-2 | 0.125412090909091 | 9.785851239669441e-2 | 7.189198347107445e-2 | 0.15993210743801647 | 5.873689599999972e-3 | 1.2136989123967372e-3 | 1.5728192546190106e-2 | 1.1483703051540239e-2 | 7.808080333508638e-3 | 3.854711547508264e-2 | 0.10716204109450435 | 8.836334270221242e-2 | 0.19633419334156402 |
| user_002 | Standing | 1.446309440264e12 | -3.06503 | -9.11964 | -0.805325 | -3.1904409090909094 | -8.948934545454545 | -0.6833968181818182 | 9.3944089009 | 83.1678337296 | 0.6485483556249999 | 9.654573578678916 | 9.526400963474687 | 0.12541090909090924 | 0.17070545454545538 | 0.12192818181818177 | 9.374148760330593e-2 | 7.790942148760345e-2 | 0.13111266942148758 | 1.57278961190083e-2 | 2.9140352211570533e-2 | 1.4866481521487592e-2 | 1.0264614582719784e-2 | 9.464194154019558e-3 | 2.238315203485424e-2 | 0.1013144342269145 | 9.728408993262751e-2 | 0.1496099997822814 |
| user_002 | Standing | 1.446309440272e12 | -3.14167 | -8.96635 | -0.690364 | -3.169539090909091 | -8.945450000000001 | -0.6868804545454544 | 9.8700903889 | 80.3954323225 | 0.476602452496 | 9.52586611095789 | 9.516273010080207 | 2.7869090909090843e-2 | 2.0899999999999253e-2 | 3.4835454545455447e-3 | 8.234049586776869e-2 | 7.537520661157031e-2 | 0.12002820661157027 | 7.766862280991699e-4 | 4.3680999999996876e-4 | 1.2135088933884925e-5 | 8.199335294365147e-3 | 9.287619276784394e-3 | 2.095440710115026e-2 | 9.05501810841102e-2 | 9.637229517233879e-2 | 0.14475637153904578 |
| user_002 | Standing | 1.446309440369e12 | -3.21831 | -8.92803 | -0.652044 | -3.1939245454545455 | -8.924548181818182 | -0.6172073636363635 | 10.357519256099998 | 79.7097196809 | 0.42516137793599995 | 9.51274935625532 | 9.499286230174913 | 2.4385454545454266e-2 | 3.4818181818181415e-3 | 3.483663636363643e-2 | 5.953851239669412e-2 | 7.917504132231427e-2 | 8.899176033057847e-2 | 5.946503933884161e-4 | 1.2123057851239389e-5 | 1.2135912331322361e-3 | 5.207328120210363e-3 | 9.433200728324624e-3 | 9.89077617961757e-3 | 7.216181899183503e-2 | 9.712466591100648e-2 | 9.945238146780383e-2 |
| user_002 | Standing | 1.446309440385e12 | -3.06503 | -9.04299 | -0.690364 | -3.1556045454545454 | -8.948933636363638 | -0.6520440909090909 | 9.3944089009 | 81.7756681401 | 0.476602452496 | 9.57322722458294 | 9.511886865986183 | 9.057454545454524e-2 | 9.405636363636205e-2 | 3.83199090909091e-2 | 5.922181818181806e-2 | 7.157404958677706e-2 | 8.455795867768595e-2 | 8.203748284297482e-3 | 8.846599540495569e-3 | 1.4684154327355378e-3 | 4.553102362734779e-3 | 8.400471258151765e-3 | 9.930491764654397e-3 | 6.74766801401401e-2 | 9.165408478705009e-2 | 9.965185279087588e-2 |
| user_002 | Standing | 1.446309440402e12 | -3.02671 | -8.92803 | -0.652044 | -3.145153636363636 | -8.966352727272728 | -0.6485603636363636 | 9.1609734241 | 79.7097196809 | 0.42516137793599995 | 9.449648378798864 | 9.524740607271267 | 0.11844363636363608 | 3.832272727272823e-2 | 3.4836363636363554e-3 | 6.903933884297513e-2 | 6.365760330578528e-2 | 7.632391735537192e-2 | 1.4028894995041256e-2 | 1.468631425619908e-3 | 1.213572231404953e-5 | 5.942090943951906e-3 | 6.592294127047354e-3 | 9.266348353708495e-3 | 7.708495925893653e-2 | 8.119294382547879e-2 | 9.626187383231481e-2 |
| user_002 | Standing | 1.446309440443e12 | -3.21831 | -8.85139 | -0.537083 | -3.1939245454545455 | -8.952417272727272 | -0.6799133636363636 | 10.357519256099998 | 78.34710493210001 | 0.28845814888899995 | 9.433614489530989 | 9.530466504098127 | 2.4385454545454266e-2 | 0.10102727272727208 | 0.1428303636363636 | 8.044033057851228e-2 | 6.270776859504178e-2 | 8.455816528925623e-2 | 5.946503933884161e-4 | 1.0206509834710612e-2 | 2.0400512776495853e-2 | 8.570026448685191e-3 | 6.867015413223179e-3 | 1.1296374451510148e-2 | 9.257443733928493e-2 | 8.286745690090398e-2 | 0.1062844036136542 |
| user_002 | Standing | 1.446309440523e12 | -3.29495 | -8.92803 | -0.652044 | -3.1799899999999997 | -8.966352727272726 | -0.7426192727272727 | 10.856695502500001 | 79.7097196809 | 0.42516137793599995 | 9.538950495800677 | 9.543716163405005 | 0.1149600000000004 | 3.832272727272645e-2 | 9.057527272727273e-2 | 7.695669421487607e-2 | 7.47438016528927e-2 | 9.50090330578512e-2 | 1.321580160000009e-2 | 1.4686314256197718e-3 | 8.203880029619835e-3 | 7.959930590533444e-3 | 9.852753776408735e-3 | 1.2180060023302025e-2 | 8.921844310754051e-2 | 9.926103856200949e-2 | 0.11036330922594711 |
| user_002 | Standing | 1.446309440604e12 | -2.87342 | -8.85139 | -0.652044 | -3.1730218181818186 | -8.938482727272726 | -0.5963053636363637 | 8.2565424964 | 78.34710493210001 | 0.42516137793599995 | 9.32892323939028 | 9.504807224694117 | 0.29960181818181875 | 8.709272727272577e-2 | 5.5738636363636296e-2 | 0.1399796694214878 | 6.840826446280976e-2 | 8.455803305785121e-2 | 8.976124945785158e-2 | 7.585143143801391e-3 | 3.1067955836776785e-3 | 2.417485405169051e-2 | 5.938182953268183e-3 | 1.1084500493594286e-2 | 0.155482648715831 | 7.70596064956744e-2 | 0.10528295443040286 |
| user_002 | Standing | 1.446309440636e12 | -3.14167 | -9.00468 | -0.422122 | -3.07548 | -8.95590272727273 | -0.5754033636363636 | 9.8700903889 | 81.0842619024 | 0.178186982884 | 9.546336432065655 | 9.487635980915412 | 6.618999999999975e-2 | 4.877727272727128e-2 | 0.15328136363636358 | 0.12509504132231408 | 7.125950413223091e-2 | 7.695739669421485e-2 | 4.381116099999967e-3 | 2.379222334710603e-3 | 2.349517643822312e-2 | 2.2137127736138257e-2 | 6.930217224943577e-3 | 9.248688850788124e-3 | 0.14878550916046313 | 8.324792625010893e-2 | 9.617010372661622e-2 |
| user_002 | Standing | 1.446309440709e12 | -3.29495 | -8.96635 | -0.537083 | -3.1486372727272722 | -8.95590181818182 | -0.4325730909090909 | 10.856695502500001 | 80.3954323225 | 0.28845814888899995 | 9.567684462496086 | 9.504070361761608 | 0.14631272727272782 | 1.0448181818180302e-2 | 0.10450990909090907 | 8.234082644628084e-2 | 8.392644628099158e-2 | 0.10989387603305784 | 2.140741416198363e-2 | 1.0916450330575344e-4 | 1.0922321098190078e-2 | 7.972127924868498e-3 | 8.659925591434994e-3 | 1.8666175948377908e-2 | 8.92867735158377e-2 | 9.305872120029908e-2 | 0.13662421435594024 |
| user_002 | Standing | 1.446309440717e12 | -3.06503 | -8.88971 | -0.383802 | -3.1521209090909093 | -8.952418181818182 | -0.41863845454545456 | 9.3944089009 | 79.02694388409998 | 0.147303975204 | 9.411092219301858 | 9.50122307587 | 8.709090909090911e-2 | 6.27081818181825e-2 | 3.483645454545459e-2 | 8.42410743801651e-2 | 8.804347107437994e-2 | 0.10894376033057851 | 7.584826446280995e-3 | 3.932316066942234e-3 | 1.2135785652975235e-3 | 8.263396200676159e-3 | 8.9898190453794e-3 | 1.8590048705084142e-2 | 9.090322436897472e-2 | 9.48146562793928e-2 | 0.1363453288715244 |
| user_002 | Standing | 1.446309440757e12 | -3.06503 | -9.00468 | -0.460442 | -3.124250909090909 | -9.001190909090907 | -0.47437718181818184 | 9.3944089009 | 81.0842619024 | 0.21200683536400003 | 9.523165316146937 | 9.540537199617482 | 5.9220909090909046e-2 | 3.4890909090936617e-3 | 1.3935181818181819e-2 | 8.74085123966943e-2 | 8.044330578512415e-2 | 5.573867768595043e-2 | 3.5071160735537137e-3 | 1.2173755371920035e-5 | 1.9418929230578515e-4 | 1.0291470260030057e-2 | 8.978836671525157e-3 | 4.037954539944403e-3 | 0.10144688393455 | 9.475672362173122e-2 | 6.354490176201709e-2 |
| user_002 | Standing | 1.446309440968e12 | -3.37159 | -8.85139 | -0.460442 | -3.305400909090909 | -8.93499818181818 | -0.453475090909091 | 11.3676191281 | 78.34710493210001 | 0.21200683536400003 | 9.482970573378577 | 9.538478337551858 | 6.618909090909098e-2 | 8.360818181817997e-2 | 6.966909090909024e-3 | 7.410644628099185e-2 | 8.456008264462754e-2 | 7.315664462809915e-2 | 4.3809957553719095e-3 | 6.99032806694184e-3 | 4.85378222809908e-5 | 8.214780759128489e-3 | 9.829378283621232e-3 | 8.988266418359123e-3 | 9.063542772629525e-2 | 9.914322106740951e-2 | 9.48064682305966e-2 |
| user_002 | Standing | 1.446309440976e12 | -3.17999 | -8.81307 | -0.728685 | -3.305400909090909 | -8.921063636363634 | -0.48831172727272737 | 10.1123364001 | 77.6702028249 | 0.530981829225 | 9.397527390448245 | 9.527360093705266 | 0.1254109090909088 | 0.10799363636363424 | 0.24037327272727266 | 8.329057851239675e-2 | 8.835999999999929e-2 | 8.392442148760329e-2 | 1.572789611900819e-2 | 1.1662625495040864e-2 | 5.77793102416198e-2 | 9.590530370548467e-3 | 1.0491278978812782e-2 | 1.2889442742334329e-2 | 9.793125328794923e-2 | 0.10242694459375805 | 0.11353168166786894 |
| user_002 | Standing | 1.446309441033e12 | -2.91174 | -9.15796 | -0.422122 | -3.071993636363636 | -8.941968181818181 | -0.589338 | 8.4782298276 | 83.86823136159998 | 0.178186982884 | 9.618973342934472 | 9.475820568928262 | 0.160253636363636 | 0.21599181818181812 | 0.16721600000000003 | 0.17386785123966939 | 0.10672925619834589 | 0.11021046280991734 | 2.5681227967768474e-2 | 4.665246552148757e-2 | 2.796119065600001e-2 | 3.920310875003756e-2 | 1.6438388672426554e-2 | 1.7255076525382412e-2 | 0.19799774935599032 | 0.12821227972556512 | 0.13135857994582012 |
| user_002 | Standing | 1.446309441106e12 | -2.98839 | -8.96635 | -0.498763 | -2.9674845454545458 | -9.053445454545455 | -0.4778608181818182 | 8.9304747921 | 80.3954323225 | 0.24876453016900002 | 9.464389660446626 | 9.539829440026057 | 2.0905454545454116e-2 | 8.70954545454552e-2 | 2.090218181818182e-2 | 9.279487603305793e-2 | 7.347512396694161e-2 | 9.279223140495872e-2 | 4.3703802975204815e-4 | 7.585618202479452e-3 | 4.3690120476033066e-4 | 1.3351447519158555e-2 | 1.0077673469045744e-2 | 1.2359885965731031e-2 | 0.11554846394114703 | 0.10038761611396968 | 0.11117502401947585 |
| user_002 | Standing | 1.446309441162e12 | -3.06503 | -8.85139 | -0.537083 | -3.037160909090909 | -9.053446363636365 | -0.5335994545454544 | 9.3944089009 | 78.34710493210001 | 0.28845814888899995 | 9.38242889564792 | 9.564728399777655 | 2.7869090909091288e-2 | 0.20205636363636437 | 3.4835454545455447e-3 | 5.098809917355374e-2 | 6.302636363636341e-2 | 5.447196694214875e-2 | 7.766862280991947e-4 | 4.082677408595071e-2 | 1.2135088933884925e-5 | 5.113660344703236e-3 | 6.733654338918086e-3 | 4.6513847852577e-3 | 7.1509861870257e-2 | 8.205884680470525e-2 | 6.820106146723598e-2 |
| user_002 | Standing | 1.446309441389e12 | -3.33327 | -8.92803 | -0.345482 | -3.347204545454545 | -8.900162727272727 | -0.3594163636363636 | 11.1106888929 | 79.7097196809 | 0.119357812324 | 9.536234392364944 | 9.51624032343799 | 1.3934545454544978e-2 | 2.7867272727272407e-2 | 1.3934363636363578e-2 | 8.455735537190089e-2 | 9.374289256198312e-2 | 8.740813223140496e-2 | 1.941715570247801e-4 | 7.765848892561805e-4 | 1.941664899504116e-4 | 8.604227120661164e-3 | 1.3292391518181741e-2 | 1.1315045705301273e-2 | 9.275897326221956e-2 | 0.11529263427548934 | 0.1063722036309358 |
| user_002 | Standing | 1.446309441478e12 | -3.37159 | -8.88971 | -0.383802 | -3.343720909090909 | -8.889711818181818 | -0.32806318181818184 | 11.3676191281 | 79.02694388409998 | 0.147303975204 | 9.515349020787623 | 9.504447769985175 | 2.7869090909090843e-2 | 1.81818181843596e-6 | 5.573881818181814e-2 | 7.473983471074379e-2 | 6.999190082644527e-2 | 8.202456198347109e-2 | 7.766862280991699e-4 | 3.305785124891094e-12 | 3.1068158523057804e-3 | 7.850709089706979e-3 | 7.146110540195194e-3 | 8.271189009026299e-3 | 8.860422726770421e-2 | 8.453467063989303e-2 | 9.094607748015468e-2 |
| user_002 | Standing | 1.446309441607e12 | -3.25663 | -8.81307 | -0.383802 | -3.253146363636364 | -8.858357272727273 | -0.4465077272727273 | 10.6056389569 | 77.6702028249 | 0.147303975204 | 9.403358216988439 | 9.448580993474923 | 3.4836363636361334e-3 | 4.528727272727373e-2 | 6.270572727272733e-2 | 8.709090909090915e-2 | 6.745603305785056e-2 | 7.157333057851244e-2 | 1.2135722314047982e-5 | 2.050937071074471e-3 | 3.93200823280166e-3 | 1.3511471925469594e-2 | 6.41544745364378e-3 | 1.040376288128701e-2 | 0.11623885720992612 | 8.009648839770556e-2 | 0.10199883764674483 |
| user_002 | Standing | 1.446309441931e12 | -3.21831 | -9.08132 | -0.1922 | -3.1974081818181816 | -8.931516363636364 | -0.27580818181818184 | 10.357519256099998 | 82.47037294239999 | 3.694084e-2 | 9.63664013225045 | 9.492035489223916 | 2.0901818181818133e-2 | 0.14980363636363592 | 8.360818181818183e-2 | 7.60066115702478e-2 | 0.12478074380165212 | 0.1165442561983471 | 4.3688600330578305e-4 | 2.244112946776846e-2 | 6.990328066942151e-3 | 8.611949853042809e-3 | 2.0636293135762387e-2 | 1.9541015617250947e-2 | 9.280059187873108e-2 | 0.14365337843490625 | 0.13978918276193958 |
| user_002 | Standing | 1.446309441939e12 | -3.06503 | -9.00468 | -0.268841 | -3.1834736363636362 | -8.93500090909091 | -0.2618735454545455 | 9.3944089009 | 81.0842619024 | 7.2275483281e-2 | 9.515826095856365 | 9.490221315119124 | 0.11844363636363608 | 6.967909090909075e-2 | 6.967454545454499e-3 | 8.202380165289243e-2 | 0.12636454545454467 | 0.11686097520661157 | 1.4028894995041256e-2 | 4.855175709917333e-3 | 4.854542284297456e-5 | 9.639073259804641e-3 | 2.0829416150037368e-2 | 1.9544325647606317e-2 | 9.817878212630589e-2 | 0.14432399713851252 | 0.13980102162576036 |
| user_002 | Standing | 1.446309442045e12 | -2.95007 | -9.19628 | -0.15388 | -3.1625718181818185 | -9.039512727272728 | -0.13646172727272726 | 8.702913004900001 | 84.57156583839999 | 2.3679054399999996e-2 | 9.659097157483197 | 9.579298743119958 | 0.21250181818181835 | 0.1567672727272722 | 1.7418272727272727e-2 | 0.10640925619834686 | 9.691314049586824e-2 | 8.677491735537189e-2 | 4.515702273057858e-2 | 2.4575977798346943e-2 | 3.0339622480165287e-4 | 1.5083599588880496e-2 | 1.2350613757099936e-2 | 1.0845087555632604e-2 | 0.12281530681832983 | 0.11113331524389947 | 0.10413975012276822 |
| user_002 | Standing | 1.446309442287e12 | -2.87342 | -9.08132 | -0.268841 | -2.998838181818182 | -9.08480090909091 | -0.10162500000000002 | 8.2565424964 | 82.47037294239999 | 7.2275483281e-2 | 9.528860945678712 | 9.568464948931542 | 0.1254181818181821 | 3.4809090909107e-3 | 0.16721599999999998 | 0.12541347107438028 | 8.899380165289292e-2 | 7.854080991735538e-2 | 1.572972033057858e-2 | 1.2116728099184755e-5 | 2.796119065599999e-2 | 2.0858878497145062e-2 | 1.349019009534193e-2 | 1.0030882045673928e-2 | 0.14442603123102518 | 0.11614727760624409 | 0.10015429119949842 |
| user_002 | Standing | 1.446309442611e12 | -2.60518 | -9.11964 | -5.99e-4 | -2.8490390909090912 | -9.077834545454547 | -8.07229090909091e-2 | 6.7869628323999995 | 83.1678337296 | 3.5880100000000004e-7 | 9.484450269825922 | 9.517987482543768 | 0.2438590909090914 | 4.180545454545381e-2 | 8.012390909090909e-2 | 0.17006842975206615 | 6.872446280991747e-2 | 6.777323140495868e-2 | 5.946725621900851e-2 | 1.7476960297520049e-3 | 6.419840808008265e-3 | 4.2711685068820476e-2 | 7.717777571600379e-3 | 8.636367726251688e-3 | 0.206668055269363 | 8.785088258862503e-2 | 9.29320597331819e-2 |
| user_002 | Standing | 1.44630944274e12 | -2.41358 | -9.11964 | -5.99e-4 | -2.737560909090909 | -9.10222 | -7.375563636363637e-2 | 5.8253684164 | 83.1678337296 | 3.5880100000000004e-7 | 9.433620858652366 | 9.509199078564167 | 0.32398090909090893 | 1.7419999999999547e-2 | 7.315663636363637e-2 | 0.22264024793388434 | 7.537578512396675e-2 | 7.569057024793388e-2 | 0.1049636294553718 | 3.034563999999842e-4 | 5.351893444041323e-3 | 6.229692536341098e-2 | 8.963426320135297e-3 | 9.325901606570248e-3 | 0.24959352027528875 | 9.4675373356197e-2 | 9.657070780816639e-2 |
| user_002 | Standing | 1.446309444812e12 | -2.33694 | -9.2346 | 0.229323 | -2.2045618181818183 | -9.213698181818183 | 0.12481318181818181 | 5.461288563599999 | 85.27783716 | 5.2589038329e-2 | 9.528468647265887 | 9.475980846384282 | 0.1323781818181815 | 2.090181818181769e-2 | 0.10450981818181819 | 0.10704264462809908 | 4.3387107438016284e-2 | 7.157342975206611e-2 | 1.752398302148752e-2 | 4.368860033057645e-4 | 1.0922302096396696e-2 | 1.5613158380766323e-2 | 4.877457122764723e-3 | 7.206518220723518e-3 | 0.12495262454533047 | 6.983879382381059e-2 | 8.489121403728138e-2 |
| user_002 | Standing | 1.446309446561e12 | -1.8771 | -9.2346 | 0.229323 | -1.8561981818181819 | -9.27292 | 0.18403527272727274 | 3.52350441 | 85.27783716 | 5.2589038329e-2 | 9.426236290711634 | 9.45880943756542 | 2.0901818181818133e-2 | 3.83199999999988e-2 | 4.528772727272726e-2 | 4.243727272727282e-2 | 2.5968925619834528e-2 | 2.5336e-2 | 4.3688600330578305e-4 | 1.468422399999908e-3 | 2.050978241528924e-3 | 2.131493715326829e-3 | 9.642383002253733e-4 | 1.193745043338843e-3 | 4.6168102791070253e-2 | 3.1052186722119478e-2 | 3.4550615672355865e-2 |
| user_002 | Standing | 1.44630944695e12 | -1.72382 | -9.31124 | 7.6042e-2 | -1.8527145454545455 | -9.279887272727272 | 0.177068 | 2.9715553923999996 | 86.6991903376 | 5.782385764e-3 | 9.469769169085591 | 9.465045650120201 | 0.1288945454545456 | 3.135272727272742e-2 | 0.101026 | 4.845429752066124e-2 | 2.0901818181818174e-2 | 4.750462809917355e-2 | 1.661380384793392e-2 | 9.829935074380258e-4 | 1.0206252676000001e-2 | 3.42117965807664e-3 | 7.656537532682156e-4 | 3.369363647926372e-3 | 5.849085106302215e-2 | 2.7670449097696545e-2 | 5.804621992797095e-2 |
| user_002 | Standing | 1.446309447921e12 | -1.60885 | -9.27292 | 0.114362 | -1.755169090909091 | -9.30078909090909 | 0.15268227272727272 | 2.5883983225 | 85.98704532639998 | 1.3078667044000002e-2 | 9.412147593187433 | 9.466772391953667 | 0.146319090909091 | 2.7869090909090843e-2 | 3.832027272727272e-2 | 7.632619834710748e-2 | 2.9452561983471914e-2 | 4.085393388429751e-2 | 2.140927636446284e-2 | 7.766862280991699e-4 | 1.4684433018925611e-3 | 7.252263568970701e-3 | 1.1771650644628554e-3 | 2.70630033675432e-3 | 8.516022292696691e-2 | 3.430983917862127e-2 | 5.202211392046963e-2 |
| user_002 | Standing | 1.446309448245e12 | -1.64717 | -9.31124 | 0.229323 | -1.7168481818181818 | -9.304272727272725 | 0.18055163636363636 | 2.7131690089 | 86.6991903376 | 5.2589038329e-2 | 9.458591247370245 | 9.46344627332974 | 6.967818181818175e-2 | 6.967272727274931e-3 | 4.877136363636364e-2 | 6.080867768595046e-2 | 3.135272727272775e-2 | 3.927053719008264e-2 | 4.855049021487594e-3 | 4.854288925622906e-5 | 2.3786459109504136e-3 | 5.8103029906085716e-3 | 1.185991044327584e-3 | 2.272729891622089e-3 | 7.622534349288675e-2 | 3.4438220690500024e-2 | 4.767315692947226e-2 |
| user_002 | Standing | 1.446309448309e12 | -1.64717 | -9.27292 | 0.152682 | -1.7133645454545452 | -9.297305454545453 | 0.18055163636363636 | 2.7131690089 | 85.98704532639998 | 2.3311793124000002e-2 | 9.419316648697185 | 9.45597525917587 | 6.619454545454517e-2 | 2.4385454545454266e-2 | 2.7869636363636346e-2 | 5.859165289256197e-2 | 2.8819173553719324e-2 | 4.1804115702479334e-2 | 4.381717847933847e-3 | 5.946503933884161e-4 | 7.767166310413214e-4 | 5.462725905409464e-3 | 9.918194873027757e-4 | 2.343340494437265e-3 | 7.39102557525643e-2 | 3.14931657237372e-2 | 4.840806228757009e-2 |
| user_002 | Standing | 1.446309449022e12 | -1.60885 | -9.34956 | 0.152682 | -1.675042727272727 | -9.304272727272725 | 0.19100263636363637 | 2.5883983225 | 87.41427219360001 | 2.3311793124000002e-2 | 9.4882022696201 | 9.456044735618114 | 6.619272727272718e-2 | 4.528727272727551e-2 | 3.832063636363636e-2 | 3.3890826446281046e-2 | 3.040264462810007e-2 | 4.465419834710744e-2 | 4.381477143801641e-3 | 2.050937071074632e-3 | 1.4684711713140496e-3 | 1.5260826492111191e-3 | 1.2444631609316841e-3 | 2.862969195238167e-3 | 3.906510782285285e-2 | 3.5276949427801775e-2 | 5.3506721028653656e-2 |
| user_002 | Standing | 1.446309449799e12 | -1.76214 | -9.31124 | 0.114362 | -1.6994281818181818 | -9.297305454545453 | 0.15964972727272728 | 3.1051373796000004 | 86.6991903376 | 1.3078667044000002e-2 | 9.477204565917315 | 9.453145818693523 | 6.271181818181826e-2 | 1.393454545454631e-2 | 4.528772727272727e-2 | 3.64233057851239e-2 | 2.248528925619908e-2 | 6.333940495867768e-2 | 3.932772139669431e-3 | 1.9417155702481723e-4 | 2.0509782415289255e-3 | 2.163873705935382e-3 | 8.417778296018074e-4 | 5.90467196818332e-3 | 4.651745592716117e-2 | 2.9013407755756775e-2 | 7.684186338307603e-2 |
| user_002 | Standing | 1.446309454073e12 | -1.68549 | -9.27292 | 0.114362 | -1.7029127272727274 | -9.297305454545453 | 0.20493736363636364 | 2.8408765400999996 | 85.98704532639998 | 1.3078667044000002e-2 | 9.425550410111018 | 9.454579743223052 | 1.7422727272727423e-2 | 2.4385454545454266e-2 | 9.057536363636363e-2 | 6.144264462809928e-2 | 3.293619834710784e-2 | 2.94527438016529e-2 | 3.0355142561983993e-4 | 5.946503933884161e-4 | 8.203896497859504e-3 | 5.386659989331347e-3 | 1.8711077313298436e-3 | 1.596423004198347e-3 | 7.339386888106762e-2 | 4.3256302793117256e-2 | 3.995526253446906e-2 |
| user_002 | Standing | 1.446309454591e12 | -1.72382 | -9.31124 | 0.152682 | -1.7029136363636364 | -9.283370909090909 | 0.19796990909090909 | 2.9715553923999996 | 86.6991903376 | 2.3311793124000002e-2 | 9.470694669512053 | 9.440886438934198 | 2.0906363636363556e-2 | 2.7869090909090843e-2 | 4.528790909090907e-2 | 6.777768595041332e-2 | 3.958677685950414e-2 | 5.383851239669421e-2 | 4.370760404958644e-4 | 7.766862280991699e-4 | 2.0509947098264446e-3 | 6.0818297021788285e-3 | 2.042111091209604e-3 | 4.07988566014425e-3 | 7.798608659356378e-2 | 4.5189723292022976e-2 | 6.387398265447561e-2 |
| user_002 | Standing | 1.446309455109e12 | -1.80046 | -9.2346 | 0.152682 | -1.7307845454545456 | -9.279887272727272 | 0.19100245454545456 | 3.2416562115999996 | 85.27783716 | 2.3311793124000002e-2 | 9.409718654918647 | 9.44219314863774 | 6.967545454545432e-2 | 4.5287272727271954e-2 | 3.8320454545454546e-2 | 4.9409008264462866e-2 | 2.7552396694214953e-2 | 5.542198347107437e-2 | 4.8546689661156705e-3 | 2.05093707107431e-3 | 1.468457236570248e-3 | 3.2373997262960265e-3 | 9.653415477084801e-4 | 3.4808043690803896e-3 | 5.689815222215944e-2 | 3.1069946052551815e-2 | 5.8998342087556915e-2 |
| user_002 | Standing | 1.446309455692e12 | -1.68549 | -9.31124 | 0.191003 | -1.7307836363636364 | -9.29033818181818 | 0.19448636363636365 | 2.8408765400999996 | 86.6991903376 | 3.6482146009e-2 | 9.464488841121268 | 9.452370650821058 | 4.529363636363648e-2 | 2.0901818181819465e-2 | 3.4833636363636455e-3 | 3.420619834710752e-2 | 2.850247933884327e-2 | 3.483647107438016e-2 | 2.051513495041333e-3 | 4.368860033058388e-4 | 1.213382222314056e-5 | 1.5448672236664225e-3 | 1.5246880216378755e-3 | 2.060896010293764e-3 | 3.930479899028136e-2 | 3.904725370160974e-2 | 4.5397092531281824e-2 |
| user_002 | Walking | 1.446034820327e12 | -5.40257 | -10.92069 | 1.34061 | -4.641632857142858 | -9.152482857142857 | 2.2712485714285715 | 29.187762604899998 | 119.26147007610001 | 1.7972351721000002 | 12.257506592007202 | 10.563954019413515 | 0.7609371428571423 | 1.7682071428571433 | 0.9306385714285714 | 0.5499359251700676 | 0.7234843299319728 | 0.43905424829931977 | 0.579025335379591 | 3.126556500051022 | 0.8660881506306123 | 0.5896706225150046 | 0.9586520619214991 | 0.2611557858821868 | 0.7679001383741277 | 0.9791077887145516 | 0.5110340359332114 |
| user_002 | Walking | 1.446034820521e12 | -3.40991 | -6.89706 | 1.14901 | -4.567182 | -8.556329 | 1.992058 | 11.6274862081 | 47.5694366436 | 1.3202239801000002 | 7.779276755058918 | 9.955637276413874 | 1.1572719999999999 | 1.659269 | 0.843048 | 0.5713082503968251 | 0.9860597781746032 | 0.5245926488095238 | 1.3392784819839996 | 2.753173614361 | 0.710729930304 | 0.5780142333953865 | 1.4819664907472159 | 0.3771716530105871 | 0.7602724731274877 | 1.2173604604829318 | 0.6141430232532054 |
| user_002 | Walking | 1.446034820716e12 | -10.11596 | -7.28026 | 2.03038 | -5.548879090909091 | -8.071053636363636 | 1.3615148181818184 | 102.33264672159999 | 53.0021856676 | 4.1224429444 | 12.62763934128624 | 10.103360417173816 | 4.567080909090908 | 0.7907936363636363 | 0.6688651818181817 | 1.236847335071494 | 1.3437529388364158 | 0.8908252757772531 | 20.858228030182637 | 0.625354575313223 | 0.44738063144866924 | 3.4087432876190964 | 2.2522219045364613 | 1.248465140146506 | 1.8462782259505464 | 1.500740452089055 | 1.1173473677180727 |
| user_002 | Walking | 1.446034821299e12 | -4.25296 | -7.93171 | 2.03038 | -4.890467272727273 | -8.158144545454546 | 1.9189002727272728 | 18.0876687616 | 62.9120235241 | 4.1224429444 | 9.226165792467638 | 10.05839102559979 | 0.637507272727273 | 0.22643454545454578 | 0.11147972727272726 | 2.1731719834710748 | 0.9032194214876031 | 0.9364723223140494 | 0.4064155227801657 | 5.1272603375206754e-2 | 1.242772959280165e-2 | 6.413410629421864 | 1.2099726792833205 | 1.360956890081912 | 2.532471249475868 | 1.099987581422318 | 1.1666005700675413 |
| user_002 | Walking | 1.446034822463e12 | -2.75846 | -8.08499 | 2.6435 | -4.890467272727272 | -7.96306 | 1.1733959090909092 | 7.609101571599999 | 65.36706330009999 | 6.98809225 | 8.942273599130143 | 9.802555864984717 | 2.132007272727272 | 0.12192999999999987 | 1.4701040909090908 | 1.765585041322314 | 1.06378520661157 | 1.399166694214876 | 4.545455010961981 | 1.4866924899999969e-2 | 2.161206038107644 | 5.420495317378587 | 1.9482049458051847 | 3.2649849600306666 | 2.3281957214501077 | 1.3957811238891236 | 1.806926938210471 |
| user_002 | Walking | 1.446034822658e12 | -5.97737 | -8.73643 | 2.37526 | -4.810343636363636 | -8.436838181818182 | 2.040828272727273 | 35.7289521169 | 76.3252091449 | 5.6418600676 | 10.848779716143193 | 10.162650533220145 | 1.1670263636363636 | 0.2995918181818187 | 0.33443172727272685 | 1.6895772727272726 | 0.6666470247933883 | 1.0039288016528927 | 1.3619505334223139 | 8.97552575214879e-2 | 0.11184458020661955 | 5.158998482861532 | 1.165199367313524 | 1.2420907046891458 | 2.2713428809542453 | 1.0794440084198549 | 1.1144912313199893 |
| user_002 | Walking | 1.446034822981e12 | -5.01936 | -7.39522 | 1.37893 | -4.69189909090909 | -8.649341818181817 | 2.0408281818181817 | 25.193974809599997 | 54.6892788484 | 1.9014479449 | 9.043489459434339 | 10.113432651366857 | 0.3274609090909095 | 1.254121818181817 | 0.6618981818181817 | 1.132189256198347 | 0.6324439669421487 | 0.8351294297520663 | 0.10723064698264491 | 1.5728215348396666 | 0.43810920309421475 | 1.8307273614960182 | 0.8123552954916602 | 0.8564678106434943 | 1.353043739683244 | 0.9013075476726355 | 0.9254554611884325 |
| user_002 | Walking | 1.44603482311e12 | -3.06503 | -6.70546 | 0.612526 | -4.552551818181818 | -8.47515909090909 | 1.8770950909090909 | 9.3944089009 | 44.96319381160001 | 0.375188100676 | 7.398161313000414 | 9.872386410211492 | 1.4875218181818175 | 1.7696990909090893 | 1.2645690909090908 | 1.1888786776859503 | 0.9127201652892559 | 0.8566640413223141 | 2.2127211595669403 | 3.131834872364457 | 1.5991349856826442 | 1.9648463503924871 | 1.3822374690904575 | 0.9094341834552345 | 1.401729770816218 | 1.1756859568313545 | 0.9536425868506684 |
| user_002 | Walking | 1.446034824018e12 | -4.75112 | -10.61413 | 1.64717 | -3.5597072727272727 | -8.830492727272729 | 2.190625363636364 | 22.573141254400003 | 112.65975565689999 | 2.7131690089 | 11.745044313249737 | 9.854628189720001 | 1.1914127272727275 | 1.7836372727272707 | 0.5434553636363639 | 1.8286085123966946 | 0.7876243801652887 | 0.9497740661157027 | 1.4194642867074385 | 3.1813619206619763 | 0.29534373226513255 | 4.0277480513314075 | 1.1720520732890294 | 1.2082524051718002 | 2.0069250238440417 | 1.082613538290109 | 1.0992053516844795 |
| user_002 | Walking | 1.446034824601e12 | -5.86241 | -12.49182 | -0.460442 | -5.165676363636364 | -8.82003909090909 | 1.0584344545454545 | 34.3678510081 | 156.0455669124 | 0.21200683536400003 | 13.80671665371112 | 10.55427175573394 | 0.6967336363636356 | 3.671780909090911 | 1.5188764545454545 | 1.386814297520661 | 1.8362090082644626 | 1.0685353719008264 | 0.4854377600404948 | 13.481975044364477 | 2.30698568417257 | 3.6135870597748307 | 4.200258772052517 | 2.26876861299566 | 1.900943728723928 | 2.0494532861357238 | 1.5062432117674955 |
| user_002 | Walking | 1.44603482473e12 | -1.37893 | -7.93171 | 2.52854 | -4.876531818181817 | -8.51696 | 1.0758526363636365 | 1.9014479449 | 62.9120235241 | 6.3935145316 | 8.438423193974097 | 10.201872866422526 | 3.4976018181818174 | 0.5852499999999994 | 1.4526873636363635 | 1.4979750413223138 | 1.6639252066115702 | 1.1828621735537188 | 12.233218478548755 | 0.34251756249999926 | 2.1103005764687683 | 4.416518988749511 | 3.8865774131692716 | 2.4518580528002136 | 2.1015515669974674 | 1.9714404411924982 | 1.5658410049555522 |
| user_002 | Walking | 1.446034825378e12 | -4.21464 | -11.03565 | 1.60885 | -3.556223636363636 | -8.945453636363636 | 2.2533327272727273 | 17.7631903296 | 121.78557092250001 | 2.5883983225 | 11.922128986661738 | 9.95200182983105 | 0.658416363636364 | 2.0901963636363643 | 0.6444827272727274 | 1.5267971074380162 | 0.7100323966942147 | 0.9703596859504132 | 0.43351210790413275 | 4.36892083855868 | 0.4153579857528927 | 3.0702830704655892 | 0.8966544321941399 | 1.1600912409439053 | 1.7522223233555694 | 0.9469183872932978 | 1.0770753181388502 |
| user_002 | Walking | 1.446034825702e12 | -6.36057 | -6.70546 | -4.5224 | -4.221602727272727 | -8.569216363636365 | 1.100239 | 40.4568507249 | 44.96319381160001 | 20.45210176 | 10.289419142813651 | 9.871047406501587 | 2.138967272727273 | 1.863756363636364 | 5.622639 | 1.1765302479338846 | 1.406133223140496 | 1.3836488760330576 | 4.575180993798347 | 3.4735877829950432 | 31.614069324321004 | 1.7407863743604812 | 2.6318653634207365 | 3.892013150556863 | 1.319388636589114 | 1.6223024882618953 | 1.9728185802442308 |
| user_002 | Walking | 1.446034826673e12 | -5.05768 | -8.58315 | 0.191003 | -4.124059999999999 | -9.074347272727273 | 1.838777545454545 | 25.580126982400003 | 73.67046392249999 | 3.6482146009e-2 | 9.96428989195462 | 10.208070843567992 | 0.9336200000000012 | 0.4911972727272733 | 1.647774545454545 | 1.116356280991736 | 0.8408293388429752 | 1.0527011983471075 | 0.8716463044000022 | 0.24127476073471132 | 2.715160952647932 | 1.5012614522227659 | 1.4204436278609316 | 1.490610296004187 | 1.225259748878892 | 1.1918236563606763 | 1.2209055229640773 |
| user_002 | Walking | 1.446034826738e12 | -2.79678 | -6.9737 | 0.919089 | -4.11360909090909 | -9.015125454545453 | 1.7168492727272722 | 7.8219783684 | 48.63249169 | 0.8447245899210001 | 7.569623151010954 | 10.126277190348603 | 1.3168290909090903 | 2.041425454545453 | 0.7977602727272721 | 1.0641013223140499 | 0.9627571900826446 | 0.9700432892561982 | 1.7340388546644612 | 4.16741788646611 | 0.6364214527418915 | 1.3336033649902335 | 1.7547264561511644 | 1.2835722745172367 | 1.1548174595970713 | 1.3246608834532574 | 1.1329484871419515 |
| user_002 | Walking | 1.446034827904e12 | -3.29495 | -7.66346 | -0.11556 | -4.284309090909091 | -8.90364909090909 | 1.5426636363636368 | 10.856695502500001 | 58.728619171599995 | 1.3354113599999998e-2 | 8.342581662033641 | 10.132261341915948 | 0.9893590909090912 | 1.24018909090909 | 1.658223636363637 | 0.9675095867768597 | 1.0663202479338845 | 1.5090592644628098 | 0.9788314107644634 | 1.538068981209915 | 2.749705628195043 | 1.199261063500376 | 1.9615683920033804 | 2.4204171887038566 | 1.0951077862477172 | 1.400560027990011 | 1.55576900235988 |
| user_002 | Walking | 1.446034828033e12 | -2.6435 | -5.74745 | 0.152682 | -4.0752890909090915 | -8.624956363636365 | 1.2186831818181818 | 6.98809225 | 33.0331815025 | 2.3311793124000002e-2 | 6.328079135537418 | 9.750752081507738 | 1.4317890909090916 | 2.877506363636365 | 1.0660011818181818 | 1.0080471074380166 | 1.4938607438016531 | 1.4210179008264463 | 2.050020000846283 | 8.280042872767776 | 1.1363585196377604 | 1.3242157561663412 | 3.201710657197371 | 2.171397846632907 | 1.1507457391475935 | 1.7893324613378507 | 1.4735663699450077 |
| user_002 | Walking | 1.446034829328e12 | -6.09233 | -4.67448 | -2.6447 | -4.590870909090908 | -8.280071818181817 | 0.5149840909090906 | 37.11648482889999 | 21.850763270399998 | 6.994438089999999 | 8.121680010274968 | 9.7659346201661 | 1.5014590909090915 | 3.605591818181817 | 3.1596840909090904 | 1.0327486776859502 | 2.0521945454545447 | 1.4906926363636364 | 2.2543794016735554 | 13.00029235933966 | 9.983603554344006 | 1.479305683744628 | 6.29329290884192 | 2.7833947529780843 | 1.2162671103604783 | 2.5086436392684233 | 1.6683509082258696 |
| user_002 | Walking | 1.446034831531e12 | -5.40257 | -10.23092 | -0.843646 | -4.709316363636363 | -8.746883636363636 | 1.6854949090909093 | 29.187762604899998 | 104.67172404639999 | 0.711738573316 | 11.60048383579823 | 10.21751908316194 | 0.6932536363636368 | 1.4840363636363634 | 2.529140909090909 | 0.8420960330578509 | 1.0194471900826445 | 1.3370945454545453 | 0.48060060433140556 | 2.2023639285950405 | 6.39655373803719 | 1.1584009121630345 | 1.8322100748866275 | 2.1417668458588355 | 1.0762903475192158 | 1.3535915465481556 | 1.4634776547179789 |
| user_002 | Walking | 1.446034832179e12 | -3.02671 | -8.04667 | 2.18366 | -4.834728999999999 | -8.05015 | -4.936900000000008e-2 | 9.1609734241 | 64.7488980889 | 4.768370995600001 | 8.870075676599383 | 9.759479910601112 | 1.8080189999999994 | 3.4799999999997056e-3 | 2.233029 | 1.9616194876033055 | 1.479922231404958 | 2.0582124380165294 | 3.268932704360998 | 1.211039999999795e-5 | 4.9864185148410005 | 6.195976928875267 | 3.318297977098797 | 4.970532145289623 | 2.4891719363827134 | 1.821619602743338 | 2.2294690276587437 |
| user_002 | Walking | 1.446034832374e12 | -4.17632 | -8.31491 | 1.72382 | -4.866081727272727 | -8.025764545454546 | 0.5742078181818182 | 17.441648742399998 | 69.1377283081 | 2.9715553923999996 | 9.463135444602916 | 9.809356874918914 | 0.6897617272727272 | 0.28914545454545326 | 1.1496121818181817 | 1.8115047520661156 | 1.12585652892562 | 1.97998873553719 | 0.47577124041025615 | 8.360509388429678e-2 | 1.32160816858476 | 5.921246149061836 | 2.5081589970130724 | 4.669803684489687 | 2.4333610806992527 | 1.583716829806728 | 2.1609728560279713 |
| user_002 | Walking | 1.446034832892e12 | -2.75846 | -5.78577 | 0.191003 | -4.792922727272727 | -8.670242727272727 | 1.204750909090909 | 7.609101571599999 | 33.475134492900004 | 3.6482146009e-2 | 6.412543817433843 | 10.124198990245487 | 2.0344627272727274 | 2.884472727272727 | 1.013747909090909 | 0.8629973553719009 | 1.2927565289256195 | 1.334877553719008 | 4.139038588661984 | 8.320182914380164 | 1.0276848231861897 | 1.0852627263624255 | 2.697601858566792 | 2.3448212789449783 | 1.0417594378561807 | 1.6424377792071125 | 1.5312809275064385 |
| user_002 | Walking | 1.446034833085e12 | -9.15796 | -5.82409 | -2.98958 | -5.249281818181819 | -7.976992727272727 | 0.2711280909090909 | 83.86823136159998 | 33.9200243281 | 8.937588576400001 | 11.257257404274808 | 9.873436534261957 | 3.90867818181818 | 2.1529027272727266 | 3.260708090909091 | 1.2119984545454545 | 1.962570165289256 | 1.4925924214876034 | 15.277765129021475 | 4.634990153098344 | 10.63221725412001 | 2.480748909105307 | 4.826592534165364 | 3.071682909242764 | 1.5750393357327008 | 2.196950735488933 | 1.7526217245152373 |
| user_002 | Walking | 1.446034833215e12 | -6.62882 | -12.60678 | -0.1922 | -5.82757090909091 | -8.454254545454544 | -0.4465073636363636 | 43.9412545924 | 158.9309019684 | 3.694084e-2 | 14.24461643572055 | 10.675288823021814 | 0.8012490909090904 | 4.152525454545456 | 0.25430736363636364 | 1.6712099256198343 | 2.2244780165289257 | 1.5765175950413224 | 0.6420001056826438 | 17.243467650647947 | 6.467223519967769e-2 | 5.0900214959958845 | 6.285847138249138 | 3.7034431770774034 | 2.2561075984969965 | 2.5071591768870873 | 1.9244332093053798 |
| user_002 | Walking | 1.446034833539e12 | -4.86608 | -7.93171 | 1.80046 | -5.353793636363636 | -7.395220909090909 | 0.19797109090909082 | 23.678734566400003 | 62.9120235241 | 3.2416562115999996 | 9.477996323174008 | 9.547768671452733 | 0.48771363636363585 | 0.5364890909090905 | 1.6024889090909091 | 1.8650285123966943 | 1.6211712396694216 | 1.979673148760331 | 0.23786459109504082 | 0.28782054466446233 | 2.567970703759372 | 5.81993499195815 | 4.597404026959279 | 4.682051222243637 | 2.4124541429751885 | 2.144155784209552 | 2.163804802250803 |
| user_002 | Walking | 1.446034833993e12 | -5.13432 | -10.72909 | -1.03525 | -4.719765454545455 | -8.781721818181817 | 1.6053699999999997 | 26.361241862399996 | 115.11337222809999 | 1.0717425625 | 11.939277894956629 | 10.229185323152004 | 0.4145545454545445 | 1.9473681818181827 | 2.6406199999999997 | 1.0422495041322313 | 0.9972793388429756 | 1.484041785123967 | 0.17185547115702401 | 3.792242835557855 | 6.972873984399999 | 1.6845566543133723 | 1.870692309292413 | 2.9552987618498023 | 1.297904716962448 | 1.3677325430406388 | 1.7190982408954418 |
| user_002 | Walking | 1.446034834964e12 | -5.63249 | -7.62514 | 2.22198 | -5.1621927272727275 | -8.429870000000001 | 0.5324010909090909 | 31.724943600099998 | 58.1427600196 | 4.937195120399999 | 9.736780717470225 | 10.26159981256484 | 0.4702972727272723 | 0.8047300000000011 | 1.6895789090909088 | 1.654741322314049 | 0.785725123966942 | 1.952752859504132 | 0.22117952473471034 | 0.6475903729000017 | 2.8546768900448254 | 4.7563571161466545 | 1.769143344757024 | 4.538077592130551 | 2.1809074065963125 | 1.3300914798452863 | 2.13027641214246 |
| user_002 | Walking | 1.446034835029e12 | -5.47921 | -9.69444 | 1.72382 | -4.914852727272727 | -8.691144545454547 | 1.016631090909091 | 30.021742224100002 | 93.9821669136 | 2.9715553923999996 | 11.268339031556515 | 10.262562882771157 | 0.564357272727273 | 1.0032954545454533 | 0.7071889090909089 | 1.403284132231404 | 0.7594395041322314 | 1.6715264958677687 | 0.3184991312801656 | 1.0066017691115678 | 0.5001161531411898 | 3.776995825254394 | 1.7087985870458295 | 3.2703463178698895 | 1.9434494655777377 | 1.3072102306231501 | 1.8084098865771248 |
| user_003 | Jogging | 1.446047735631e12 | -6.13065 | 1.30349 | -1.41845 | -4.1150026 | -9.376380999999999 | -0.4642744 | 37.5848694225 | 1.6990861801000001 | 2.0120004025 | 6.426192963574935 | 11.322521733313769 | 2.0156473999999998 | 10.679870999999999 | 0.9541756 | 1.508743876269841 | 5.754433644047619 | 0.7064118632539683 | 4.062834441126759 | 114.05964457664096 | 0.91045107563536 | 3.8693449254455743 | 46.70436439108835 | 0.6896023926483791 | 1.9670650536892709 | 6.834059144541284 | 0.8304230203025318 |
| user_003 | Jogging | 1.446047736344e12 | -8.08499 | -2.91174 | -0.996927 | -4.580421818181818 | -9.544642454545455 | -0.9098348181818181 | 65.36706330009999 | 8.4782298276 | 0.993863443329 | 8.65096275399617 | 11.846538256703665 | 3.5045681818181817 | 6.632902454545455 | 8.70921818181819e-2 | 2.3809260991735535 | 7.526616578512397 | 1.4064498429752066 | 12.281998141012396 | 43.995394971515125 | 7.585048133851254e-3 | 7.642918822991907 | 62.51352266285617 | 3.284973150723658 | 2.764582938345657 | 7.906549352458136 | 1.8124494891509826 |
| user_003 | Jogging | 1.446047736408e12 | -6.82042 | 3.29615 | -1.49509 | -4.886984545454545 | -9.18234090909091 | -0.9690569999999998 | 46.51812897640001 | 10.864604822499999 | 2.2352941081 | 7.721271132851119 | 12.219725568362337 | 1.9334354545454557 | 12.478490909090908 | 0.5260330000000002 | 2.501587785123967 | 7.943073272727275 | 1.422918 | 3.738172656893393 | 155.71273536826445 | 0.2767107170890002 | 7.949350319215065 | 70.99922684871673 | 3.299315536658378 | 2.8194592246058576 | 8.426103894963362 | 1.8164018103543 |
| user_003 | Jogging | 1.44604773699e12 | -8.27659 | -19.08292 | 1.30229 | -4.524682272727272 | -8.534378181818182 | -0.2827757272727272 | 68.5019420281 | 364.15783572640004 | 1.6959592440999998 | 20.841202868323126 | 11.605676349077582 | 3.7519077272727284 | 10.54854181818182 | 1.585065727272727 | 2.701741016528926 | 7.784408900826446 | 1.9350175619834713 | 14.07681159396881 | 111.27173448993061 | 2.512433359774619 | 10.161299677030145 | 73.07180543440168 | 6.465091993559316 | 3.1876793560567136 | 8.548204807700953 | 2.5426545171452837 |
| user_003 | Jogging | 1.446047737443e12 | -4.21464 | -13.52647 | -1.22685 | -5.256252636363637 | -9.485420000000001 | 0.15964890909090904 | 17.7631903296 | 182.9653906609 | 1.5051609225 | 14.22089103794133 | 12.227766577150401 | 1.041612636363637 | 4.0410499999999985 | 1.386498909090909 | 2.7387941818181822 | 7.314746694214876 | 2.2672321322314053 | 1.0849568842324064 | 16.33008510249999 | 1.9223792249102807 | 10.071191419435943 | 64.77100283891653 | 6.9486060094820425 | 3.1735140490371148 | 8.048043416813588 | 2.6360208666628653 |
| user_003 | Jogging | 1.446047737832e12 | -4.25296 | -10.65245 | -1.49509 | -4.273859 | -9.380910000000002 | 9.694363636363644e-2 | 18.0876687616 | 113.4746910025 | 2.2352941081 | 11.5670935792964 | 11.598471252113294 | 2.0899e-2 | 1.2715399999999981 | 1.5920336363636365 | 2.398979024793389 | 7.37903520661157 | 2.1345355950413225 | 4.3676820100000004e-4 | 1.6168139715999952 | 2.5345710993132236 | 10.035068711816916 | 65.6950501683973 | 6.309138226020736 | 3.1678176576022987 | 8.105248310101135 | 2.5117997981568387 |
| user_003 | Jogging | 1.446047739644e12 | -4.75112 | -18.58475 | -1.61005 | -3.939425818181818 | -7.938673545454545 | -0.23400472727272728 | 22.573141254400003 | 345.3929325625 | 2.5922610025 | 19.2498918131869 | 10.502220457474333 | 0.8116941818181824 | 10.646076454545454 | 1.3760452727272727 | 2.0211578677685953 | 6.429262074380166 | 1.5872846033057852 | 0.6588474447974886 | 113.33894387602712 | 1.8935005925950743 | 6.706053344980842 | 61.9598330580618 | 3.6706176146924605 | 2.5896048627118464 | 7.871456857409675 | 1.915885595408155 |
| user_003 | Jogging | 1.446047740033e12 | -3.71647 | -18.85299 | 0.497565 | -4.106641636363637 | -8.123307272727272 | -0.9168033636363636 | 13.812149260900002 | 355.4352319400999 | 0.24757092922499999 | 19.22225148441839 | 10.77016093587484 | 0.39017163636363694 | 10.729682727272726 | 1.4143683636363635 | 1.899864553719008 | 6.932174727272727 | 1.5011428016528925 | 0.15223390582267815 | 115.12609142793468 | 2.0004378680554047 | 5.325225943114908 | 67.17508940760895 | 3.137830390156627 | 2.3076451077050186 | 8.19604108137636 | 1.771392218046762 |
| user_003 | Jogging | 1.446047740163e12 | -7.28026 | -6.20729 | -1.34181 | -4.381851636363636 | -9.060413636363636 | -0.8610647272727273 | 53.0021856676 | 38.530449144100004 | 1.8004540760999999 | 9.660905179526399 | 11.655872111868048 | 2.8984083636363644 | 2.8531236363636356 | 0.48074527272727263 | 2.1592396776859504 | 7.111743074380166 | 1.624021338842975 | 8.400771042397228 | 8.140314484376855 | 0.23111601724961975 | 6.355301068903384 | 68.16794305933205 | 3.3226541962235387 | 2.5209722467538955 | 8.256388015308634 | 1.8228149100288649 |
| user_003 | Jogging | 1.446047741586e12 | -5.70913 | -13.79471 | 2.18366 | -4.754604272727273 | -8.084986727272726 | -0.526633 | 32.5941653569 | 190.2940239841 | 4.768370995600001 | 15.088292161030022 | 11.192019763213258 | 0.9545257272727268 | 5.709723272727274 | 2.710293 | 2.1820410909090913 | 7.525347834710744 | 1.5670159504132235 | 0.911119364025528 | 32.60093985112345 | 7.345688145849 | 6.725637535457142 | 70.61233436629162 | 3.209251785542351 | 2.593383414664546 | 8.403114563439654 | 1.7914384682545899 |
| user_003 | Jogging | 1.446047741846e12 | -4.78944 | -17.01362 | 1.26397 | -4.639642454545454 | -8.078019545454545 | -0.5161817272727273 | 22.938735513599998 | 289.4632655044 | 1.5976201609 | 17.720034457610403 | 10.869660757908436 | 0.14979754545454593 | 8.935600454545455 | 1.7801517272727274 | 2.1741234710743806 | 7.074687818181817 | 1.5527638429752066 | 2.2439304624206756e-2 | 79.84495548327294 | 3.168940172112075 | 6.7387556156140995 | 62.02374722341651 | 3.395613878513215 | 2.595911326608461 | 7.875515679840686 | 1.8427191534558962 |
| user_003 | Jogging | 1.446047741911e12 | -9.92436 | -19.08292 | -5.13552 | -4.942721545454545 | -8.123307727272728 | -0.7461035454545454 | 98.4929214096 | 364.15783572640004 | 26.373565670399998 | 22.113894338320424 | 11.071813299428175 | 4.981638454545455 | 10.959612272727274 | 4.389416454545454 | 2.4198804876033058 | 7.12631 | 1.7234634462809917 | 24.816721691806027 | 120.11310116851428 | 19.266976811434386 | 8.522939200738632 | 63.12595207200314 | 4.573635273326528 | 2.9194073372413505 | 7.945184206297745 | 2.1386059181921593 |
| user_003 | Jogging | 1.446047742105e12 | -1.37893 | 1.03525 | -3.71767 | -5.291088181818181 | -8.461222363636365 | -1.038731181818182 | 1.9014479449 | 1.0717425625 | 13.8210702289 | 4.09808012809657 | 11.394511324828654 | 3.912158181818181 | 9.496472363636364 | 2.678938818181818 | 2.4556680578512395 | 7.01831676859504 | 1.8542590495867766 | 15.304981639566938 | 90.18298735330924 | 7.1767131915613955 | 7.66219816374371 | 59.86598085424357 | 4.899174334712138 | 2.7680675865563162 | 7.737310957577159 | 2.213407855482613 |
| user_003 | Jogging | 1.446047743076e12 | -8.27659 | -15.05928 | -0.728685 | -4.587387272727272 | -9.088283272727274 | 0.2606753636363636 | 68.5019420281 | 226.78191411839998 | 0.530981829225 | 17.19926853025224 | 11.999853736250367 | 3.689202727272728 | 5.970996727272725 | 0.9893603636363637 | 3.0143207438016533 | 7.571903148760328 | 1.6436561157024796 | 13.610216762916535 | 35.65280191710159 | 0.9788339291346778 | 13.052084914282272 | 67.81846212605052 | 3.7920228341500035 | 3.612766933291196 | 8.235196544469993 | 1.9473116941440072 |
| user_003 | Jogging | 1.44604774327e12 | -2.29862 | -1.18733 | 1.30229 | -4.639641818181818 | -8.698112363636362 | -2.150190909090909e-2 | 5.2836539044 | 1.4097525289 | 1.6959592440999998 | 2.8964401732816785 | 11.391576321113966 | 2.3410218181818183 | 7.510782363636362 | 1.323791909090909 | 2.8230356198347106 | 7.835078702479337 | 1.5758826859504131 | 5.480383153203307 | 56.41185171391101 | 1.7524250185745534 | 12.127020067467846 | 69.08815603044107 | 2.9333404785628705 | 3.4823871219994835 | 8.311928538578822 | 1.7126997631116991 |
| user_003 | Jogging | 1.446047743659e12 | -6.28393 | -8.54483 | -3.90927 | -4.674479090909092 | -8.569216363636365 | -0.5161813636363636 | 39.4877762449 | 73.01411972889998 | 15.282391932899998 | 11.304171261384 | 11.09202422756434 | 1.6094509090909082 | 2.438636363636526e-2 | 3.393088636363636 | 2.6973064462809915 | 7.171282801652893 | 1.653155925619835 | 2.590332228773551 | 5.946947314050379e-4 | 11.513050494220039 | 11.415731180268821 | 63.780162821143975 | 3.500501430448313 | 3.3787173868598157 | 7.9862483570913305 | 1.8709627015117947 |
| user_003 | Jogging | 1.446047744112e12 | -6.32225 | -19.58108 | -2.2615 | -4.158896818181819 | -8.503027454545455 | -0.5684371818181818 | 39.970845062500004 | 383.4186939664 | 5.114382249999999 | 20.700336260044182 | 11.354514333727545 | 2.1633531818181817 | 11.078052545454545 | 1.6930628181818181 | 2.7714127272727276 | 6.852686305785125 | 1.8821283719008262 | 4.680096989282851 | 122.72324819985192 | 2.86646170630976 | 12.191477094626935 | 62.46514659401501 | 5.505646912405573 | 3.4916295758036724 | 7.903489520080039 | 2.346411496819254 |
| user_003 | Jogging | 1.446047745795e12 | -1.11069 | 0.958607 | -2.68302 | -3.9951649999999996 | -9.670054818181818 | -9.465881818181819e-2 | 1.2336322760999998 | 0.918927380449 | 7.1985963204 | 3.0579659868855638 | 11.578644378470724 | 2.8844749999999997 | 10.628661818181818 | 2.5883611818181818 | 2.4385655123966945 | 6.973345719008264 | 1.5590983388429753 | 8.320196025624998 | 112.96845204527604 | 6.699613607543214 | 8.35464690663406 | 56.06450086026842 | 3.6200956105588475 | 2.8904406076987743 | 7.487623178303541 | 1.9026548847751785 |
| user_003 | Jogging | 1.446047746378e12 | -8.23827 | -17.32018 | -1.26517 | -4.576936636363637 | -9.276400272727273 | -0.4743760909090911 | 67.8690925929 | 299.98863523240004 | 1.6006551288999997 | 19.221300240987862 | 11.717208326426968 | 3.6613333636363627 | 8.043779727272728 | 0.7907939090909089 | 2.3983437355371895 | 6.6256129008264475 | 2.0360425371900828 | 13.405361999676762 | 64.70239230088373 | 0.6253550066552807 | 7.549598737169998 | 57.38207630334404 | 5.904934442457743 | 2.747653314588651 | 7.575095794994545 | 2.4300070869151273 |
| user_003 | Jogging | 1.446047746443e12 | -1.53221 | -8.12331 | 1.72382 | -4.806858181818182 | -9.607348454545455 | -0.282774090909091 | 2.3476674841 | 65.9881653561 | 2.9715553923999996 | 8.4443702093525 | 12.065932595475562 | 3.274648181818182 | 1.4840384545454555 | 2.0065940909090907 | 2.191540991735537 | 6.272179206611571 | 2.185207082644628 | 10.723320714685125 | 2.202370134569664 | 4.02641984567128 | 5.724745155916998 | 54.95898877838006 | 6.258809155594935 | 2.3926439676468787 | 7.413432995473828 | 2.5017612107463285 |
| user_003 | Jogging | 1.446047747867e12 | -8.54483 | -16.05561 | -3.25783 | -4.4793936363636355 | -8.879263090909092 | -0.8645490000000001 | 73.01411972889998 | 257.7826124721 | 10.613456308899998 | 18.477288451228443 | 11.952809629959383 | 4.065436363636364 | 7.17634690909091 | 2.3932809999999995 | 2.48765438016529 | 7.61592423140496 | 1.7449996942148758 | 16.52777282677686 | 51.499954959618655 | 5.727793944960998 | 9.127936903276487 | 72.93491111289995 | 4.231126364306643 | 3.0212475739794127 | 8.540193856868822 | 2.056970190427329 |
| user_003 | Jogging | 1.446047749615e12 | -5.24928 | -13.29655 | 1.22565 | -4.2076685454545455 | -8.370647363636364 | -0.29670981818181813 | 27.5549405184 | 176.7982419025 | 1.5022179224999999 | 14.347661842383935 | 11.022203270846463 | 1.0416114545454542 | 4.925902636363636 | 1.522359818181818 | 1.9812563471074378 | 7.141195123966941 | 1.5100093719008265 | 1.084954422240297 | 24.264516782934223 | 2.317579416014578 | 6.159065103561868 | 61.365181331507394 | 3.5171502368635177 | 2.4817463817968726 | 7.833593130327065 | 1.8754066857253968 |
| user_003 | Jogging | 1.446047750068e12 | -8.12331 | -17.09026 | -4.40743 | -4.510747636363636 | -8.475158181818182 | -0.7251994545454544 | 65.9881653561 | 292.0769868676 | 19.425439204899998 | 19.429117103682298 | 11.165998374535848 | 3.612562363636364 | 8.615101818181818 | 3.682230545454545 | 2.161772330578512 | 7.3682685950413225 | 1.7811014214876033 | 13.050606831161954 | 74.21997933763967 | 13.558821789878477 | 6.444378643861114 | 63.33591248530026 | 3.728135679427015 | 2.5385780751950717 | 7.958386299074723 | 1.9308380769570024 |
| user_003 | Jogging | 1.446047750974e12 | 3.8919e-2 | -0.842448 | -1.57173 | -3.8732365454545454 | -9.973134363636364 | -0.40122081818181826 | 1.514688561e-3 | 0.7097186327039999 | 2.4703351929000004 | 1.7836951853287601 | 11.766917856584998 | 3.9121555454545454 | 9.130686363636364 | 1.1705091818181819 | 2.1823563719008265 | 6.663935347107437 | 1.389666223140496 | 15.304961011830752 | 83.36943347109505 | 1.3700917447206695 | 6.276925041612132 | 57.1699286394013 | 3.746889708828522 | 2.5053792211184582 | 7.561079859345575 | 1.9356884327878086 |
| user_003 | Jogging | 1.446047751169e12 | -2.6435 | -19.58108 | 2.10702 | -3.7025366363636367 | -8.384583090909091 | -0.17478227272727276 | 6.98809225 | 383.4186939664 | 4.439533280399999 | 19.870740285575675 | 10.519545204986324 | 1.0590366363636368 | 11.196496909090909 | 2.2818022727272727 | 1.7560833388429749 | 6.586661157024793 | 1.5315473223140499 | 1.1215585971604058 | 125.36154303528228 | 5.206621611823347 | 4.519674473767474 | 57.41535496920656 | 4.058250858479553 | 2.1259526038384475 | 7.577292060439967 | 2.0145100790215853 |
| user_003 | Jogging | 1.446047751428e12 | -4.02303 | -2.79678 | 1.41725 | -4.137994636363636 | -8.785204 | -0.44999136363636366 | 16.1847703809 | 7.8219783684 | 2.0085975625 | 5.100524121284008 | 10.925348616610346 | 0.11496463636363607 | 5.988424 | 1.8672413636363636 | 1.6297226611570246 | 7.185217322314048 | 1.6297223140495867 | 1.3216867614223074e-2 | 35.861222003776 | 3.4865903100745865 | 5.189255505654557 | 62.7452396789755 | 3.459631785679336 | 2.277993745745268 | 7.921189284379935 | 1.86000854451783 |
| user_003 | Jumping | 1.446047778488e12 | 0.805325 | -10.92069 | -1.49509 | -3.045865625 | -10.45605625 | -2.8985725 | 0.6485483556249999 | 119.26147007610001 | 2.2352941081 | 11.051937049215626 | 11.40789343159099 | 3.8511906249999996 | 0.46463375000000084 | 1.4034825000000002 | 1.2694057180059524 | 2.5898877395833337 | 0.9794885 | 14.831669230087888 | 0.2158845216390633 | 1.9697631278062506 | 2.994031096828895 | 19.251323506625305 | 2.225648644605465 | 1.7303268757170984 | 4.387633018681633 | 1.491860799339357 |
| user_003 | Jumping | 1.446047779394e12 | -3.75479 | -19.19788 | -4.9056 | -2.7758821818181816 | -10.80573090909091 | -2.442647 | 14.098447944099998 | 368.55859649440004 | 24.064911359999996 | 20.167348754819013 | 11.820595137903576 | 0.9789078181818183 | 8.392149090909092 | 2.4629529999999997 | 1.600585561983471 | 5.376877752066116 | 1.2062982314049586 | 0.9582605164974878 | 70.42816636404629 | 6.066137480208998 | 3.2206154865366234 | 39.98863645901631 | 1.938698124881656 | 1.7946073349166451 | 6.323656889728942 | 1.3923714033553174 |
| user_003 | Jumping | 1.446047779718e12 | -0.574206 | -0.535886 | -1.18853 | -2.692273545454545 | -6.2699988181818185 | -1.8852610909090906 | 0.329712530436 | 0.287173804996 | 1.4126035609000003 | 1.4246016623365285 | 7.945104724247008 | 2.1180675454545446 | 5.734112818181819 | 0.6967310909090905 | 1.9156986446280988 | 6.127132917355372 | 1.4292522892561985 | 4.48621012710784 | 32.88004981163704 | 0.4854342130393713 | 4.538447186411323 | 48.73007696745106 | 2.676119181954217 | 2.130363158339752 | 6.980693158093333 | 1.6358848315068566 |
| user_003 | Jumping | 1.446047779783e12 | -1.72382 | -13.98632 | -4.10087 | -2.5633780909090906 | -7.144398818181816 | -2.104732 | 2.9715553923999996 | 195.61714714239997 | 16.817134756899996 | 14.676710710908626 | 8.766751727191796 | 0.8395580909090907 | 6.841921181818183 | 1.9961379999999997 | 1.8707269752066111 | 6.4061433140495865 | 1.5936175289256198 | 0.704857788010917 | 46.81188545821232 | 3.984566915043999 | 4.440687567006215 | 51.69169643753226 | 3.035135331393246 | 2.107293896685086 | 7.189693765212275 | 1.7421639794787533 |
| user_003 | Jumping | 1.446047779847e12 | -3.14167 | -19.58108 | -3.48775 | -2.5424762727272725 | -8.530896999999998 | -2.1987910909090904 | 9.8700903889 | 383.4186939664 | 12.1644000625 | 20.135868106883297 | 10.050840976414916 | 0.5991937272727275 | 11.050183000000002 | 1.2889589090909097 | 1.7627335867768592 | 7.189334537190083 | 1.6230705206611569 | 0.3590331228029837 | 122.10654433348905 | 1.661415069324828 | 4.182981353748214 | 62.25323570487753 | 3.1015204576431388 | 2.0452338139558064 | 7.890071970830021 | 1.7611134141909028 |
| user_003 | Jumping | 1.446047780236e12 | -4.02303 | -9.84772 | -0.383802 | -2.0791488181818183 | -7.405672454545454 | -1.9200985454545454 | 16.1847703809 | 96.97758919840001 | 0.147303975204 | 10.644701196111802 | 8.261996395467271 | 1.943881181818182 | 2.442047545454547 | 1.5362965454545454 | 1.4647220413223139 | 4.597488347107438 | 1.317141173553719 | 3.778674049026852 | 5.963596214260577 | 2.36020707557557 | 2.7032977327714196 | 31.28153946209859 | 2.150969664053501 | 1.6441708344242758 | 5.5929902075811455 | 1.4666184452861286 |
| user_003 | Jumping | 1.446047780559e12 | -1.8771 | 1.99325 | -1.68669 | -2.995352090909091 | -8.844426363636364 | -2.5123207272727273 | 3.52350441 | 3.9730455625 | 2.8449231561 | 3.215816090605929 | 10.297818465970156 | 1.118252090909091 | 10.837676363636364 | 0.8256307272727272 | 1.4400184710743802 | 4.84324438016529 | 1.320624966942149 | 1.2504877388225537 | 117.45522896292232 | 0.6816660978168925 | 2.8856324564354807 | 37.52954362327869 | 2.3031833074215906 | 1.6987149426656258 | 6.126136108778411 | 1.5176242312975865 |
| user_003 | Jumping | 1.446047782114e12 | -3.02671 | -6.93538 | -2.68302 | -2.1592739090909094 | -9.077831363636363 | -2.784047363636363 | 9.1609734241 | 48.0994957444 | 7.1985963204 | 8.028640326288132 | 10.440987932571106 | 0.8674360909090906 | 2.142451363636363 | 0.101027363636363 | 1.4593388842975203 | 6.418493917355372 | 1.5176108347107438 | 0.7524453718116441 | 4.590097845547312 | 1.020652820331392e-2 | 3.096061633576934 | 52.39564747111347 | 2.9478029008267157 | 1.7595629098094032 | 7.23848378261038 | 1.7169166842997117 |
| user_003 | Jumping | 1.446047783021e12 | -1.76214 | -12.22358 | -5.67201 | -2.2951368181818177 | -8.046666363636364 | -2.6028974545454546 | 3.1051373796000004 | 149.4159080164 | 32.171697440100004 | 13.590170817031698 | 9.340850426104161 | 0.5329968181818177 | 4.176913636363636 | 3.0691125454545456 | 1.147392132231405 | 5.408547049586778 | 1.5980530743801653 | 0.28408560819194156 | 17.446607525640495 | 9.41945181666648 | 2.104757384422643 | 39.50631767277085 | 3.717980372110848 | 1.450778199595873 | 6.285405131952183 | 1.9282065169765525 |
| user_003 | Jumping | 1.446047783474e12 | -2.8351 | -4.63616 | -3.75599 | -1.9258677272727274 | -6.823900909090909 | -2.5610926363636364 | 8.03779201 | 21.493979545600002 | 14.107460880100001 | 6.6059997302225195 | 8.171110182376047 | 0.9092322727272728 | 2.187740909090909 | 1.1948973636363638 | 1.018495512396694 | 4.917666958677686 | 1.2648870661157026 | 0.8267033257688018 | 4.786210285309917 | 1.4277797096251326 | 1.6454297082086164 | 35.01551621607166 | 2.5264965598920623 | 1.2827430406003442 | 5.917390997396713 | 1.589495693574557 |
| user_003 | Jumping | 1.446047783928e12 | -1.22565 | 0.345482 | -0.805325 | -2.3752596363636367 | -6.256064 | -2.17789 | 1.5022179224999999 | 0.119357812324 | 0.6485483556249999 | 1.5066930976310338 | 7.873185761106638 | 1.1496096363636368 | 6.601546 | 1.3725650000000003 | 1.3497614297520661 | 5.791750727272727 | 1.7095292975206613 | 1.3216023160201331 | 43.580409590116 | 1.8839346792250007 | 2.7612193103155254 | 44.96614526686418 | 5.186450031648668 | 1.6616917013440025 | 6.705680074896518 | 2.2773778851232986 |
| user_003 | Jumping | 1.446047785158e12 | -4.3296 | -19.38948 | -5.17384 | -2.127920545454545 | -8.778236090909092 | -3.0174524545454546 | 18.74543616 | 375.95193467039996 | 26.768620345600002 | 20.529636898299003 | 10.425059342349634 | 2.201679454545455 | 10.611243909090907 | 2.1563875454545456 | 1.4099344958677689 | 7.0499873553719 | 1.8989135371900827 | 4.847392420567572 | 112.59849729821889 | 4.65000724619148 | 2.826879506975875 | 61.40943646769882 | 5.966533630196475 | 1.6813326580352488 | 7.836417323477535 | 2.442648896218299 |
| user_003 | Jumping | 1.446047785741e12 | -5.67081 | -19.58108 | -6.59169 | -2.981417909090909 | -11.300410000000001 | -3.0104854545454547 | 32.158086056100004 | 383.4186939664 | 43.450377056099995 | 21.424919068192533 | 12.213006242934197 | 2.689392090909091 | 8.280669999999999 | 3.581204545454545 | 1.1746283057851243 | 4.157596 | 1.0412976611570246 | 7.232829818644373 | 68.56949564889999 | 12.825025996384296 | 1.990508293795564 | 32.482416710883314 | 2.1246119344821945 | 1.4108537464229112 | 5.699334760380664 | 1.457604862259383 |
| user_003 | Jumping | 1.446047786065e12 | -3.7722e-2 | 0.230521 | 0.382604 | -2.6016978181818184 | -7.217555818181818 | -2.3207198181818183 | 1.422949284e-3 | 5.3139931441e-2 | 0.146385820816 | 0.4482730212058272 | 8.746920955704528 | 2.5639758181818184 | 7.448076818181819 | 2.7033238181818184 | 1.274704181818182 | 5.073483123966943 | 1.6246536198347108 | 6.573971996221125 | 55.4738482895374 | 7.3079596659491255 | 2.612508774391919 | 37.036034654309766 | 4.703861187725261 | 1.6163257018286628 | 6.085723839799976 | 2.1688386725907627 |
| user_003 | Jumping | 1.44604778697e12 | -0.267643 | -8.92803 | -1.57173 | -2.608665363636364 | -10.115963636363636 | -3.3309809090909095 | 7.163277544900001e-2 | 79.7097196809 | 2.4703351929000004 | 9.069271616246201 | 11.16523610158163 | 2.341022363636364 | 1.1879336363636366 | 1.7592509090909094 | 1.3396285206611571 | 3.1194631487603317 | 1.5305941487603305 | 5.4803857070455875 | 1.4111863244041327 | 3.0949637611371914 | 2.725388990184014 | 16.98034189431018 | 4.198812068786995 | 1.6508752194469496 | 4.120721040583818 | 2.0491003071560443 |
| user_003 | Jumping | 1.446047787165e12 | -0.650847 | 0.383802 | -0.728685 | -2.3682926363636363 | -7.3185804545454545 | -2.703921636363636 | 0.42360181740899994 | 0.147303975204 | 0.530981829225 | 1.049708350847034 | 9.016998308071146 | 1.7174456363636363 | 7.702382454545455 | 1.9752366363636358 | 1.3750980991735537 | 5.015211049586777 | 1.9258318429752064 | 2.9496195138644956 | 59.32669547608966 | 3.90155976963313 | 3.0779925111574262 | 38.28173155279879 | 5.6355475934485995 | 1.7544208477892145 | 6.187223250602712 | 2.373930831647923 |
| user_003 | Jumping | 1.446047788071e12 | -0.382604 | -7.1653 | -1.83997 | -2.674855818181818 | -10.45388 | -3.4598772727272733 | 0.146385820816 | 51.34152409 | 3.3854896009 | 7.407658166500125 | 11.447674154518019 | 2.2922518181818177 | 3.2885799999999996 | 1.6199072727272732 | 0.9424898099173553 | 3.4890507603305783 | 1.4162686033057852 | 5.254418397957849 | 10.814758416399998 | 2.6240995722347122 | 1.5625571828485925 | 19.114791200640433 | 3.1594544490391656 | 1.2500228729301686 | 4.372046568901163 | 1.7774854286432746 |
| user_003 | Jumping | 1.446047788524e12 | -1.83878 | -13.06663 | -4.40743 | -2.284686363636364 | -9.189309181818182 | -3.341432 | 3.3811118884000004 | 170.7368195569 | 19.425439204899998 | 13.911986581728721 | 10.958216505306934 | 0.4459063636363638 | 3.8773208181818184 | 1.0659979999999996 | 1.2341684628099174 | 7.045554041322314 | 2.3818761983471077 | 0.1988324851314051 | 15.033616727106125 | 1.136351736003999 | 2.4039647392245826 | 56.751996420586586 | 6.585076787756369 | 1.5504724245289185 | 7.533392092582636 | 2.566140445836192 |
| user_003 | Jumping | 1.446047788978e12 | -4.98104 | -15.97897 | -4.67568 | -2.476287272727273 | -9.502837545454547 | -3.463361818181818 | 24.8107594816 | 255.3274822609 | 21.861983462399998 | 17.37815367652444 | 10.622998855950827 | 2.5047527272727272 | 6.4761324545454535 | 1.2123181818181816 | 0.6900824132231407 | 3.6885678677685947 | 1.235749785123967 | 6.2737862247801655 | 41.94029156881692 | 1.4697153739669417 | 0.9094025823986598 | 24.75910834947861 | 2.8048255856699282 | 0.9536260181007331 | 4.975852524892454 | 1.6747613518558184 |
| user_003 | Jumping | 1.446047789366e12 | -0.689167 | 0.230521 | -0.728685 | -2.040828272727273 | -7.03640390909091 | -2.648185181818182 | 0.47495115388899994 | 5.3139931441e-2 | 0.530981829225 | 1.029112683118326 | 8.566401817896816 | 1.3516612727272732 | 7.2669249090909105 | 1.919500181818182 | 0.9640250082644628 | 5.1925611652892565 | 1.7240976033057849 | 1.826988196190712 | 52.80819763436594 | 3.684480948000034 | 1.8265146617737733 | 38.92138095737842 | 4.682598069418348 | 1.3514860938144253 | 6.238700261863718 | 2.163931160970318 |
| user_003 | Jumping | 1.446047789431e12 | -1.14901 | -1.95374 | -2.72134 | -1.953736454545455 | -6.5486911818181825 | -2.7039233636363638 | 1.3202239801000002 | 3.8170999876000002 | 7.405691395600001 | 3.5416119724357156 | 8.169979126377969 | 0.8047264545454549 | 4.594951181818183 | 1.741663636363633e-2 | 1.0083625371900826 | 5.557712355371901 | 1.6746931487603307 | 0.647584666645298 | 21.113576363292314 | 3.0333922222313927e-4 | 1.8762498455014827 | 40.81039543439705 | 4.654028349160952 | 1.3697626967841847 | 6.388301451434258 | 2.1573197141733425 |
| user_003 | Jumping | 1.446047789495e12 | -3.90807 | -17.74171 | -7.58802 | -2.1139846363636363 | -7.62514390909091 | -3.205570636363637 | 15.2730111249 | 314.7682737241 | 57.578047520400006 | 19.688050496923253 | 9.358800088769957 | 1.7940853636363636 | 10.116566090909092 | 4.382449363636363 | 1.1537262148760332 | 6.411527363636364 | 2.0167259669421482 | 3.2187422920142232 | 102.34490947173167 | 19.205862424836763 | 2.1654029518886837 | 50.06674660371802 | 6.36506042608091 | 1.4715308192113015 | 7.0757859354080255 | 2.5229071378235286 |
| user_003 | Jumping | 1.44604778969e12 | -2.79678 | -7.7401 | -2.37646 | -2.023410090909091 | -8.565733909090909 | -3.4076233636363638 | 7.8219783684 | 59.90914801 | 5.647562131599999 | 8.566136148229258 | 10.261278348407382 | 0.773369909090909 | 0.8256339090909091 | 1.031163363636364 | 1.00804579338843 | 6.803598520661157 | 2.0537784710743803 | 0.5981010162872809 | 0.6816713518407355 | 1.06329788250586 | 1.7002829353981377 | 55.666831209060064 | 6.432113881496044 | 1.303948977298628 | 7.46102078867631 | 2.5361612491117445 |
| user_003 | Jumping | 1.446047790078e12 | -5.51753 | -19.58108 | -7.24314 | -2.786332727272727 | -10.19608909090909 | -3.6131590909090905 | 30.4431373009 | 383.4186939664 | 52.4630770596 | 21.594557377424987 | 11.293414580744383 | 2.7311972727272726 | 9.38499090909091 | 3.62998090909091 | 0.8785175041322314 | 4.163930198347109 | 1.2959231239669422 | 7.459438542552892 | 88.07805436371902 | 13.176761400364468 | 1.3735754555330413 | 30.66790812930183 | 3.5983363200454987 | 1.171996354743922 | 5.537861331714783 | 1.8969281272745941 |
| user_003 | Jumping | 1.446047790984e12 | -3.94639 | -5.36425 | -3.75599 | -2.120953181818182 | -7.109561 | -3.2508581818181814 | 15.5739940321 | 28.7751780625 | 14.107460880100001 | 7.645693753656368 | 8.673083640943078 | 1.8254368181818181 | 1.745311 | 0.5051318181818187 | 1.195847074380165 | 4.592418834710744 | 1.636056132231405 | 3.33221957717376 | 3.046110486721 | 0.25515815373967 | 2.4076508899487554 | 32.64328227998147 | 4.830573809957643 | 1.551660687762874 | 5.713429992568516 | 2.1978566399921635 |
| user_003 | Jumping | 1.446047791114e12 | -7.70178 | -19.58108 | -7.5497 | -2.946581363636364 | -10.105513 | -4.309891818181819 | 59.317415168400004 | 383.4186939664 | 56.997970089999995 | 22.354732814882848 | 11.61773381092931 | 4.7551986363636365 | 9.475567 | 3.239808181818181 | 1.3019401157024795 | 4.772619710743801 | 1.4381207768595041 | 22.611914071274587 | 89.786369971489 | 10.496357054976027 | 3.343503517246846 | 35.837988853437764 | 4.02036337652106 | 1.8285249566923734 | 5.9864838472543935 | 2.0050843813967183 |
| user_003 | Jumping | 1.446047791568e12 | -2.87342 | -15.97897 | -6.28513 | -2.3055863636363636 | -6.6601691818181825 | -3.212538181818182 | 8.2565424964 | 255.3274822609 | 39.5028591169 | 17.409390680727455 | 8.421526987651102 | 0.5678336363636363 | 9.318800818181817 | 3.0725918181818175 | 1.446669702479339 | 6.484367231404959 | 2.031925909090909 | 0.3224350385859503 | 86.8400486889461 | 9.440820481157846 | 3.813957346986096 | 49.52847432526871 | 5.995280938105451 | 1.9529355716423664 | 7.037646930989697 | 2.448526278826807 |
| user_003 | Jumping | 1.446047791827e12 | -2.29862 | -6.55217 | -2.49142 | -1.9641881818181817 | -7.280260090909092 | -3.181184545454545 | 5.2836539044 | 42.930931708900005 | 6.207173616400001 | 7.377110493255472 | 8.788531724429772 | 0.33443181818181844 | 0.7280900909090917 | 0.6897645454545449 | 1.1261739999999998 | 6.306384099173553 | 2.2213108677685947 | 0.11184464101239687 | 0.5301151804800094 | 0.47577512816611495 | 1.9109248881675382 | 50.99588928979224 | 6.629910003092078 | 1.3823620684059361 | 7.141140615461387 | 2.5748611619060315 |
| user_003 | Jumping | 1.446047792344e12 | -3.10335 | 3.14286 | -1.26517 | -2.953548181818182 | -8.90713090909091 | -3.501681181818181 | 9.6307812225 | 9.877568979600001 | 1.6006551288999997 | 4.594453757629953 | 10.941628346937778 | 0.14980181818181793 | 12.04999090909091 | 2.236511181818181 | 0.999812826446281 | 3.8237974793388427 | 1.812455851239669 | 2.2440584730578434e-2 | 145.2022809091736 | 5.001982266397757 | 1.8885143120312426 | 26.270304023665425 | 5.8452978576593555 | 1.374232262767558 | 5.12545646978544 | 2.4177050807861895 |
| user_003 | Jumping | 1.446047793057e12 | -3.40991 | -4.5212 | -3.18118 | -2.462351909090909 | -7.2071046363636375 | -3.024419818181818 | 11.6274862081 | 20.441249440000004 | 10.1199061924 | 6.495278426711207 | 8.674293689934707 | 0.9475580909090908 | 2.685904636363637 | 0.15676018181818208 | 1.0726530165289256 | 4.93793579338843 | 1.4678901570247935 | 0.8978663356472808 | 7.214083715639682 | 2.4573754603669503e-2 | 1.72976303665992 | 39.52732881159241 | 3.1747400768519607 | 1.3152045607660885 | 6.287076332572431 | 1.7817800304336 |
| user_003 | Jumping | 1.446047793186e12 | -4.7128 | -14.7144 | -2.75966 | -3.2113400000000003 | -8.840943 | -3.634060909090908 | 22.21048384 | 216.51356736 | 7.615723315599999 | 15.695215019731332 | 10.612367757126883 | 1.5014599999999994 | 5.873457 | 0.8744009090909084 | 0.7952270578512397 | 4.540798404958678 | 1.071702603305785 | 2.254382131599998 | 34.497497130849 | 0.764576949819007 | 0.7830567086205332 | 34.99927923490504 | 1.741114685615601 | 0.8849049150165984 | 5.916018867017332 | 1.3195130486719717 |
| user_003 | Jumping | 1.446047793575e12 | -0.995729 | 0.383802 | -0.537083 | -2.7410467272727272 | -6.395409181818182 | -2.8885566363636364 | 0.991476241441 | 0.147303975204 | 0.28845814888899995 | 1.1946708189011732 | 8.27717575135295 | 1.7453177272727274 | 6.779211181818182 | 2.3514736363636364 | 1.2639381239669418 | 5.466502917355373 | 1.8156223140495868 | 3.0461339691324385 | 45.957704247688675 | 5.529428262513223 | 1.9598808640562646 | 39.03616815904957 | 4.99167780265026 | 1.3999574508020822 | 6.247893097600948 | 2.2342063026162693 |
| user_003 | Jumping | 1.446047794481e12 | -1.95374 | 3.41111 | -1.07357 | -2.643502818181818 | -9.126602727272727 | -3.428524545454545 | 3.8170999876000002 | 11.635671432099999 | 1.1525525448999998 | 4.0749630629737 | 10.79227622301754 | 0.6897628181818181 | 12.537712727272726 | 2.354954545454545 | 1.1131894545454544 | 3.9501590082644626 | 1.6290873471074379 | 0.4757727453461238 | 157.19424043161652 | 5.545810911157022 | 1.8705564207425525 | 31.301814921364322 | 3.736381640676486 | 1.3676828655585886 | 5.594802491720715 | 1.932972229670278 |
| user_003 | Sitting | 1.44604752743e12 | 0.460442 | -0.689167 | 9.38788 | 0.46044236363636365 | -0.657814 | 9.443618181818183 | 0.21200683536400003 | 0.47495115388899994 | 88.13229089439999 | 9.424396473178163 | 9.478071981030691 | 3.6363636363168084e-7 | 3.1352999999999964e-2 | 5.573818181818346e-2 | 3.4032538370720185e-2 | 4.40547175127902e-2 | 2.419795146267877e-2 | 1.3223140495527202e-13 | 9.830106089999977e-4 | 3.1067449123968775e-3 | 2.3483069980241396e-3 | 3.906631446102497e-3 | 8.135460878653428e-4 | 4.845933344593319e-2 | 6.250305149432703e-2 | 2.8522729320058816e-2 |
| user_003 | Sitting | 1.446047527559e12 | 0.460442 | -0.612526 | 9.50284 | 0.453475 | -0.6578139090909091 | 9.44710181818182 | 0.21200683536400003 | 0.375188100676 | 90.30396806560002 | 9.53368569870226 | 9.481220909788886 | 6.967000000000001e-3 | 4.528790909090907e-2 | 5.573818181818169e-2 | 3.4032455037386854e-2 | 5.070537866981499e-2 | 2.7628805457168906e-2 | 4.8539089000000014e-5 | 2.0509947098264446e-3 | 3.1067449123966797e-3 | 2.308586652947751e-3 | 4.1636947314954344e-3 | 1.065423617408702e-3 | 4.804775388036106e-2 | 6.452669781954935e-2 | 3.264082746207121e-2 |
| user_003 | Sitting | 1.446047528402e12 | 0.422122 | -0.689167 | 9.46452 | 0.4569586363636363 | -0.6752324545454544 | 9.457552727272725 | 0.178186982884 | 0.47495115388899994 | 89.5771388304 | 9.498961888920968 | 9.492741447006507 | 3.483663636363632e-2 | 1.3934545454545533e-2 | 6.967272727274931e-3 | 3.166966115702479e-2 | 2.5652355371900804e-2 | 2.565223140495896e-2 | 1.2135912331322283e-3 | 1.9417155702479557e-4 | 4.854288925622906e-5 | 1.4210099365206607e-3 | 7.910343762411707e-4 | 1.09331825574758e-3 | 3.769628544725144e-2 | 2.8125333353423043e-2 | 3.3065363384478026e-2 |
| user_003 | Sitting | 1.446047529049e12 | 0.460442 | -0.650847 | 9.46452 | 0.4639258181818182 | -0.657814 | 9.436650909090908 | 0.21200683536400003 | 0.42360181740899994 | 89.5771388304 | 9.49803913885245 | 9.471062812864579 | 3.483818181818199e-3 | 6.9670000000000565e-3 | 2.786909090909262e-2 | 2.0901991735537175e-2 | 3.3886504132231404e-2 | 2.565223140495896e-2 | 1.2136989123967062e-5 | 4.853908900000079e-5 | 7.766862280992689e-4 | 1.0150072620518401e-3 | 1.7045471308392188e-3 | 1.0182974268970488e-3 | 3.1859178615460886e-2 | 4.128616149315917e-2 | 3.191077289720587e-2 |
| user_003 | Sitting | 1.446047529689e12 | 0.383802 | -0.689167 | 9.50284 | 0.45347518181818175 | -0.7031015454545454 | 9.45406909090909 | 0.147303975204 | 0.47495115388899994 | 90.30396806560002 | 9.53552427476817 | 9.4913909847906 | 6.967318181818177e-2 | 1.3934545454545422e-2 | 4.877090909091031e-2 | 4.687115702479338e-2 | 4.972128099173549e-2 | 4.6237355371901406e-2 | 4.854352264669415e-3 | 1.9417155702479248e-4 | 2.378601573553838e-3 | 3.2568331531720477e-3 | 3.7676414018760294e-3 | 2.8993343855747643e-3 | 5.7068670504682756e-2 | 6.138111600383321e-2 | 5.3845467641898744e-2 |
| user_003 | Sitting | 1.44604752981e12 | 0.422122 | -0.765807 | 9.54116 | 0.4325731818181817 | -0.710068909090909 | 9.499356363636364 | 0.178186982884 | 0.586460361249 | 91.0337341456 | 9.58114719069345 | 9.536678617258826 | 1.045118181818172e-2 | 5.573809090909099e-2 | 4.180363636363538e-2 | 8.170777685950412e-2 | 7.790738016528927e-2 | 6.587239669421473e-2 | 1.0922720139669217e-4 | 3.1067347781900912e-3 | 1.747544013223058e-3 | 9.461612793580014e-3 | 9.708744318026302e-3 | 7.735971351465028e-3 | 9.72708219024596e-2 | 9.853296056663628e-2 | 8.795437084912283e-2 |
| user_003 | Sitting | 1.446047529883e12 | 0.460442 | -0.650847 | 9.46452 | 0.47786072727272727 | -0.6926505454545453 | 9.454069090909089 | 0.21200683536400003 | 0.42360181740899994 | 89.5771388304 | 9.49803913885245 | 9.492320128700781 | 1.7418727272727252e-2 | 4.180354545454534e-2 | 1.0450909090911509e-2 | 5.130488429752066e-2 | 9.279201652892563e-2 | 6.17553719008262e-2 | 3.034120598016522e-4 | 1.7475364125702387e-3 | 1.0922150082649681e-4 | 3.8283135643719006e-3 | 1.240843196444929e-2 | 5.813010988429641e-3 | 6.1873367165299e-2 | 0.11139314146054635 | 7.624310453037468e-2 |
| user_003 | Sitting | 1.446047529891e12 | 0.575403 | -0.765807 | 9.50284 | 0.49876263636363627 | -0.678715818181818 | 9.44710181818182 | 0.331088612409 | 0.586460361249 | 90.30396806560002 | 9.550995604608874 | 9.485168028171538 | 7.664036363636373e-2 | 8.709118181818198e-2 | 5.573818181818169e-2 | 5.130490082644628e-2 | 8.139087603305785e-2 | 5.8588429752066e-2 | 5.873745338314064e-3 | 7.584873950487632e-3 | 3.1067449123966797e-3 | 3.828316097934638e-3 | 8.9927083140571e-3 | 5.34964704552959e-3 | 6.187338763907014e-2 | 9.482989145863818e-2 | 7.314128140475522e-2 |
| user_003 | Sitting | 1.44604753002e12 | 0.537083 | -0.689167 | 9.50284 | 0.4778606363636364 | -0.6403956363636364 | 9.502839999999999 | 0.28845814888899995 | 0.47495115388899994 | 90.30396806560002 | 9.542922894395511 | 9.537381176494081 | 5.922236363636357e-2 | 4.8771363636363585e-2 | 1.7763568394002505e-15 | 8.519144628099172e-2 | 8.424148760330576e-2 | 4.687074380165254e-2 | 3.5072883546776786e-3 | 2.3786459109504084e-3 | 3.1554436208840472e-30 | 1.0087168569314048e-2 | 1.018977686158527e-2 | 2.9942136691209083e-3 | 0.10043489716883294 | 0.10094442461862502 | 5.471940852312741e-2 |
| user_003 | Sitting | 1.446047530142e12 | 0.575403 | -0.765807 | 9.46452 | 0.3629000909090909 | -0.6891668181818181 | 9.447101818181817 | 0.331088612409 | 0.586460361249 | 89.5771388304 | 9.512869588302891 | 9.479941573052752 | 0.21250290909090908 | 7.664018181818189e-2 | 1.7418181818182887e-2 | 0.10039261983471072 | 6.618949586776866e-2 | 7.47398347107437e-2 | 4.515748637209917e-2 | 5.873717469123977e-3 | 3.0339305785127694e-4 | 1.3279947553097667e-2 | 6.071256061773864e-3 | 7.888219504132201e-3 | 0.11523865476955927 | 7.791826526414627e-2 | 8.881564898221597e-2 |
| user_003 | Sitting | 1.446047530158e12 | 0.498763 | -0.535886 | 9.50284 | 0.3698673636363637 | -0.6647811818181817 | 9.461036363636364 | 0.24876453016900002 | 0.287173804996 | 90.30396806560002 | 9.530997135702277 | 9.492622848471019 | 0.12889563636363632 | 0.12889518181818171 | 4.180363636363715e-2 | 9.849253719008265e-2 | 8.202431404958684e-2 | 7.125619834710778e-2 | 1.6614085073586766e-2 | 1.6613967895942123e-2 | 1.7475440132232066e-3 | 1.29092732124846e-2 | 8.401342212178822e-3 | 7.35314447483098e-3 | 0.11361898262387583 | 9.165883597438286e-2 | 8.575047798602047e-2 |
| user_003 | Sitting | 1.446047530198e12 | 0.422122 | -0.535886 | 9.57948 | 0.43257318181818183 | -0.6403957272727271 | 9.44361818181818 | 0.178186982884 | 0.287173804996 | 91.7664370704 | 9.603738743753913 | 9.47623788966848 | 1.0451181818181832e-2 | 0.10450972727272712 | 0.13586181818181942 | 9.057513223140498e-2 | 7.094005785123968e-2 | 5.7955041322314375e-2 | 1.0922720139669449e-4 | 1.0922283094619801e-2 | 1.8458433639669758e-2 | 1.2908162652194589e-2 | 7.0321907231382375e-3 | 4.926000012021046e-3 | 0.11361409530597244 | 8.385815835765914e-2 | 7.018546866710407e-2 |
| user_003 | Sitting | 1.446047530247e12 | 0.307161 | -0.880768 | 9.38788 | 0.46740963636363625 | -0.6264609999999999 | 9.457552727272729 | 9.434787992100001e-2 | 0.775752269824 | 88.13229089439999 | 9.43410785629171 | 9.490591922281 | 0.16024863636363623 | 0.25430700000000006 | 6.967272727272977e-2 | 7.505712396694214e-2 | 6.048910743801654e-2 | 5.067107438016582e-2 | 2.5679625456404915e-2 | 6.467205024900004e-2 | 4.854288925620183e-3 | 9.283960639918853e-3 | 9.24644060953268e-3 | 4.152623526371195e-3 | 9.635331151506342e-2 | 9.615841413798731e-2 | 6.444085293019634e-2 |
| user_003 | Sitting | 1.446047530328e12 | 0.345482 | -0.574206 | 9.4262 | 0.39773645454545453 | -0.6473628181818181 | 9.429683636363636 | 0.119357812324 | 0.329712530436 | 88.85324643999999 | 9.449990305961165 | 9.461319720505479 | 5.225445454545452e-2 | 7.315681818181807e-2 | 3.4836363636365775e-3 | 8.392447107438017e-2 | 8.550806611570248e-2 | 5.320462809917343e-2 | 2.7305280198429726e-3 | 5.351920046487588e-3 | 1.2135722314051077e-5 | 9.723052447205857e-3 | 1.2296954448760333e-2 | 5.915613004357611e-3 | 9.86055396375166e-2 | 0.1108916338086888 | 7.691302233274681e-2 |
| user_003 | Sitting | 1.446047530441e12 | 0.498763 | -0.612526 | 9.46452 | 0.5057300909090908 | -0.657813909090909 | 9.429683636363636 | 0.24876453016900002 | 0.375188100676 | 89.5771388304 | 9.49742551754132 | 9.466509525966101 | 6.967090909090812e-3 | 4.528790909090896e-2 | 3.4836363636364e-2 | 6.492278512396693e-2 | 5.3521900826446235e-2 | 4.402049586776839e-2 | 4.8540355735535836e-5 | 2.0509947098264346e-3 | 1.213572231404984e-3 | 5.985194005758828e-3 | 4.486975113920357e-3 | 4.422919159729566e-3 | 7.736403560931157e-2 | 6.698488720540147e-2 | 6.650503108584768e-2 |
| user_003 | Sitting | 1.446047530514e12 | 0.498763 | -0.880768 | 9.31124 | 0.4499914545454546 | -0.7031015454545453 | 9.440134545454544 | 0.24876453016900002 | 0.775752269824 | 86.6991903376 | 9.366093483282825 | 9.477717120133109 | 4.877154545454543e-2 | 0.1776664545454547 | 0.12889454545454448 | 6.2389223140495845e-2 | 6.713966942148762e-2 | 6.650578512396682e-2 | 2.378663646024791e-3 | 3.156536907075212e-2 | 1.6613803847933633e-2 | 6.276472416003001e-3 | 7.296975301380921e-3 | 6.55549654455295e-3 | 7.922419085104626e-2 | 8.542233490944227e-2 | 8.096602092577447e-2 |
| user_003 | Sitting | 1.446047530659e12 | 0.383802 | -0.727487 | 9.54116 | 0.4465077272727273 | -0.6821995454545454 | 9.461036363636365 | 0.147303975204 | 0.529237335169 | 91.0337341456 | 9.576548201516713 | 9.496631596516915 | 6.270572727272733e-2 | 4.5287454545454575e-2 | 8.012363636363418e-2 | 5.9222214876033095e-2 | 6.08056611570248e-2 | 6.428892561983478e-2 | 3.93200823280166e-3 | 2.050953539206614e-3 | 6.419797104131881e-3 | 4.411940753462063e-3 | 4.693273525742299e-3 | 5.314343126070618e-3 | 6.642244164032261e-2 | 6.850747058345023e-2 | 7.289954132963128e-2 |
| user_003 | Sitting | 1.446047531194e12 | 0.575403 | -0.612526 | 9.46452 | 0.4360567272727273 | -0.675232090909091 | 9.485421818181818 | 0.331088612409 | 0.375188100676 | 89.5771388304 | 9.501758550051933 | 9.52033802670179 | 0.13934627272727268 | 6.270609090909096e-2 | 2.090181818181769e-2 | 0.10197616528925618 | 5.8272214876033075e-2 | 4.9404297520661444e-2 | 1.9417383722983458e-2 | 3.93205383709918e-3 | 4.368860033057645e-4 | 1.2546293734994739e-2 | 5.233893928461308e-3 | 3.038343568444816e-3 | 0.11201023942030808 | 7.23456559059444e-2 | 5.512117168969484e-2 |
| user_003 | Sitting | 1.446047531283e12 | 0.307161 | -0.689167 | 9.50284 | 0.4325730909090908 | -0.6508465454545456 | 9.44710181818182 | 9.434787992100001e-2 | 0.47495115388899994 | 90.30396806560002 | 9.532747090918232 | 9.480348281329318 | 0.12541209090909078 | 3.832045454545441e-2 | 5.573818181818169e-2 | 8.709162809917352e-2 | 7.284034710743799e-2 | 5.162115702479333e-2 | 1.572819254619005e-2 | 1.4684572365702374e-3 | 3.1067449123966797e-3 | 1.0321058878268211e-2 | 7.570614707637863e-3 | 3.4983977688955513e-3 | 0.1015926123213111 | 8.700927943408027e-2 | 5.9147254956553576e-2 |
| user_003 | Sitting | 1.446047531348e12 | 0.613724 | -0.765807 | 9.57948 | 0.46044236363636365 | -0.7379379999999999 | 9.443618181818183 | 0.37665714817600005 | 0.586460361249 | 91.7664370704 | 9.629618610299424 | 9.484515872090418 | 0.1532816363636364 | 2.7869000000000144e-2 | 0.13586181818181764 | 8.424131404958675e-2 | 6.713955371900827e-2 | 6.777256198347087e-2 | 2.349526004631406e-2 | 7.76681161000008e-4 | 1.8458433639669276e-2 | 1.073036766763711e-2 | 7.2020997030293e-3 | 5.847211660405668e-3 | 0.10358748798786999 | 8.48651854592288e-2 | 7.646706258517891e-2 |
| user_003 | Sitting | 1.446047531509e12 | 0.383802 | -0.650847 | 9.46452 | 0.4499912727272728 | -0.6543302727272727 | 9.440134545454542 | 0.147303975204 | 0.42360181740899994 | 89.5771388304 | 9.494632411158054 | 9.473935581651636 | 6.618927272727282e-2 | 3.4832727272727793e-3 | 2.438545454545782e-2 | 4.940451239669421e-2 | 4.307072727272726e-2 | 5.193785123966954e-2 | 4.381019824165301e-3 | 1.2133188892562345e-5 | 5.946503933885893e-4 | 4.212253419712996e-3 | 3.38923822532682e-3 | 4.649084893764115e-3 | 6.490187531738198e-2 | 5.821716435319415e-2 | 6.818419827030392e-2 |
| user_003 | Sitting | 1.446047531598e12 | 0.498763 | -0.689167 | 9.46452 | 0.4813443636363637 | -0.6856832727272727 | 9.468003636363637 | 0.24876453016900002 | 0.47495115388899994 | 89.5771388304 | 9.502676176449349 | 9.506151313791571 | 1.741863636363633e-2 | 3.4837272727272772e-3 | 3.4836363636365775e-3 | 8.740837190082638e-2 | 5.605531404958677e-2 | 7.062280991735535e-2 | 3.034088927685939e-4 | 1.2136355710743833e-5 | 1.2135722314051077e-5 | 1.465137964412546e-2 | 5.0474440098054055e-3 | 7.304601585574778e-3 | 0.12104288349227914 | 7.104536585735487e-2 | 8.54669619535805e-2 |
| user_003 | Sitting | 1.44604753193e12 | 0.460442 | -0.880768 | 9.46452 | 0.4534750909090908 | -0.7170360909090907 | 9.422716363636363 | 0.21200683536400003 | 0.775752269824 | 89.5771388304 | 9.516559143702517 | 9.461390118682749 | 6.96690909090919e-3 | 0.1637319090909093 | 4.180363636363715e-2 | 3.0719553719008252e-2 | 7.727383471074388e-2 | 5.320462809917359e-2 | 4.853782228099312e-5 | 2.6808138054553784e-2 | 1.7475440132232066e-3 | 1.446381519275732e-3 | 9.485840937113464e-3 | 5.361782767843718e-3 | 3.8031322870441045e-2 | 9.7395281903763e-2 | 7.322419523520704e-2 |
| user_003 | Sitting | 1.446047531955e12 | 0.422122 | -0.650847 | 9.65612 | 0.47786072727272727 | -0.6996178181818181 | 9.44710181818182 | 0.178186982884 | 0.42360181740899994 | 93.24065345439999 | 9.687230886826894 | 9.485730492495545 | 5.573872727272727e-2 | 4.8770818181818165e-2 | 0.20901818181818044 | 4.402082644628096e-2 | 8.455785123966947e-2 | 6.808925619834692e-2 | 3.106805717983471e-3 | 2.3785927061239654e-3 | 4.3688600330577934e-2 | 4.2089599727242605e-3 | 1.0109191196815939e-2 | 7.540696546957087e-3 | 6.487649784570881e-2 | 0.10054447372588877 | 8.68371841261397e-2 |
| user_003 | Sitting | 1.446047532084e12 | 0.383802 | -0.650847 | 9.46452 | 0.47786072727272716 | -0.6612976363636363 | 9.450585454545456 | 0.147303975204 | 0.42360181740899994 | 89.5771388304 | 9.494632411158054 | 9.486668111432918 | 9.405872727272718e-2 | 1.0450636363636301e-2 | 1.3934545454544534e-2 | 6.555622314049582e-2 | 7.949095041322311e-2 | 7.03061157024793e-2 | 8.847044176165272e-3 | 1.0921580040495736e-4 | 1.941715570247677e-4 | 5.857225243961679e-3 | 1.1110982175987227e-2 | 7.402790611570207e-3 | 7.65325110261102e-2 | 0.10540864374417891 | 8.603947124180975e-2 |
| user_003 | Sitting | 1.446047532165e12 | 0.422122 | -0.574206 | 9.54116 | 0.44650772727272725 | -0.6369118181818182 | 9.433167272727273 | 0.178186982884 | 0.329712530436 | 91.0337341456 | 9.567739213571825 | 9.465520089793488 | 2.4385727272727253e-2 | 6.27058181818182e-2 | 0.1079927272727268 | 5.953895867768593e-2 | 3.927050413223136e-2 | 6.808925619834726e-2 | 5.946636946198338e-4 | 3.932019633851242e-3 | 1.166242914380155e-2 | 4.64693759204658e-3 | 2.0719397710060082e-3 | 7.326666535236722e-3 | 6.816845012208052e-2 | 4.551856512463907e-2 | 8.559594929222247e-2 |
| user_003 | Sitting | 1.446047532214e12 | 0.537083 | -0.612526 | 9.54116 | 0.463926 | -0.6299445454545455 | 9.443618181818179 | 0.28845814888899995 | 0.375188100676 | 91.0337341456 | 9.575874915388411 | 9.47633465958667 | 7.315699999999997e-2 | 1.7418545454545464e-2 | 9.754181818182062e-2 | 4.655442975206612e-2 | 4.655447107438018e-2 | 4.9087603305785876e-2 | 5.351946648999996e-3 | 3.0340572575206643e-4 | 9.514406294215351e-3 | 3.2932427384973705e-3 | 2.640112742971452e-3 | 3.719047265514745e-3 | 5.7386781914456314e-2 | 5.1382027431500325e-2 | 6.09839918791378e-2 |
| user_003 | Sitting | 1.446047532391e12 | 0.460442 | -0.535886 | 9.4262 | 0.41863836363636364 | -0.6299446363636363 | 9.461036363636364 | 0.21200683536400003 | 0.287173804996 | 88.85324643999999 | 9.452641275345213 | 9.492556516525278 | 4.1803636363636376e-2 | 9.405863636363632e-2 | 3.4836363636364e-2 | 9.659232231404959e-2 | 8.835825619834711e-2 | 6.49223140495872e-2 | 1.7475440132231415e-3 | 8.847027074586768e-3 | 1.213572231404984e-3 | 1.4473703594323817e-2 | 1.2876186440492116e-2 | 5.98070460586029e-3 | 0.12030670635639484 | 0.11347328514012502 | 7.733501539315997e-2 |
| user_003 | Sitting | 1.446047532529e12 | 0.498763 | -0.689167 | 9.34956 | 0.42560581818181814 | -0.6752322727272727 | 9.481938181818181 | 0.24876453016900002 | 0.47495115388899994 | 87.41427219360001 | 9.388183417342145 | 9.515991487267254 | 7.315718181818187e-2 | 1.3934727272727265e-2 | 0.13237818181818106 | 7.98074214876033e-2 | 4.908786776859502e-2 | 6.808925619834709e-2 | 5.3519732515785205e-3 | 1.9417662416528904e-4 | 1.7523983021487402e-2 | 7.866250311642372e-3 | 3.222624247057847e-3 | 6.397732154470296e-3 | 8.869188413627467e-2 | 5.676816226599067e-2 | 7.998582470957148e-2 |
| user_003 | Sitting | 1.446047532691e12 | 0.422122 | -0.842448 | 9.46452 | 0.42908936363636363 | -0.6787158181818183 | 9.405298181818182 | 0.178186982884 | 0.7097186327039999 | 89.5771388304 | 9.511311394649425 | 9.440375723637255 | 6.967363636363633e-3 | 0.16373218181818172 | 5.9221818181818264e-2 | 4.497070247933884e-2 | 8.96250743801653e-2 | 6.048859504132167e-2 | 4.854415604132226e-5 | 2.6808227362942118e-2 | 3.5072237487603405e-3 | 3.0802885080879025e-3 | 1.2626851415314798e-2 | 4.959097436513814e-3 | 5.550034691862658e-2 | 0.11236926365921776 | 7.042085938494229e-2 |
| user_003 | Sitting | 1.446047532788e12 | 0.460442 | -0.689167 | 9.54116 | 0.4360566363636364 | -0.6403957272727273 | 9.436650909090908 | 0.21200683536400003 | 0.47495115388899994 | 91.0337341456 | 9.57709205003549 | 9.468743163819505 | 2.438536363636362e-2 | 4.877127272727266e-2 | 0.104509090909092 | 4.117052066115701e-2 | 5.162148760330579e-2 | 5.35213223140498e-2 | 5.946459596776853e-4 | 2.3786370434380104e-3 | 1.0922150082644855e-2 | 2.744925226993988e-3 | 4.317064291385426e-3 | 4.449397099323852e-3 | 5.239203400321454e-2 | 6.570437041312721e-2 | 6.670380123594045e-2 |
| user_003 | Sitting | 1.446047532804e12 | 0.230521 | -0.574206 | 9.54116 | 0.41167100000000006 | -0.636912 | 9.454069090909092 | 5.3139931441e-2 | 0.329712530436 | 91.0337341456 | 9.561202152840247 | 9.484988102207778 | 0.18115000000000006 | 6.270600000000004e-2 | 8.709090909090733e-2 | 5.225482644628099e-2 | 6.0805702479338856e-2 | 6.017190082644642e-2 | 3.281532250000002e-2 | 3.932042436000005e-3 | 7.584826446280685e-3 | 5.409289030824944e-3 | 5.143409093702481e-3 | 5.145546261157036e-3 | 7.354786897541589e-2 | 7.171756475022337e-2 | 7.173246309138587e-2 |
| user_003 | Sitting | 1.446047532819e12 | 0.575403 | -0.727487 | 9.34956 | 0.42212199999999994 | -0.6403956363636364 | 9.450585454545454 | 0.331088612409 | 0.529237335169 | 87.41427219360001 | 9.395456249761265 | 9.482358118978716 | 0.15328100000000006 | 8.70913636363636e-2 | 0.10102545454545364 | 6.55560991735537e-2 | 7.157335537190083e-2 | 6.935603305785162e-2 | 2.349506496100002e-2 | 7.584905620041317e-3 | 1.0206142466115519e-2 | 7.620226583552217e-3 | 6.374646010217882e-3 | 6.172669667918895e-3 | 8.729390920076965e-2 | 7.984138031257904e-2 | 7.856633928037436e-2 |
| user_003 | Sitting | 1.446047532991e12 | 0.498763 | -0.689167 | 9.46452 | 0.41863836363636364 | -0.6578139090909092 | 9.408781818181819 | 0.24876453016900002 | 0.47495115388899994 | 89.5771388304 | 9.502676176449349 | 9.441844129998412 | 8.012463636363637e-2 | 3.1353090909090775e-2 | 5.573818181818169e-2 | 7.474038842975207e-2 | 6.58730082644628e-2 | 6.080528925619788e-2 | 6.41995735240496e-3 | 9.830163095537105e-4 | 3.1067449123966797e-3 | 6.935123140278735e-3 | 7.1513644837716e-3 | 6.11640404628094e-3 | 8.327738672820333e-2 | 8.456574060322301e-2 | 7.820744239700554e-2 |
| user_003 | Sitting | 1.446047533055e12 | 0.460442 | -0.650847 | 9.38788 | 0.42212200000000005 | -0.6578139090909091 | 9.415749090909088 | 0.21200683536400003 | 0.42360181740899994 | 88.13229089439999 | 9.421671802136444 | 9.44895072125354 | 3.8319999999999965e-2 | 6.966909090909135e-3 | 2.7869090909089067e-2 | 7.695723966942146e-2 | 6.523954545454545e-2 | 5.225454545454495e-2 | 1.4684223999999974e-3 | 4.8537822280992347e-5 | 7.766862280990708e-4 | 7.050963895679938e-3 | 7.138123901706236e-3 | 4.8355337184071805e-3 | 8.397001783779695e-2 | 8.448741860008646e-2 | 6.953800197307354e-2 |
| user_003 | Sitting | 1.446047533249e12 | 0.345482 | -0.650847 | 9.46452 | 0.4256057272727272 | -0.6578139090909091 | 9.436650909090908 | 0.119357812324 | 0.42360181740899994 | 89.5771388304 | 9.493160614891808 | 9.46968381931937 | 8.012372727272721e-2 | 6.966909090909135e-3 | 2.786909090909262e-2 | 5.85887603305785e-2 | 5.953899173553718e-2 | 4.4337190082644765e-2 | 6.419811672074369e-3 | 4.8537822280992347e-5 | 7.766862280992689e-4 | 4.047867299084896e-3 | 6.319509547628849e-3 | 3.5458374106687065e-3 | 6.362285201941906e-2 | 7.949534293044372e-2 | 5.954693451949233e-2 |
| user_003 | Sitting | 1.446047533443e12 | 0.498763 | -0.650847 | 9.4262 | 0.4499913636363637 | -0.657814 | 9.4262 | 0.24876453016900002 | 0.42360181740899994 | 88.85324643999999 | 9.461797545264748 | 9.460293159957134 | 4.8771636363636295e-2 | 6.9670000000000565e-3 | 0.0 | 5.193814049586775e-2 | 5.257162809917355e-2 | 2.660231404958728e-2 | 2.37867251358677e-3 | 4.853908900000079e-5 | 0.0 | 3.3914277338775323e-3 | 5.8075949420383155e-3 | 9.09075926070645e-4 | 5.823596598217919e-2 | 7.620757798302158e-2 | 3.0150885991470384e-2 |
| user_003 | Sitting | 1.446047533702e12 | 0.422122 | -0.650847 | 9.50284 | 0.4395403636363637 | -0.6612977272727272 | 9.443618181818183 | 0.178186982884 | 0.42360181740899994 | 90.30396806560002 | 9.534451052152557 | 9.477111134477436 | 1.7418363636363676e-2 | 1.0450727272727223e-2 | 5.9221818181818264e-2 | 3.4203123966942146e-2 | 2.786920661157025e-2 | 2.72357024793389e-2 | 3.0339939176859645e-4 | 1.0921770052892458e-4 | 3.5072237487603405e-3 | 1.6593011434440253e-3 | 1.3106807544943642e-3 | 1.1385514025544887e-3 | 4.073452029230276e-2 | 3.62033251856009e-2 | 3.374242733643341e-2 |
| user_003 | Sitting | 1.44604753435e12 | 0.498763 | -0.689167 | 9.50284 | 0.45695872727272735 | -0.6612978181818181 | 9.468003636363635 | 0.24876453016900002 | 0.47495115388899994 | 90.30396806560002 | 9.540842926579288 | 9.502149947082033 | 4.180427272727266e-2 | 2.7869181818181876e-2 | 3.4836363636365775e-2 | 2.945285950413223e-2 | 1.8051628099173548e-2 | 3.293619834710784e-2 | 1.7475972182561929e-3 | 7.766912952148793e-4 | 1.2135722314051078e-3 | 1.126443607117956e-3 | 5.284638936957168e-4 | 1.4783516273478882e-3 | 3.356253278758854e-2 | 2.2988342560865863e-2 | 3.844933845136855e-2 |
| user_003 | Sitting | 1.446047534415e12 | 0.498763 | -0.650847 | 9.46452 | 0.4639260909090909 | -0.6612978181818181 | 9.46452 | 0.24876453016900002 | 0.42360181740899994 | 89.5771388304 | 9.499973956699987 | 9.499015665677254 | 3.4836909090909085e-2 | 1.0450818181818144e-2 | 0.0 | 3.103636363636363e-2 | 1.805163636363636e-2 | 2.7552396694215275e-2 | 1.213610235008264e-3 | 1.0921960066942071e-4 | 0.0 | 1.2091900474124713e-3 | 5.284640664357619e-4 | 1.1595131047333121e-3 | 3.477341006304201e-2 | 2.2988346317988205e-2 | 3.405162411300395e-2 |
| user_003 | Sitting | 1.446047534803e12 | 0.422122 | -0.612526 | 9.46452 | 0.4604424545454545 | -0.6543305454545453 | 9.471487272727273 | 0.178186982884 | 0.375188100676 | 89.5771388304 | 9.493709175762653 | 9.505321529005101 | 3.832045454545452e-2 | 4.180454545454526e-2 | 6.967272727273155e-3 | 3.4520173553719e-2 | 1.3301223140495816e-2 | 2.026842975206639e-2 | 1.4684572365702459e-3 | 1.7476200206611408e-3 | 4.854288925620431e-5 | 1.6096746418955666e-3 | 3.4863351750112544e-4 | 9.443798455296936e-4 | 4.012075076435592e-2 | 1.8671730436709003e-2 | 3.0730763829259006e-2 |
| user_003 | Sitting | 1.446047534998e12 | 0.460442 | -0.689167 | 9.4262 | 0.453475 | -0.6543305454545454 | 9.461036363636362 | 0.21200683536400003 | 0.47495115388899994 | 88.85324643999999 | 9.462568595748884 | 9.494558667728176 | 6.967000000000001e-3 | 3.483645454545459e-2 | 3.483636363636222e-2 | 2.8502826446280997e-2 | 1.3934595041322238e-2 | 1.8051570247933538e-2 | 4.8539089000000014e-5 | 1.2135785652975235e-3 | 1.2135722314048601e-3 | 1.319511052964688e-3 | 3.7069852473929204e-4 | 5.924438984222251e-4 | 3.632507471382114e-2 | 1.9253532785940664e-2 | 2.434017046822444e-2 |
| user_003 | Sitting | 1.446047535515e12 | 0.460442 | -0.650847 | 9.46452 | 0.44650754545454546 | -0.6647814545454546 | 9.440134545454544 | 0.21200683536400003 | 0.42360181740899994 | 89.5771388304 | 9.49803913885245 | 9.474119764777338 | 1.3934454545454555e-2 | 1.3934454545454611e-2 | 2.4385454545456042e-2 | 2.4068826446280996e-2 | 2.0585239669421454e-2 | 3.008595041322305e-2 | 1.941690234793391e-4 | 1.9416902347934065e-4 | 5.946503933885027e-4 | 8.980566373538687e-4 | 6.454093360931615e-4 | 1.2367404285499523e-3 | 2.9967593119132354e-2 | 2.5404907716682648e-2 | 3.516732046303716e-2 |
| user_003 | Sitting | 1.446047537963e12 | 2.91294 | -4.06135 | -17.7429 | 0.6694618181818182 | -0.9748269999999999 | 7.00156909090909 | 8.485219443599998 | 16.4945638225 | 314.81050041 | 18.433401305133568 | 10.321177531628067 | 2.2434781818181815 | 3.086523 | 24.74446909090909 | 0.2169370991735537 | 0.3173297520661157 | 2.264065123966943 | 5.033194352294213 | 9.526624229529 | 612.2887505909553 | 0.457926093891347 | 0.8678727182561082 | 55.66323812816227 | 0.6767023672866432 | 0.9315968646663149 | 7.4607799946226985 |
| user_003 | Sitting | 1.446047537996e12 | 0.498763 | -0.574206 | 9.38788 | 0.7008150000000001 | -0.9574087272727273 | 7.0050527272727265 | 0.24876453016900002 | 0.329712530436 | 88.13229089439999 | 9.418639389795374 | 10.325252885713102 | 0.20205200000000006 | 0.3832027272727273 | 2.3828272727272726 | 0.2704586033057851 | 0.4246898595041323 | 3.15778347107438 | 4.082501070400003e-2 | 0.14684433018925622 | 5.677865811652892 | 0.467783707752465 | 0.9122205865213097 | 57.8899981788081 | 0.6839471527482697 | 0.9551023958305778 | 7.608547704970253 |
| user_003 | Sitting | 1.446047538044e12 | 0.422122 | -0.919089 | 9.50284 | 0.6903640909090908 | -0.9643761818181816 | 7.005052727272727 | 0.178186982884 | 0.8447245899210001 | 90.30396806560002 | 9.556509804233187 | 10.325897387815681 | 0.2682420909090908 | 4.528718181818159e-2 | 2.4977872727272734 | 0.41043860330578513 | 0.5539019090909091 | 4.500261157024793 | 7.195381933528093e-2 | 2.050928837033037e-3 | 6.2389412597983505 | 0.5123842726084574 | 0.959901352937687 | 61.24369845327421 | 0.7158102210840925 | 0.9797455552018018 | 7.825835319841213 |
| user_003 | Sitting | 1.446047538101e12 | 0.383802 | -0.804128 | 9.54116 | 0.443024 | -0.7135526363636363 | 9.461036363636364 | 0.147303975204 | 0.646621840384 | 91.0337341456 | 9.582674989854764 | 9.498997704798395 | 5.9222e-2 | 9.057536363636365e-2 | 8.012363636363595e-2 | 0.13871318181818182 | 0.11464396694214875 | 0.9611742148760336 | 3.5072452839999997e-3 | 8.203896497859506e-3 | 6.419797104132165e-3 | 3.661955596183396e-2 | 2.796989594571301e-2 | 2.23701703117055 | 0.1913623681966597 | 0.1672420280483139 | 1.4956660827773525 |
| user_003 | Sitting | 1.446047538125e12 | 0.498763 | -0.804128 | 9.57948 | 0.47089336363636375 | -0.7240035454545454 | 9.450585454545454 | 0.24876453016900002 | 0.646621840384 | 91.7664370704 | 9.626101154722663 | 9.490513908927614 | 2.7869636363636263e-2 | 8.012445454545458e-2 | 0.12889454545454626 | 6.175596694214875e-2 | 5.922220661157021e-2 | 0.3128957024793395 | 7.767166310413167e-4 | 6.419928216206618e-3 | 1.661380384793409e-2 | 8.979472165661901e-3 | 4.764998892272725e-3 | 0.5763698394401208 | 9.476007685550862e-2 | 6.902897139804942e-2 | 0.7591902524664821 |
| user_003 | Sitting | 1.446047538262e12 | 0.537083 | -0.612526 | 9.50284 | 0.5092136363636363 | -0.6543302727272726 | 9.485421818181818 | 0.28845814888899995 | 0.375188100676 | 90.30396806560002 | 9.537694391998782 | 9.522259443822756 | 2.786936363636372e-2 | 4.180427272727261e-2 | 1.7418181818182887e-2 | 7.220681818181819e-2 | 6.175582644628099e-2 | 6.1121983471074254e-2 | 7.767014294958724e-4 | 1.7475972182561883e-3 | 3.0339305785127694e-4 | 9.30272696033734e-3 | 5.69174341896544e-3 | 5.808597998497331e-3 | 9.645064520436004e-2 | 7.544364399315187e-2 | 7.621415877970006e-2 |
| user_003 | Sitting | 1.446047538343e12 | 0.537083 | -0.574206 | 9.38788 | 0.4743770909090909 | -0.7065851818181819 | 9.471487272727272 | 0.28845814888899995 | 0.329712530436 | 88.13229089439999 | 9.420746338466236 | 9.51013996484748 | 6.270590909090906e-2 | 0.13237918181818187 | 8.360727272727253e-2 | 4.782130578512396e-2 | 6.175572727272729e-2 | 5.5104793388430065e-2 | 3.932031034917352e-3 | 1.7524247778851254e-2 | 6.990176052892529e-3 | 3.4631438297821194e-3 | 5.9587168723117985e-3 | 3.907702585123994e-3 | 5.88484819666754e-2 | 7.719272551420761e-2 | 6.251161960087095e-2 |
| user_003 | Sitting | 1.446047538481e12 | 0.422122 | -0.650847 | 9.50284 | 0.42908945454545455 | -0.7031014545454545 | 9.415749090909092 | 0.178186982884 | 0.42360181740899994 | 90.30396806560002 | 9.534451052152557 | 9.452520129048189 | 6.9674545454545544e-3 | 5.225445454545452e-2 | 8.709090909090911e-2 | 5.162155371900827e-2 | 7.600711570247934e-2 | 8.075702479338903e-2 | 4.854542284297533e-5 | 2.7305280198429726e-3 | 7.584826446280995e-3 | 5.528469067821186e-3 | 9.055587604848238e-3 | 7.911387701277323e-3 | 7.435367555017833e-2 | 9.516085121964935e-2 | 8.894598192879385e-2 |
| user_003 | Sitting | 1.446047538554e12 | 0.422122 | -0.535886 | 9.4262 | 0.4465077272727273 | -0.6647812727272726 | 9.422716363636363 | 0.178186982884 | 0.287173804996 | 88.85324643999999 | 9.450852195854086 | 9.457397142342744 | 2.438572727272731e-2 | 0.12889527272727264 | 3.4836363636365775e-3 | 4.940454545454545e-2 | 7.474023140495865e-2 | 5.637157024793347e-2 | 5.946636946198365e-4 | 1.6613991331437993e-2 | 1.2135722314051077e-5 | 4.071027434838466e-3 | 8.259009006755818e-3 | 4.830017480991683e-3 | 6.380460355521744e-2 | 9.087909004141612e-2 | 6.94983271812472e-2 |
| user_003 | Sitting | 1.446047538586e12 | 0.345482 | -0.689167 | 9.4262 | 0.4465078181818182 | -0.6299447272727272 | 9.398330909090907 | 0.119357812324 | 0.47495115388899994 | 88.85324643999999 | 9.457671775136468 | 9.430760240156756 | 0.10102581818181816 | 5.922227272727276e-2 | 2.786909090909262e-2 | 5.3204925619834725e-2 | 9.469222314049584e-2 | 4.5603966942148494e-2 | 1.020621593930578e-2 | 3.5072775869834753e-3 | 7.766862280992689e-4 | 4.033517999304282e-3 | 1.2079629896032291e-2 | 3.5149464811419576e-3 | 6.350998346169114e-2 | 0.10990736961656526 | 5.928698407864881e-2 |
| user_003 | Sitting | 1.446047538716e12 | 0.422122 | -0.650847 | 9.4262 | 0.46044236363636365 | -0.6508465454545453 | 9.457552727272729 | 0.178186982884 | 0.42360181740899994 | 88.85324643999999 | 9.458067204259704 | 9.49159945306404 | 3.832036363636365e-2 | 4.5454545460899e-7 | 3.13527272727292e-2 | 5.098814049586778e-2 | 5.57385867768595e-2 | 6.143867768595047e-2 | 1.4684502692231417e-3 | 2.0661157030569337e-13 | 9.829935074381372e-4 | 4.204520725820437e-3 | 5.136800779629599e-3 | 5.889135064763309e-3 | 6.484227576065199e-2 | 7.167147814597938e-2 | 7.674070018421325e-2 |
| user_003 | Sitting | 1.446047538942e12 | 0.307161 | -0.574206 | 9.57948 | 0.41863845454545456 | -0.6334282727272728 | 9.478454545454547 | 9.434787992100001e-2 | 0.329712530436 | 91.7664370704 | 9.601588279069095 | 9.509319384860925 | 0.11147745454545455 | 5.922227272727276e-2 | 0.10102545454545364 | 5.573864462809917e-2 | 5.478864462809916e-2 | 4.40204958677692e-2 | 1.2427222871933884e-2 | 3.5072775869834753e-3 | 1.0206142466115519e-2 | 4.095318860127722e-3 | 5.583616413305784e-3 | 2.7879063897821754e-3 | 6.39946783735001e-2 | 7.472360010937498e-2 | 5.280062868737621e-2 |
| user_003 | Sitting | 1.446047539177e12 | 0.345482 | -0.689167 | 9.46452 | 0.3907692727272727 | -0.6821995454545454 | 9.468003636363637 | 0.119357812324 | 0.47495115388899994 | 89.5771388304 | 9.495864773500779 | 9.501078447172395 | 4.5287272727272676e-2 | 6.9674545454545544e-3 | 3.4836363636365775e-3 | 6.84062644628099e-2 | 5.985547107438018e-2 | 6.302214876033106e-2 | 2.0509370710743756e-3 | 4.854542284297533e-5 | 1.2135722314051077e-5 | 7.985403573391432e-3 | 5.155520228918111e-3 | 8.248981431104595e-3 | 8.936108534139137e-2 | 7.180195142834289e-2 | 9.082390341261817e-2 |
| user_003 | Sitting | 1.446047539242e12 | 0.307161 | -0.574206 | 9.46452 | 0.42560581818181814 | -0.6403957272727273 | 9.471487272727272 | 9.434787992100001e-2 | 0.329712530436 | 89.5771388304 | 9.48689618583217 | 9.503357170811485 | 0.11844481818181812 | 6.618972727272732e-2 | 6.967272727271379e-3 | 6.492270247933883e-2 | 6.048900000000001e-2 | 7.347305785123949e-2 | 1.4029174954123954e-2 | 4.381079996438023e-3 | 4.8542889256179554e-5 | 7.264995577235909e-3 | 6.130837874947407e-3 | 9.196671019083402e-3 | 8.52349434048965e-2 | 7.829966714455054e-2 | 9.589927538351581e-2 |
| user_003 | Sitting | 1.44604753925e12 | 0.575403 | -0.650847 | 9.54116 | 0.4290894545454545 | -0.6473630909090909 | 9.46452 | 0.331088612409 | 0.42360181740899994 | 91.0337341456 | 9.580627566888193 | 9.497061195942141 | 0.1463135454545455 | 3.48390909090901e-3 | 7.663999999999938e-2 | 6.618947107438017e-2 | 5.1938214876033055e-2 | 6.428892561983429e-2 | 2.1407653583479354e-2 | 1.2137622553718444e-5 | 5.8736895999999044e-3 | 7.6180346566506385e-3 | 5.266982370293012e-3 | 6.86109609737033e-3 | 8.728135343044721e-2 | 7.257397860316749e-2 | 8.283173363735864e-2 |
| user_003 | Sitting | 1.446047539363e12 | 0.460442 | -0.727487 | 9.34956 | 0.4569588181818181 | -0.6717486363636362 | 9.4262 | 0.21200683536400003 | 0.529237335169 | 87.41427219360001 | 9.389116910771374 | 9.462187082718389 | 3.483181818181913e-3 | 5.573836363636375e-2 | 7.663999999999938e-2 | 8.455800826446282e-2 | 7.157352066115702e-2 | 6.365553719008284e-2 | 1.2132555578513057e-5 | 3.106765180859517e-3 | 5.8736895999999044e-3 | 9.295014362248687e-3 | 9.007034869194588e-3 | 5.014259810668705e-3 | 9.641065481703091e-2 | 9.49053995787099e-2 | 7.081143841688788e-2 |
| user_003 | Sitting | 1.446047539436e12 | 0.460442 | -0.765807 | 9.54116 | 0.42560581818181814 | -0.7135525454545454 | 9.447101818181817 | 0.21200683536400003 | 0.586460361249 | 91.0337341456 | 9.582911944822044 | 9.484323546353263 | 3.483618181818188e-2 | 5.225445454545463e-2 | 9.405818181818226e-2 | 7.030644628099174e-2 | 6.903965289256202e-2 | 6.903933884297525e-2 | 1.2135595636694255e-3 | 2.7305280198429843e-3 | 8.846941566942232e-3 | 6.440833891956421e-3 | 8.33402396231856e-3 | 8.223606738993262e-3 | 8.025480603650115e-2 | 9.129087556989778e-2 | 9.068410411419006e-2 |
| user_003 | Sitting | 1.446047539468e12 | 0.460442 | -0.765807 | 9.34956 | 0.44999136363636355 | -0.7170361818181819 | 9.4262 | 0.21200683536400003 | 0.586460361249 | 87.41427219360001 | 9.392163722498294 | 9.46466076088866 | 1.0450636363636467e-2 | 4.8770818181818165e-2 | 7.663999999999938e-2 | 6.840630578512398e-2 | 3.7686694214876054e-2 | 7.727338842975164e-2 | 1.0921580040496085e-4 | 2.3785927061239654e-3 | 5.8736895999999044e-3 | 6.29520499386251e-3 | 2.36428181288505e-3 | 9.046629361382369e-3 | 7.934232788280483e-2 | 4.862388109648437e-2 | 9.511377061909788e-2 |
| user_003 | Sitting | 1.446047539525e12 | 0.460442 | -0.727487 | 9.38788 | 0.4290893636363636 | -0.6821993636363636 | 9.429683636363636 | 0.21200683536400003 | 0.529237335169 | 88.13229089439999 | 9.427276121178004 | 9.464803624816936 | 3.1352636363636444e-2 | 4.528763636363642e-2 | 4.180363636363715e-2 | 6.397267768595044e-2 | 5.510515702479342e-2 | 6.650578512396699e-2 | 9.829878069504182e-4 | 2.050970007404964e-3 | 1.7475440132232066e-3 | 8.124416326288506e-3 | 5.511913618561984e-3 | 5.330891838317037e-3 | 9.013554418922928e-2 | 7.424226302155655e-2 | 7.301295664686534e-2 |
| user_003 | Sitting | 1.446047539541e12 | 0.345482 | -0.727487 | 9.4262 | 0.41515472727272723 | -0.6891666363636363 | 9.4262 | 0.119357812324 | 0.529237335169 | 88.85324643999999 | 9.460541294634941 | 9.461245060511372 | 6.967272727272722e-2 | 3.832036363636371e-2 | 0.0 | 6.618949586776862e-2 | 6.143910743801655e-2 | 6.682247933884321e-2 | 4.854288925619827e-3 | 1.468450269223146e-3 | 0.0 | 8.582264722732536e-3 | 5.798763261621339e-3 | 6.938323421187104e-3 | 9.264051339847236e-2 | 7.614961104051247e-2 | 8.329659909736474e-2 |
| user_003 | Sitting | 1.446047539613e12 | 0.460442 | -0.574206 | 9.38788 | 0.4012200909090909 | -0.6856830909090909 | 9.44361818181818 | 0.21200683536400003 | 0.329712530436 | 88.13229089439999 | 9.41668786039975 | 9.477938578653424 | 5.922190909090913e-2 | 0.11147709090909086 | 5.573818181818169e-2 | 6.4289347107438e-2 | 8.930847107438017e-2 | 6.143867768595079e-2 | 3.5072345163719054e-3 | 1.2427141797553708e-2 | 3.1067449123966797e-3 | 5.837369956217128e-3 | 1.0567076387593537e-2 | 6.332640552967716e-3 | 7.640268291242873e-2 | 0.10279628586477985 | 7.957788984993078e-2 |
| user_003 | Sitting | 1.446047539662e12 | 0.460442 | -0.574206 | 9.50284 | 0.42212209090909086 | -0.664781090909091 | 9.468003636363637 | 0.21200683536400003 | 0.329712530436 | 90.30396806560002 | 9.531300406104092 | 9.501586987084501 | 3.8319909090909154e-2 | 9.0575090909091e-2 | 3.4836363636364e-2 | 6.618954545454546e-2 | 8.550803305785125e-2 | 3.9586776859504624e-2 | 1.468415432735542e-3 | 8.203847093190098e-3 | 1.213572231404984e-3 | 8.463145261121711e-3 | 8.296535774210368e-3 | 2.9379480474831074e-3 | 9.199535456272621e-2 | 9.108532139818341e-2 | 5.4202841691954745e-2 |
| user_003 | Sitting | 1.446047539743e12 | 0.307161 | -0.650847 | 9.38788 | 0.443024 | -0.6647812727272728 | 9.447101818181817 | 9.434787992100001e-2 | 0.42360181740899994 | 88.13229089439999 | 9.415425672359694 | 9.48130930593024 | 0.13586299999999996 | 1.3934272727272878e-2 | 5.9221818181818264e-2 | 6.36560826446281e-2 | 4.053709090909094e-2 | 4.718743801652924e-2 | 1.845875476899999e-2 | 1.9416395643802073e-4 | 3.5072237487603405e-3 | 5.967562775254698e-3 | 2.700786287416983e-3 | 2.8695467035311996e-3 | 7.725000178158378e-2 | 5.196908973050214e-2 | 5.3568150085019736e-2 |
| user_003 | Sitting | 1.44604753984e12 | 0.345482 | -0.650847 | 9.57948 | 0.4465079090909091 | -0.6647812727272727 | 9.44710181818182 | 0.119357812324 | 0.42360181740899994 | 91.7664370704 | 9.607777927290629 | 9.481999925423107 | 0.10102590909090908 | 1.3934272727272767e-2 | 0.13237818181818106 | 8.139092561983471e-2 | 7.917409917355371e-2 | 6.04885950413228e-2 | 1.0206234307644627e-2 | 1.9416395643801764e-4 | 1.7523983021487402e-2 | 7.2760015071224645e-3 | 1.0569256395184072e-2 | 5.581329016979755e-3 | 8.529948128284523e-2 | 0.10280688885081618 | 7.47082928260294e-2 |
| user_003 | Sitting | 1.446047539938e12 | 0.537083 | -0.574206 | 9.4262 | 0.4499914545454545 | -0.6787157272727273 | 9.457552727272727 | 0.28845814888899995 | 0.329712530436 | 88.85324643999999 | 9.458933191397696 | 9.493288075858345 | 8.70915454545455e-2 | 0.10450972727272734 | 3.135272727272742e-2 | 5.7638661157024804e-2 | 7.410695041322317e-2 | 3.895338842975268e-2 | 7.584937289661166e-3 | 1.0922283094619848e-2 | 9.829935074380258e-4 | 4.463789009020284e-3 | 8.106787283856505e-3 | 2.737157005559762e-3 | 6.68115933728592e-2 | 9.003769923680027e-2 | 5.2317845956802944e-2 |
| user_003 | Sitting | 1.446047540156e12 | 0.345482 | -0.804128 | 9.46452 | 0.453475 | -0.6926506363636363 | 9.443618181818183 | 0.119357812324 | 0.646621840384 | 89.5771388304 | 9.504899709260902 | 9.480333405363366 | 0.107993 | 0.11147736363636362 | 2.090181818181769e-2 | 4.718766942148758e-2 | 5.4788471074380146e-2 | 6.333884297520678e-2 | 1.1662488049e-2 | 1.2427202603314046e-2 | 4.368860033057645e-4 | 5.102591182545451e-3 | 4.019181194645379e-3 | 5.653040103380977e-3 | 7.143242388821376e-2 | 6.339701250568026e-2 | 7.518670163919267e-2 |
| user_003 | Sitting | 1.446047540172e12 | 0.498763 | -0.765807 | 9.4262 | 0.45347509090909094 | -0.6926505454545454 | 9.45406909090909 | 0.24876453016900002 | 0.586460361249 | 88.85324643999999 | 9.470399745069793 | 9.490850263665708 | 4.528790909090907e-2 | 7.315645454545461e-2 | 2.7869090909090843e-2 | 5.2888297520661126e-2 | 6.555606611570247e-2 | 6.3655537190083e-2 | 2.0509947098264446e-3 | 5.351866841661166e-3 | 7.766862280991699e-4 | 5.354138748736284e-3 | 5.332058540061606e-3 | 5.5967744817431225e-3 | 7.31719806260312e-2 | 7.30209458995267e-2 | 7.481159323088316e-2 |
| user_003 | Sitting | 1.446047540269e12 | 0.460442 | -0.650847 | 9.2346 | 0.45695872727272724 | -0.6682647272727272 | 9.394847272727274 | 0.21200683536400003 | 0.42360181740899994 | 85.27783716 | 9.26895063169359 | 9.43032809691739 | 3.4832727272727793e-3 | 1.741772727272728e-2 | 0.16024727272727368 | 5.383861983471073e-2 | 7.347342975206611e-2 | 8.645752066115685e-2 | 1.2133188892562345e-5 | 3.0337722334710765e-4 | 2.567918841652923e-2 | 5.776714665064613e-3 | 7.215338384983472e-3 | 1.0210555456048083e-2 | 7.600470159841832e-2 | 8.494314795781631e-2 | 0.10104729316536927 |
| user_003 | Sitting | 1.446047540318e12 | 0.422122 | -0.535886 | 9.65612 | 0.45695872727272724 | -0.668264909090909 | 9.440134545454546 | 0.178186982884 | 0.287173804996 | 93.24065345439999 | 9.680186684268026 | 9.475438618282498 | 3.483672727272724e-2 | 0.132378909090909 | 0.2159854545454536 | 6.428949586776858e-2 | 5.7955223140495844e-2 | 8.962446280991737e-2 | 1.213597567074378e-3 | 1.752417557209915e-2 | 4.66497165752062e-2 | 6.929614733910591e-3 | 4.520045244982718e-3 | 1.2364094543050317e-2 | 8.324430751655389e-2 | 6.723128174430945e-2 | 0.11119395011892651 |
| user_003 | Sitting | 1.446047540325e12 | 0.383802 | -0.574206 | 9.46452 | 0.4360567272727273 | -0.6508466363636364 | 9.44361818181818 | 0.147303975204 | 0.329712530436 | 89.5771388304 | 9.489686788089479 | 9.476578365490541 | 5.225472727272734e-2 | 7.664063636363638e-2 | 2.0901818181819465e-2 | 5.6688727272727266e-2 | 5.763855371900824e-2 | 8.930776859504132e-2 | 2.7305565223471146e-3 | 5.873787142223143e-3 | 4.368860033058388e-4 | 5.49977403832757e-3 | 4.470402678275729e-3 | 1.2349752325770074e-2 | 7.41604614220244e-2 | 6.686106997555251e-2 | 0.11112943950983499 |
| user_003 | Sitting | 1.44604754035e12 | 0.345482 | -0.804128 | 9.34956 | 0.44650772727272714 | -0.6647813636363636 | 9.408781818181819 | 0.119357812324 | 0.646621840384 | 87.41427219360001 | 9.390434060591023 | 9.44335301311665 | 0.10102572727272713 | 0.1393466363636363 | 5.9221818181818264e-2 | 5.478842148760328e-2 | 6.143899999999997e-2 | 8.645752066115717e-2 | 1.0206197570983441e-2 | 1.941748506585949e-2 | 3.5072237487603405e-3 | 4.3148482379737e-3 | 5.454524538504126e-3 | 1.1695526568294479e-2 | 6.568750442796331e-2 | 7.38547529851947e-2 | 0.10814585784159501 |
| user_003 | Sitting | 1.446047540366e12 | 0.460442 | -0.727487 | 9.38788 | 0.44999136363636355 | -0.668265 | 9.412265454545453 | 0.21200683536400003 | 0.529237335169 | 88.13229089439999 | 9.427276121178004 | 9.447275769909304 | 1.0450636363636467e-2 | 5.9222e-2 | 2.4385454545454266e-2 | 6.017223966942149e-2 | 6.365590909090904e-2 | 6.238876033057863e-2 | 1.0921580040496085e-4 | 3.5072452839999997e-3 | 5.946503933884161e-4 | 5.200762819015026e-3 | 5.541682701910585e-3 | 7.90476821637862e-3 | 7.21163145135345e-2 | 7.444247914941163e-2 | 8.890876343971171e-2 |
| user_003 | Sitting | 1.44604754048e12 | 0.498763 | -0.765807 | 9.4262 | 0.4987627272727273 | -0.7414219090909092 | 9.44361818181818 | 0.24876453016900002 | 0.586460361249 | 88.85324643999999 | 9.470399745069793 | 9.486452058500035 | 2.7272727270988284e-7 | 2.43850909090908e-2 | 1.741818181818111e-2 | 6.397279338842976e-2 | 7.505698347107435e-2 | 5.6371570247933954e-2 | 7.43801652797708e-14 | 5.946326586446228e-4 | 3.0339305785121503e-4 | 5.997347520679191e-3 | 8.279990516842221e-3 | 4.944755219233634e-3 | 7.744254335104957e-2 | 9.099445322019481e-2 | 7.031895348505718e-2 |
| user_003 | Sitting | 1.446047540593e12 | 0.460442 | -0.689167 | 9.50284 | 0.42560572727272733 | -0.668265 | 9.502839999999999 | 0.21200683536400003 | 0.47495115388899994 | 90.30396806560002 | 9.538916398357468 | 9.536132101776438 | 3.483627272727269e-2 | 2.0901999999999976e-2 | 1.7763568394002505e-15 | 5.858879338842975e-2 | 4.3703983471074376e-2 | 9.310809917355378e-2 | 1.213565897528923e-3 | 4.36893603999999e-4 | 3.1554436208840472e-30 | 4.6480467704342575e-3 | 2.341116897894815e-3 | 1.3706746729977431e-2 | 6.817658520661077e-2 | 4.838508962371378e-2 | 0.11707581616191036 |
| user_003 | Sitting | 1.446047540649e12 | 0.652044 | -0.574206 | 9.50284 | 0.4465077272727273 | -0.6299446363636364 | 9.443618181818183 | 0.42516137793599995 | 0.329712530436 | 90.30396806560002 | 9.542475673218771 | 9.476115480764772 | 0.20553627272727265 | 5.573863636363641e-2 | 5.9221818181818264e-2 | 8.360787603305782e-2 | 6.93565371900826e-2 | 7.442314049586765e-2 | 4.2245159406619805e-2 | 3.1067955836776907e-3 | 3.5072237487603405e-3 | 1.1187097730272723e-2 | 6.7685262636356025e-3 | 8.376958139143426e-3 | 0.10576907738215703 | 8.227105361933565e-2 | 9.152572392034616e-2 |
| user_003 | Sitting | 1.44604754073e12 | 0.422122 | -0.765807 | 9.38788 | 0.47437709090909086 | -0.6856831818181817 | 9.450585454545454 | 0.178186982884 | 0.586460361249 | 88.13229089439999 | 9.428517287385805 | 9.487907311509131 | 5.225509090909086e-2 | 8.012381818181835e-2 | 6.270545454545484e-2 | 7.125673553719011e-2 | 5.415497520661158e-2 | 7.378975206611554e-2 | 2.7305945259173503e-3 | 6.419826240033085e-3 | 3.931974029752103e-3 | 7.686441355646129e-3 | 3.738938656548464e-3 | 6.12853976859502e-3 | 8.767235228762901e-2 | 6.114686137937469e-2 | 7.828499069805794e-2 |
| user_003 | Sitting | 1.446047540852e12 | 0.537083 | -0.650847 | 9.31124 | 0.44650772727272725 | -0.6856831818181818 | 9.401814545454545 | 0.28845814888899995 | 0.42360181740899994 | 86.6991903376 | 9.349398392618532 | 9.437789541464076 | 9.057527272727273e-2 | 3.483618181818182e-2 | 9.057454545454569e-2 | 4.782117355371898e-2 | 5.732195867768597e-2 | 9.72251239669423e-2 | 8.203880029619835e-3 | 1.2135595636694218e-3 | 8.203748284297563e-3 | 3.1343671547543177e-3 | 4.990056541979716e-3 | 1.4301397123365868e-2 | 5.598541912636109e-2 | 7.064033226124942e-2 | 0.11958844895459539 |
| user_003 | Sitting | 1.446047540925e12 | 0.460442 | -0.574206 | 9.46452 | 0.4848280000000001 | -0.6891670000000001 | 9.454069090909089 | 0.21200683536400003 | 0.329712530436 | 89.5771388304 | 9.49309529058884 | 9.492107685859132 | 2.4386000000000074e-2 | 0.11496100000000009 | 1.0450909090911509e-2 | 4.433761157024795e-2 | 7.062329752066118e-2 | 5.478809917355385e-2 | 5.946769960000036e-4 | 1.321603152100002e-2 | 1.0922150082649681e-4 | 2.8111200184079647e-3 | 6.5169880832118745e-3 | 3.972794186626621e-3 | 5.301999640143296e-2 | 8.072786435433477e-2 | 6.303010539913939e-2 |
| user_003 | Sitting | 1.446047540933e12 | 0.345482 | -0.765807 | 9.4262 | 0.4813443636363636 | -0.699617909090909 | 9.450585454545454 | 0.119357812324 | 0.586460361249 | 88.85324643999999 | 9.463565111181566 | 9.48928338586127 | 0.13586236363636361 | 6.618909090909098e-2 | 2.4385454545454266e-2 | 5.098821487603307e-2 | 7.34735619834711e-2 | 5.1304462809917434e-2 | 1.8458581852859497e-2 | 4.3809957553719095e-3 | 5.946503933884161e-4 | 4.1317176202314045e-3 | 6.804936827912101e-3 | 3.6694011287753763e-3 | 6.427843822178168e-2 | 8.2492040997372e-2 | 6.057558195160304e-2 |
| user_003 | Sitting | 1.446047541118e12 | 0.383802 | -0.612526 | 9.38788 | 0.443024 | -0.6717485454545453 | 9.46452 | 0.147303975204 | 0.375188100676 | 88.13229089439999 | 9.415666889301043 | 9.499323719754216 | 5.9222e-2 | 5.9222545454545306e-2 | 7.664000000000115e-2 | 5.76385950413223e-2 | 7.030660330578511e-2 | 7.632330578512493e-2 | 3.5072452839999997e-3 | 3.507309890115685e-3 | 5.873689600000177e-3 | 5.4037578223200605e-3 | 7.833167167294515e-3 | 9.64789923966959e-3 | 7.351025657906562e-2 | 8.850518158443897e-2 | 9.822372035139776e-2 |
| user_003 | Sitting | 1.446047541158e12 | 0.537083 | -0.574206 | 9.50284 | 0.4674095454545455 | -0.6299444545454546 | 9.474970909090908 | 0.28845814888899995 | 0.329712530436 | 90.30396806560002 | 9.535310102189914 | 9.508476174425821 | 6.967345454545448e-2 | 5.573845454545456e-2 | 2.786909090909262e-2 | 5.9855570247933865e-2 | 0.10007608264462808 | 9.089123966942222e-2 | 4.854390268297512e-3 | 3.1067753151157044e-3 | 7.766862280992689e-4 | 6.554502585894815e-3 | 1.4869794603729529e-2 | 1.105123003816697e-2 | 8.095988257090554e-2 | 0.12194176726507423 | 0.10512483074025361 |
| user_003 | Sitting | 1.446047541175e12 | 0.460442 | -0.689167 | 9.46452 | 0.4778605454545454 | -0.6194935454545454 | 9.450585454545454 | 0.21200683536400003 | 0.47495115388899994 | 89.5771388304 | 9.500741908906535 | 9.483763584885539 | 1.741854545454541e-2 | 6.96734545454546e-2 | 1.393454545454631e-2 | 6.523947933884294e-2 | 8.677486776859503e-2 | 6.04885950413228e-2 | 3.0340572575206454e-4 | 4.8543902682975275e-3 | 1.9417155702481723e-4 | 6.91085958427573e-3 | 1.1449687157067612e-2 | 5.543818602554532e-3 | 8.313157994574463e-2 | 0.10700321096615564 | 7.445682374742112e-2 |
| user_003 | Sitting | 1.446047541192e12 | 0.498763 | -0.689167 | 9.34956 | 0.4848280000000001 | -0.6229772727272729 | 9.443618181818183 | 0.24876453016900002 | 0.47495115388899994 | 87.41427219360001 | 9.388183417342145 | 9.477402355192464 | 1.393499999999992e-2 | 6.61897272727271e-2 | 9.405818181818226e-2 | 6.492290082644625e-2 | 8.772497520661153e-2 | 5.352132231405013e-2 | 1.9418422499999777e-4 | 4.381079996437993e-3 | 8.846941566942232e-3 | 6.8987275471728005e-3 | 1.1558912227920352e-2 | 3.8139265490609105e-3 | 8.305857901007457e-2 | 0.10751238174238516 | 6.1756995952368915e-2 |
| user_003 | Sitting | 1.4460475412e12 | 0.498763 | -0.689167 | 9.27292 | 0.4778607272727273 | -0.6369119090909092 | 9.422716363636365 | 0.24876453016900002 | 0.47495115388899994 | 85.98704532639998 | 9.311861307518384 | 9.457088391091498 | 2.0902272727272686e-2 | 5.225509090909075e-2 | 0.14979636363636573 | 5.5422041322314014e-2 | 7.91742066115702e-2 | 6.397223140495938e-2 | 4.3690500516528754e-4 | 2.730594525917339e-3 | 2.243895055867831e-2 | 5.508618789685197e-3 | 9.86099776814199e-3 | 5.74350639699485e-3 | 7.422006999245688e-2 | 9.930255670496098e-2 | 7.578592479474569e-2 |
| user_003 | Sitting | 1.44604754196e12 | 0.460442 | -0.650847 | 9.46452 | 0.44999127272727274 | -0.678716 | 9.44361818181818 | 0.21200683536400003 | 0.42360181740899994 | 89.5771388304 | 9.49803913885245 | 9.478809400678257 | 1.0450727272727278e-2 | 2.7869000000000033e-2 | 2.0901818181819465e-2 | 4.528754545454547e-2 | 4.148710743801653e-2 | 1.9951735537190984e-2 | 1.0921770052892573e-4 | 7.766811610000018e-4 | 4.368860033058388e-4 | 2.986535792406464e-3 | 2.2738162678332076e-3 | 5.769984336589671e-4 | 5.4649206695124714e-2 | 4.7684549571461905e-2 | 2.40207916950913e-2 |
| user_003 | Sitting | 1.446047542413e12 | 0.422122 | -0.765807 | 9.4262 | 0.4534750000000001 | -0.6821996363636363 | 9.450585454545454 | 0.178186982884 | 0.586460361249 | 88.85324643999999 | 9.46667279376091 | 9.486175161191744 | 3.1353000000000075e-2 | 8.360736363636367e-2 | 2.4385454545454266e-2 | 2.7235900826446276e-2 | 2.8819264462809923e-2 | 2.4702148760330802e-2 | 9.830106090000048e-4 | 6.990191254223147e-3 | 5.946503933884161e-4 | 1.3062715649120968e-3 | 1.4066530360563494e-3 | 1.1032474830954288e-3 | 3.6142379071003296e-2 | 3.750537342910146e-2 | 3.3215169472628446e-2 |
| user_003 | Standing | 1.446047630907e12 | -0.689167 | -9.54116 | 0.344284 | -0.8041277272727272 | -9.4262 | 0.4035064545454546 | 0.47495115388899994 | 91.0337341456 | 0.11853147265599999 | 9.572210652307282 | 9.470404311628682 | 0.11496072727272721 | 0.11495999999999995 | 5.9222454545454606e-2 | 8.497683663911854e-2 | 6.639318273645532e-2 | 8.869623911845728e-2 | 1.3215968815074367e-2 | 1.3215801599999988e-2 | 3.507299122388437e-3 | 1.0229017505059551e-2 | 6.166949670227525e-3 | 1.3160927524382622e-2 | 0.10113860541385546 | 7.852992850007903e-2 | 0.11472108578802165 |
| user_003 | Standing | 1.446047631231e12 | -0.727487 | -9.50284 | 0.344284 | -0.8006440909090909 | -9.429683636363636 | 0.396539 | 0.529237335169 | 90.30396806560002 | 0.11853147265599999 | 9.536862003480234 | 9.472512914924552 | 7.315709090909095e-2 | 7.315636363636457e-2 | 5.2254999999999996e-2 | 6.460626446280994e-2 | 6.77725619834712e-2 | 4.845475206611569e-2 | 5.351959950280998e-3 | 5.351853540496005e-3 | 2.7305850249999997e-3 | 5.803188286028555e-3 | 5.375021737640885e-3 | 3.176314455302029e-3 | 7.617866030607623e-2 | 7.331453974240638e-2 | 5.635880104563997e-2 |
| user_003 | Standing | 1.446047631943e12 | -0.842448 | -9.38788 | 0.382604 | -0.7971605454545454 | -9.40529818181818 | 0.3860879090909091 | 0.7097186327039999 | 88.13229089439999 | 0.146385820816 | 9.433366066676305 | 9.44757028741933 | 4.5287454545454575e-2 | 1.741818181818111e-2 | 3.483909090909121e-3 | 6.428952066115706e-2 | 6.365553719008267e-2 | 5.130495867768594e-2 | 2.050953539206614e-3 | 3.0339305785121503e-4 | 1.2137622553719216e-5 | 6.695712793529687e-3 | 5.6033939666416425e-3 | 3.987201700823439e-3 | 8.182733524641803e-2 | 7.485582119409045e-2 | 6.314429270190172e-2 |
| user_003 | Standing | 1.446047632979e12 | -0.689167 | -9.38788 | 0.305964 | -0.7379380909090908 | -9.412265454545452 | 0.3617023636363636 | 0.47495115388899994 | 88.13229089439999 | 9.361396929600001e-2 | 9.418113187766698 | 9.448590535195043 | 4.877109090909082e-2 | 2.438545454545249e-2 | 5.5738363636363586e-2 | 5.415501652892563e-2 | 3.0085950413224018e-2 | 6.270585950413224e-2 | 2.378619308462801e-3 | 5.946503933883295e-4 | 3.1067651808594984e-3 | 4.3258806551938435e-3 | 1.1749585694966677e-3 | 4.971305307842974e-3 | 6.577142734648415e-2 | 3.427766867067636e-2 | 7.05074840555453e-2 |
| user_003 | Standing | 1.446047633237e12 | -0.689167 | -9.38788 | 0.382604 | -0.7483889999999999 | -9.422716363636363 | 0.36170236363636366 | 0.47495115388899994 | 88.13229089439999 | 0.146385820816 | 9.420914386040506 | 9.459752343029551 | 5.9221999999999886e-2 | 3.4836363636364e-2 | 2.0901636363636344e-2 | 4.750432231404958e-2 | 3.008595041322434e-2 | 5.732199173553716e-2 | 3.5072452839999863e-3 | 1.213572231404984e-3 | 4.3687840267768517e-4 | 3.7378388638985736e-3 | 1.0999377406462195e-3 | 4.3214836728707705e-3 | 6.1137867675431515e-2 | 3.31653092951991e-2 | 6.573799261363836e-2 |
| user_003 | Standing | 1.446047633367e12 | -0.804128 | -9.34956 | 0.305964 | -0.7449053636363636 | -9.422716363636363 | 0.36866963636363637 | 0.646621840384 | 87.41427219360001 | 9.361396929600001e-2 | 9.38906321223156 | 9.459703198070578 | 5.922263636363634e-2 | 7.31563636363628e-2 | 6.270563636363635e-2 | 4.4337380165289236e-2 | 3.6736528925621285e-2 | 5.352162809917354e-2 | 3.5073206578595012e-3 | 5.351853540495745e-3 | 3.931996831768593e-3 | 3.2524059982727287e-3 | 1.6526647296770385e-3 | 4.056701973697217e-3 | 5.702986935170664e-2 | 4.065297934564008e-2 | 6.369224421934916e-2 |
| user_003 | Standing | 1.446047634532e12 | -0.765807 | -9.46452 | 0.305964 | -0.7344543636363635 | -9.429683636363636 | 0.3477676363636364 | 0.586460361249 | 89.5771388304 | 9.361396929600001e-2 | 9.500379632464432 | 9.465045583166999 | 3.13526363636365e-2 | 3.4836363636364e-2 | 4.1803636363636376e-2 | 5.225490909090909e-2 | 3.1669421487604606e-2 | 5.447188429752066e-2 | 9.829878069504217e-4 | 1.213572231404984e-3 | 1.7475440132231415e-3 | 4.147163891128476e-3 | 1.3569944042074078e-3 | 3.7643580316784374e-3 | 6.43984773975944e-2 | 3.683740496027656e-2 | 6.1354364406115705e-2 |
| user_003 | Standing | 1.446047634662e12 | -0.535886 | -9.46452 | 0.420925 | -0.7065850909090909 | -9.4262 | 0.36170236363636366 | 0.287173804996 | 89.5771388304 | 0.177177855625 | 9.48901946941943 | 9.460098310075162 | 0.17069909090909097 | 3.8320000000000576e-2 | 5.922263636363634e-2 | 6.365589256198347e-2 | 3.451966942148859e-2 | 5.035481818181819e-2 | 2.9138179637190103e-2 | 1.4684224000000442e-3 | 3.5073206578595012e-3 | 6.600807368122467e-3 | 1.6449419972952627e-3 | 3.3550440600578517e-3 | 8.124535290170427e-2 | 4.055788452687421e-2 | 5.792274216624979e-2 |
| user_003 | Standing | 1.446047635634e12 | -0.765807 | -9.38788 | 0.420925 | -0.6543303636363635 | -9.419232727272727 | 0.35125145454545453 | 0.586460361249 | 88.13229089439999 | 0.177177855625 | 9.4284637726023 | 9.449174059864886 | 0.11147663636363647 | 3.135272727272742e-2 | 6.967354545454546e-2 | 6.302253719008266e-2 | 5.035438016528928e-2 | 8.265804958677685e-2 | 1.2427040454950437e-2 | 9.829935074380258e-4 | 4.854402936206612e-3 | 5.892509816134489e-3 | 4.032369550713747e-3 | 8.761074538072124e-3 | 7.676268505031913e-2 | 6.350094133722545e-2 | 9.36006118466761e-2 |
| user_003 | Standing | 1.446047636993e12 | -0.535886 | -9.38788 | 0.382604 | -0.6612977272727272 | -9.419232727272727 | 0.3512512727272728 | 0.287173804996 | 88.13229089439999 | 0.146385820816 | 9.410943125968405 | 9.449580293155417 | 0.1254117272727272 | 3.135272727272742e-2 | 3.13527272727272e-2 | 4.053711570247934e-2 | 2.818578512396722e-2 | 6.808980165289256e-2 | 1.572810133752891e-2 | 9.829935074380258e-4 | 9.82993507438012e-4 | 3.375969831589033e-3 | 1.227914448685197e-3 | 7.375343415595795e-3 | 5.810309657487312e-2 | 3.504161024675089e-2 | 8.587981960621363e-2 |
| user_003 | Standing | 1.44604763764e12 | -0.574206 | -9.38788 | 0.344284 | -0.647362909090909 | -9.412265454545452 | 0.3721534545454545 | 0.329712530436 | 88.13229089439999 | 0.11853147265599999 | 9.411723269279223 | 9.442694565988013 | 7.3156909090909e-2 | 2.438545454545249e-2 | 2.786945454545453e-2 | 6.713971074380164e-2 | 3.07193388429758e-2 | 7.790748760330576e-2 | 5.351933347735523e-3 | 5.946503933883295e-4 | 7.767064966611562e-4 | 5.882607076129975e-3 | 1.161719599699493e-3 | 8.702586125975955e-3 | 7.669815562404336e-2 | 3.408400797587474e-2 | 9.328765259119749e-2 |
| user_003 | Standing | 1.446047637899e12 | -0.689167 | -9.34956 | 0.267643 | -0.6299445454545455 | -9.422716363636363 | 0.33383318181818183 | 0.47495115388899994 | 87.41427219360001 | 7.163277544900001e-2 | 9.378744911923876 | 9.45028455359085 | 5.9222454545454495e-2 | 7.31563636363628e-2 | 6.619018181818181e-2 | 5.858897520661155e-2 | 4.3070413223140876e-2 | 6.713981818181818e-2 | 3.507299122388424e-3 | 5.351853540495745e-3 | 4.381140169123966e-3 | 5.521843364183319e-3 | 2.8949213956423483e-3 | 6.59534203249737e-3 | 7.430910687246428e-2 | 5.380447375118865e-2 | 8.121171117823692e-2 |
| user_003 | Standing | 1.446047638223e12 | -0.535886 | -9.46452 | 0.382604 | -0.6125263636363636 | -9.422716363636363 | 0.35821881818181817 | 0.287173804996 | 89.5771388304 | 0.146385820816 | 9.487396821900726 | 9.449822535242 | 7.664036363636362e-2 | 4.180363636363715e-2 | 2.4385181818181834e-2 | 5.3838578512396665e-2 | 5.0037685950412907e-2 | 4.465428925619834e-2 | 5.873745338314047e-3 | 1.7475440132232066e-3 | 5.946370923057859e-4 | 5.01765396693313e-3 | 3.3957957529676466e-3 | 2.419472113918106e-3 | 7.083540052073631e-2 | 5.827345667598282e-2 | 4.918812980707953e-2 |
| user_003 | Standing | 1.446047638418e12 | -0.650847 | -9.46452 | 0.420925 | -0.6299447272727271 | -9.419232727272725 | 0.33731672727272727 | 0.42360181740899994 | 89.5771388304 | 0.177177855625 | 9.496205479212946 | 9.446599682163269 | 2.0902272727272853e-2 | 4.528727272727551e-2 | 8.360827272727273e-2 | 3.357000826446282e-2 | 4.2437024793388935e-2 | 5.2888462809917346e-2 | 4.369050051652945e-4 | 2.050937071074632e-3 | 6.990343268438016e-3 | 1.615187078950413e-3 | 2.38963404838471e-3 | 3.374911324686701e-3 | 4.018939012911757e-2 | 4.888388331939997e-2 | 5.8093986992516715e-2 |
| user_003 | Standing | 1.446047638676e12 | -0.535886 | -9.38788 | 0.344284 | -0.5916243636363636 | -9.422716363636363 | 0.33731672727272727 | 0.287173804996 | 88.13229089439999 | 0.11853147265599999 | 9.409463118162055 | 9.447580888994437 | 5.573836363636364e-2 | 3.4836363636364e-2 | 6.967272727272711e-3 | 4.1170694214876034e-2 | 3.1352727272728226e-2 | 4.497091735537189e-2 | 3.1067651808595045e-3 | 1.213572231404984e-3 | 4.854288925619812e-5 | 2.55075781586326e-3 | 1.4309119855748112e-3 | 2.652259924018782e-3 | 5.0505027629566344e-2 | 3.7827397287876034e-2 | 5.150009634960678e-2 |
| user_003 | Standing | 1.44604763913e12 | -0.535886 | -9.46452 | 0.344284 | -0.5707222727272726 | -9.429683636363633 | 0.3129310909090909 | 0.287173804996 | 89.5771388304 | 0.11853147265599999 | 9.485928742513934 | 9.452492415666207 | 3.483627272727263e-2 | 3.483636363636755e-2 | 3.13529090909091e-2 | 4.465428099173552e-2 | 2.4385454545455557e-2 | 5.447174380165289e-2 | 1.213565897528919e-3 | 1.2135722314052313e-3 | 9.830049084628104e-4 | 2.854158935062359e-3 | 8.417778296018401e-4 | 4.706525393682193e-3 | 5.3424329055799656e-2 | 2.9013407755757337e-2 | 6.860412082143603e-2 |
| user_003 | Standing | 1.446047639194e12 | -0.650847 | -9.31124 | 0.114362 | -0.5742059999999999 | -9.419232727272725 | 0.29551272727272726 | 0.42360181740899994 | 86.6991903376 | 1.3078667044000002e-2 | 9.334659652180845 | 9.441912906219029 | 7.664100000000007e-2 | 0.10799272727272502 | 0.18115072727272724 | 5.0038123966942155e-2 | 3.420297520661206e-2 | 6.808974380165288e-2 | 5.873842881000011e-3 | 1.1662429143801166e-2 | 3.2815585991438e-2 | 3.360561736989483e-3 | 1.9019986608564915e-3 | 7.600397437682192e-3 | 5.797035222412818e-2 | 4.361190962175918e-2 | 8.718025830245166e-2 |
| user_003 | Standing | 1.446047640165e12 | -0.727487 | -9.2346 | 0.382604 | -0.6438792727272727 | -9.408781818181815 | 0.26067590909090915 | 0.529237335169 | 85.27783716 | 0.146385820816 | 9.27110890433205 | 9.435124200958072 | 8.360772727272725e-2 | 0.17418181818181466 | 0.12192809090909085 | 4.9404785123966934e-2 | 5.5738181818181846e-2 | 7.347362809917354e-2 | 6.9902520597107404e-3 | 3.033930578512274e-2 | 1.4866459352735522e-2 | 3.4002625236025552e-3 | 5.125687806461234e-3 | 9.953659155622087e-3 | 5.831177002632106e-2 | 7.15939090039176e-2 | 9.976802672009749e-2 |
| user_003 | Standing | 1.446047640294e12 | -0.842448 | -9.46452 | 0.382604 | -0.6508465454545456 | -9.41574909090909 | 0.28157790909090913 | 0.7097186327039999 | 89.5771388304 | 0.146385820816 | 9.509639492847244 | 9.443550494292248 | 0.1916014545454544 | 4.877090909091031e-2 | 0.10102609090909087 | 7.695737190082642e-2 | 6.460561983471083e-2 | 8.519137190082643e-2 | 3.671111738393383e-2 | 2.378601573553838e-3 | 1.0206271044371893e-2 | 9.261906457942896e-3 | 5.9509169238166106e-3 | 1.1252200041873776e-2 | 9.62387991297839e-2 | 7.714218640806475e-2 | 0.10607638776784292 |
| user_003 | Standing | 1.446047640683e12 | -0.650847 | -9.50284 | 0.382604 | -0.647363 | -9.426200000000001 | 0.28506163636363635 | 0.42360181740899994 | 90.30396806560002 | 0.146385820816 | 9.53278320868701 | 9.453927396237697 | 3.4839999999999316e-3 | 7.663999999999938e-2 | 9.754236363636365e-2 | 7.252361983471071e-2 | 6.36555371900822e-2 | 0.10514338016528924 | 1.2138255999999524e-5 | 5.8736895999999044e-3 | 9.514512703768598e-3 | 8.447700141810665e-3 | 5.77770706897057e-3 | 1.3642986799874529e-2 | 9.19113711235485e-2 | 7.601122988723817e-2 | 0.1168031968735211 |
| user_003 | Standing | 1.446047641654e12 | -0.842448 | -9.57948 | 0.267643 | -0.6508466363636364 | -9.44361818181818 | 0.2571923636363636 | 0.7097186327039999 | 91.7664370704 | 7.163277544900001e-2 | 9.620176114736829 | 9.470404615826363 | 0.1916013636363636 | 0.13586181818181942 | 1.0450636363636412e-2 | 8.170772727272727e-2 | 8.297388429752059e-2 | 4.592101652892564e-2 | 3.671108254731403e-2 | 1.8458433639669758e-2 | 1.0921580040495968e-4 | 1.1734313433839967e-2 | 1.353243362764841e-2 | 3.3395934129819694e-3 | 0.10832503604356643 | 0.11632898876741089 | 5.7789215369149716e-2 |
| user_003 | Standing | 1.446047642107e12 | -0.535886 | -9.46452 | 0.114362 | -0.6403955454545454 | -9.433167272727273 | 0.23977390909090912 | 0.287173804996 | 89.5771388304 | 1.3078667044000002e-2 | 9.480368732409094 | 9.460083485751973 | 0.10450954545454538 | 3.135272727272742e-2 | 0.1254119090909091 | 0.1121106033057851 | 0.1073593388429755 | 8.265771900826446e-2 | 1.0922245091115687e-2 | 9.829935074380258e-4 | 1.572814694182645e-2 | 1.9075439881976707e-2 | 1.8643779216829522e-2 | 1.8134347264641623e-2 | 0.1381138656398289 | 0.13654222503251337 | 0.13466383057317813 |
| user_003 | Standing | 1.446047642172e12 | -0.574206 | -9.54116 | 0.382604 | -0.636911909090909 | -9.443618181818179 | 0.25370854545454546 | 0.329712530436 | 91.0337341456 | 0.146385820816 | 9.566077173891708 | 9.470739264005575 | 6.2705909090909e-2 | 9.754181818182062e-2 | 0.12889545454545454 | 0.11401075206611568 | 0.11622677685950465 | 9.247529752066116e-2 | 3.932031034917345e-3 | 9.514406294215351e-3 | 1.6614038202479336e-2 | 1.9274024774400447e-2 | 1.9508725243576372e-2 | 1.960499642810594e-2 | 0.13883092153551546 | 0.13967363832726765 | 0.14001784324901573 |
| user_003 | Standing | 1.446047642949e12 | -0.535886 | -9.50284 | 0.229323 | -0.5672386363636363 | -9.440134545454546 | 0.250225 | 0.287173804996 | 90.30396806560002 | 5.2589038329e-2 | 9.52070012703504 | 9.460813853842183 | 3.135263636363628e-2 | 6.270545454545484e-2 | 2.0901999999999976e-2 | 6.048906611570246e-2 | 3.071933884297483e-2 | 4.560432231404959e-2 | 9.829878069504078e-4 | 3.931974029752103e-3 | 4.36893603999999e-4 | 4.64585674370473e-3 | 1.2985222876033022e-3 | 2.936898062541696e-3 | 6.816052188550738e-2 | 3.603501474404169e-2 | 5.4193155126285975e-2 |
| user_003 | Standing | 1.446047643791e12 | -0.420925 | -9.50284 | 0.382604 | -0.5358857272727273 | -9.422716363636361 | 0.2328068181818182 | 0.177177855625 | 90.30396806560002 | 0.146385820816 | 9.519849355007725 | 9.441925086561081 | 0.11496072727272733 | 8.01236363636395e-2 | 0.1497971818181818 | 7.63238595041322e-2 | 4.655404958677745e-2 | 5.890551239669421e-2 | 1.3215968815074391e-2 | 6.419797104132735e-3 | 2.2439195680669415e-2 | 1.429944640338467e-2 | 3.63961344673185e-3 | 5.249324134230651e-3 | 0.11958029270487955 | 6.032920890192287e-2 | 7.245221966393198e-2 |
| user_003 | Standing | 1.446047644761e12 | -0.497565 | -9.46452 | 0.267643 | -0.511499909090909 | -9.429683636363636 | 0.2502249090909091 | 0.24757092922499999 | 89.5771388304 | 7.163277544900001e-2 | 9.481368178436803 | 9.447264955731209 | 1.3934909090909053e-2 | 3.4836363636364e-2 | 1.741809090909091e-2 | 5.4471834710743815e-2 | 4.433719008264509e-2 | 3.483661157024793e-2 | 1.9418169137189978e-4 | 1.213572231404984e-3 | 3.0338989091735545e-4 | 4.018076853086403e-3 | 3.7731063921863624e-3 | 2.742723741573253e-3 | 6.338830217860708e-2 | 6.142561674241751e-2 | 5.237102005473307e-2 |
| user_003 | Standing | 1.446047646381e12 | -0.574206 | -9.46452 | 0.344284 | -0.4731796363636364 | -9.41574909090909 | 0.2711270909090909 | 0.329712530436 | 89.5771388304 | 0.11853147265599999 | 9.488170678981907 | 9.432077816933814 | 0.10102636363636358 | 4.877090909091031e-2 | 7.31569090909091e-2 | 6.682293388429751e-2 | 5.3204628099174235e-2 | 5.6688652892561975e-2 | 1.0206326149586765e-2 | 2.378601573553838e-3 | 5.35193334773554e-3 | 5.782216449574002e-3 | 3.949625989481666e-3 | 3.964020835802403e-3 | 7.604088669639512e-2 | 6.28460499115232e-2 | 6.296047042234042e-2 |
| user_003 | Standing | 1.446047646445e12 | -0.459245 | -9.38788 | 0.229323 | -0.48014700000000005 | -9.422716363636363 | 0.2746107272727273 | 0.210905970025 | 88.13229089439999 | 5.2589038329e-2 | 9.401903312774174 | 9.43942770260395 | 2.090200000000003e-2 | 3.4836363636364e-2 | 4.5287727272727285e-2 | 6.112236363636363e-2 | 4.687074380165367e-2 | 5.3521719008264475e-2 | 4.368936040000013e-4 | 1.213572231404984e-3 | 2.050978241528927e-3 | 5.186448298261457e-3 | 3.067028003005348e-3 | 3.5668501872141243e-3 | 7.20170000643005e-2 | 5.5380754807111e-2 | 5.972311267184694e-2 |
| user_003 | Standing | 1.44604764651e12 | -0.574206 | -9.54116 | 0.344284 | -0.4940816363636364 | -9.426199999999998 | 0.2885453636363636 | 0.329712530436 | 91.0337341456 | 0.11853147265599999 | 9.56462117120652 | 9.444022785372637 | 8.01243636363636e-2 | 0.11496000000000173 | 5.573863636363635e-2 | 6.523946280991734e-2 | 4.9720991735537984e-2 | 5.352174380165291e-2 | 6.419913648132226e-3 | 1.3215801600000398e-2 | 3.1067955836776846e-3 | 5.659752639225393e-3 | 3.632993961833309e-3 | 3.566852951106687e-3 | 7.52313275120504e-2 | 6.027432257465287e-2 | 5.9723135811063095e-2 |
| user_003 | Walking | 1.446047679584e12 | -2.56686 | -10.34589 | -0.268841 | -2.37526 | -10.115966666666667 | 0.944633 | 6.5887702596 | 107.03743989210001 | 7.2275483281e-2 | 10.662949199681156 | 10.471786160882395 | 0.19160000000000021 | 0.2299233333333337 | 1.213474 | 8.302666666666673e-2 | 0.22992277777777836 | 0.449198 | 3.671056000000008e-2 | 5.286473921111128e-2 | 1.4725191486759999 | 1.333817013333336e-2 | 8.810738774537086e-2 | 0.49683577435866666 | 0.11549099589722724 | 0.29682888630551246 | 0.7048657846417761 |
| user_003 | Walking | 1.446047679648e12 | -2.91174 | -9.2346 | -1.11189 | -2.50938 | -9.895624999999999 | 0.4305022499999999 | 8.4782298276 | 85.27783716 | 1.2362993721000002 | 9.746402739457261 | 10.290440305526111 | 0.40235999999999983 | 0.6610249999999986 | 1.54239225 | 0.16286 | 0.33769833333333343 | 0.7224965624999999 | 0.16189356959999987 | 0.4369540506249982 | 2.3789738528600624 | 5.0477019999999984e-2 | 0.1753190534652777 | 0.9673702939840156 | 0.22467091489554225 | 0.41871118144286246 | 0.9835498431620106 |
| user_003 | Walking | 1.446047679714e12 | -2.0687 | -9.57948 | -0.958607 | -2.421244 | -9.832396 | 0.15268039999999994 | 4.279519690000001 | 91.7664370704 | 0.918927380449 | 9.847074902774377 | 10.201767224975764 | 0.35254399999999997 | 0.25291599999999903 | 1.1112874 | 0.2007968 | 0.32074186666666654 | 0.80025473 | 0.12428727193599998 | 6.39665030559995e-2 | 1.2349596853987599 | 6.523907038719998e-2 | 0.15304854338342205 | 1.0208881722669645 | 0.25541940096085103 | 0.3912141911835792 | 1.0103901089514706 |
| user_003 | Walking | 1.446047680749e12 | -0.995729 | -8.39155 | 1.30229 | -2.7027253636363637 | -9.175375454545453 | -0.10162490909090907 | 0.991476241441 | 70.4181114025 | 1.6959592440999998 | 8.550178178730604 | 10.034046038882586 | 1.7069963636363639 | 0.7838254545454522 | 1.403914909090909 | 1.6648737190082643 | 0.7987106611570245 | 2.2143453057851237 | 2.9138365854677692 | 0.6143823431933847 | 1.9709770719677355 | 4.793715662380766 | 1.7562285270555222 | 6.099185223480105 | 2.18945556300665 | 1.3252277264891201 | 2.469652854852298 |
| user_003 | Walking | 1.446047680879e12 | -2.14534 | -9.96268 | 1.26397 | -1.8561953636363635 | -9.262466363636365 | 0.9121214545454546 | 4.6024837156 | 99.25499278240001 | 1.5976201609 | 10.269133199004676 | 9.595484485092896 | 0.2891446363636365 | 0.7002136363636353 | 0.35184854545454547 | 0.9500893388429752 | 0.8227794214876031 | 1.808972809917355 | 8.36046207378596e-2 | 0.4902991365495853 | 0.12379739893847935 | 1.1134641619282062 | 1.7791144310392937 | 4.574982385900888 | 1.0552081130886959 | 1.3338344841243586 | 2.1389208461046163 |
| user_003 | Walking | 1.446047681331e12 | -2.41358 | -7.24194 | -0.422122 | -2.5459590000000003 | -9.168406363636365 | 0.3164146363636364 | 5.8253684164 | 52.4456949636 | 0.178186982884 | 7.645210942994575 | 9.622119321250128 | 0.13237900000000025 | 1.926466363636365 | 0.7385366363636364 | 0.9576910165289256 | 1.0479508264462807 | 1.194580123966942 | 1.7524199641000066e-2 | 3.7112726502223192 | 0.5454363632513142 | 1.6246740838005926 | 1.4249213524337336 | 1.748376268873452 | 1.2746270371369786 | 1.1937006963362857 | 1.3222618004288909 |
| user_003 | Walking | 1.446047681915e12 | -1.72382 | -8.50651 | 0.152682 | -2.9430970909090903 | -8.931518181818182 | -0.7008159090909089 | 2.9715553923999996 | 72.36071238010001 | 2.3311793124000002e-2 | 8.680759158369964 | 9.863686110314818 | 1.2192770909090904 | 0.4250081818181819 | 0.8534979090909088 | 1.7291638099173554 | 1.0723361157024789 | 1.766850909090909 | 1.4866366244157343 | 0.18063195461239676 | 0.7284586808225533 | 5.3292291302925765 | 2.027118169973629 | 4.631297047916401 | 2.3085123197186053 | 1.4237690016198656 | 2.152044852673011 |
| user_003 | Walking | 1.44604768308e12 | -1.26397 | -7.97003 | 0.650847 | -2.6156328181818185 | -9.304270909090908 | -0.5266319090909091 | 1.5976201609 | 63.5213782009 | 0.42360181740899994 | 8.095838448191083 | 10.195105110337266 | 1.3516628181818184 | 1.3342409090909078 | 1.177478909090909 | 1.7788858099173555 | 0.9665576033057852 | 1.6474559008264462 | 1.8269923740552156 | 1.780198803491732 | 1.3864565813539171 | 6.173944335417497 | 1.5980932189392185 | 5.193812224823833 | 2.4847423076483195 | 1.2641571179798887 | 2.2789936868766953 |
| user_003 | Walking | 1.446047683597e12 | -3.71647 | -10.65245 | 0.727487 | -1.5635655454545452 | -9.558577272727275 | 0.9086370909090907 | 13.812149260900002 | 113.4746910025 | 0.529237335169 | 11.305577278430722 | 9.886379907429324 | 2.152904454545455 | 1.0938727272727249 | 0.18115009090909073 | 1.3937824462809922 | 0.8601490909090902 | 1.4929075371900828 | 4.634997590401663 | 1.1965575434710691 | 3.281535543637184e-2 | 2.867568569354223 | 1.4904467409425983 | 3.32652396830064 | 1.693389668491639 | 1.2208385400791533 | 1.8238760835924792 |
| user_003 | Walking | 1.446047683727e12 | -1.8771 | -9.34956 | -7.7239e-2 | -2.159272727272727 | -9.314720909090909 | 0.8180616363636362 | 3.52350441 | 87.41427219360001 | 5.965863121e-3 | 9.536442862342385 | 9.649491378034925 | 0.282172727272727 | 3.483909090909165e-2 | 0.8953006363636362 | 1.074868801652893 | 0.6308609917355367 | 1.1185721487603304 | 7.962144801652879e-2 | 1.2137622553719527e-3 | 0.801563229473132 | 1.6366407131942973 | 0.6092058806637107 | 1.4778986670610335 | 1.2793125940106653 | 0.780516419214683 | 1.2156885567697977 |
| user_003 | Walking | 1.446047684763e12 | -1.41725 | -8.54483 | 0.535886 | -2.7166602727272724 | -9.161439090909091 | -0.2618743636363637 | 2.0085975625 | 73.01411972889998 | 0.287173804996 | 8.678127165258411 | 9.973846093809122 | 1.2994102727272725 | 0.6166090909090922 | 0.7977603636363637 | 1.6227539752066116 | 0.7645065289256194 | 1.8409585537190083 | 1.6884670568691647 | 0.38020677099173716 | 0.6364215977892232 | 4.2806235768678 | 0.8269072979024034 | 4.606044262947701 | 2.0689667896966832 | 0.9093444330408601 | 2.1461696724508297 |
| user_003 | Walking | 1.446047685086e12 | -2.18366 | -9.57948 | 0.689167 | -2.6051827272727275 | -9.175373636363636 | 0.6926499090909091 | 4.768370995600001 | 91.7664370704 | 0.47495115388899994 | 9.849353238659328 | 9.64232140030103 | 0.4215227272727273 | 0.4041063636363642 | 3.482909090909092e-3 | 1.1331395454545456 | 0.5349014876033061 | 1.1983802809917354 | 0.17768140960743806 | 0.16330195313140541 | 1.2130655735537198e-5 | 1.9829541042932683 | 0.45450676466250944 | 1.7708770012784947 | 1.4081740319624092 | 0.6741711686675049 | 1.3307430260115942 |
| user_003 | Walking | 1.446047687546e12 | -1.8771 | -8.96635 | 1.30229 | -2.5424754545454546 | -9.11615 | 0.152682 | 3.52350441 | 80.3954323225 | 1.6959592440999998 | 9.252831781492626 | 9.79489048617874 | 0.6653754545454547 | 0.14979999999999905 | 1.149608 | 1.3972666942148761 | 0.45952801652892605 | 1.826388785123967 | 0.4427244955115704 | 2.2440039999999713e-2 | 1.3215985536639998 | 2.848263819498798 | 0.4674070052281754 | 4.43694763761664 | 1.687680010991064 | 0.683671708664455 | 2.1064063325048754 |
| user_003 | Walking | 1.446047687999e12 | -2.10702 | -8.50651 | -1.07357 | -1.922383636363636 | -9.408779090909091 | 0.4174404545454545 | 4.439533280399999 | 72.36071238010001 | 1.1525525448999998 | 8.829088186523 | 9.707851614316736 | 0.18463636363636393 | 0.9022690909090905 | 1.4910104545454543 | 1.0020287520661157 | 0.6609490909090908 | 1.251585561983471 | 3.409058677685961e-2 | 0.8140895124099166 | 2.2231121755638426 | 1.3171834867227503 | 0.8584210792066113 | 1.7665195376922902 | 1.1476861446940754 | 0.926510161415735 | 1.32910478807816 |
| user_003 | Walking | 1.446047688063e12 | -2.60518 | -8.27659 | -0.422122 | -2.033860909090909 | -9.349557272727274 | 0.28157754545454544 | 6.7869628323999995 | 68.5019420281 | 0.178186982884 | 8.687179740478724 | 9.670566566573536 | 0.5713190909090908 | 1.0729672727272739 | 0.7036995454545454 | 0.9193704876033059 | 0.7575411570247934 | 1.1682942396694216 | 0.32640550363719 | 1.151258768343804 | 0.4951930502729338 | 1.1475787221806768 | 0.9630710310996242 | 1.5729835182858294 | 1.0712510080185114 | 0.9813618247617054 | 1.254186396946574 |
| user_003 | Walking | 1.446047689099e12 | -4.78944 | -11.15061 | 1.11069 | -1.6820099090909089 | -9.286850909090909 | 1.4033162727272726 | 22.938735513599998 | 124.33610337210001 | 1.2336322760999998 | 12.18640517797599 | 9.603071921107578 | 3.107430090909091 | 1.8637590909090918 | 0.2926262727272726 | 1.4004335206611571 | 0.6150263636363636 | 1.4010657520661156 | 9.65612176988728 | 3.473597948946284 | 8.563013549025612e-2 | 2.525419334934868 | 0.6853445784876789 | 2.4557176957492683 | 1.5891567999838367 | 0.8278554091673732 | 1.5670729707800044 |
| user_003 | Walking | 1.446047689423e12 | -2.2603 | -7.39522 | -0.958607 | -2.3439056363636364 | -9.23807909090909 | 0.2850610909090909 | 5.1089560899999995 | 54.6892788484 | 0.918927380449 | 7.792121811089006 | 9.686183649343109 | 8.360563636363638e-2 | 1.8428590909090907 | 1.243668090909091 | 1.0967207190082644 | 0.9507247933884297 | 1.283254867768595 | 6.989902431768598e-3 | 3.39612962894628 | 1.5467103203454629 | 1.9360527094687148 | 1.2617876186290011 | 1.9974827090385192 | 1.3914211114787338 | 1.123293202431583 | 1.4133232853945763 |
| user_003 | Walking | 1.446047691882e12 | -4.09967 | -9.2346 | -2.18486 | -2.939613636363637 | -9.248530909090908 | 1.0514659999999998 | 16.8072941089 | 85.27783716 | 4.7736132196000005 | 10.33725033500205 | 9.866399368302162 | 1.1600563636363628 | 1.3930909090907662e-2 | 3.236326 | 0.8816842975206609 | 0.5000657851239663 | 1.2335327851239668 | 1.3457307668132212 | 1.9407022809913373e-4 | 10.473805978276 | 0.9549407656270471 | 0.49907817604537946 | 2.182993822858251 | 0.9772107068729072 | 0.7064546525045889 | 1.4774957945314942 |
| user_003 | Walking | 1.446047692724e12 | -2.37526 | -9.08132 | 0.650847 | -3.1904381818181817 | -8.760819090909091 | -0.9829931818181816 | 5.6418600676 | 82.47037294239999 | 0.42360181740899994 | 9.40934826794125 | 9.711024072087437 | 0.8151781818181818 | 0.32050090909090834 | 1.6338401818181816 | 1.1299748760330577 | 0.9947448760330573 | 1.8054882066115703 | 0.6645154681123967 | 0.10272083272809869 | 2.6694337397236687 | 3.8177683449762583 | 1.624831435103606 | 4.349292087728119 | 1.9539110381427958 | 1.2746887600915002 | 2.085495645578796 |
| user_003 | Walking | 1.446047694212e12 | -1.68549 | -8.39155 | 0.995729 | -3.0232225454545447 | -8.931517272727273 | -0.272325 | 2.8408765400999996 | 70.4181114025 | 0.991476241441 | 8.616870904454878 | 9.798154136854562 | 1.3377325454545448 | 0.5399672727272726 | 1.268054 | 1.7348647107438018 | 0.6739317355371907 | 1.7291648925619834 | 1.7895283631682957 | 0.29156465561652883 | 1.6079609469160001 | 5.105803910511268 | 0.6571788245633365 | 3.537602261767337 | 2.259602600129339 | 0.8106656675617493 | 1.8808514725430439 |
| user_003 | Walking | 1.446047694665e12 | -2.87342 | -9.31124 | -1.26517 | -2.444933818181818 | -9.474969999999999 | 0.41395727272727273 | 8.2565424964 | 86.6991903376 | 1.6006551288999997 | 9.826311004792185 | 9.933644266781107 | 0.42848618181818177 | 0.16372999999999927 | 1.6791272727272726 | 1.1565773719008263 | 0.6859657024793389 | 1.47200647107438 | 0.18360040800912392 | 2.680751289999976e-2 | 2.8194683980165283 | 1.6581222852926367 | 1.0010658402313288 | 2.4672273237565943 | 1.287680971860902 | 1.000532778189365 | 1.5707410110379731 |
| user_003 | Walking | 1.446047695248e12 | -3.25663 | -8.65979 | 0.727487 | -3.033673545454545 | -9.036027272727273 | -1.1223383636363635 | 10.6056389569 | 74.99196284409999 | 0.529237335169 | 9.28045468369783 | 9.887957901050113 | 0.22295645454545499 | 0.37623727272727336 | 1.8498253636363635 | 1.4678886198347103 | 1.2683712396694213 | 1.649039 | 4.9709580623479535e-2 | 0.1415544853892567 | 3.4218538759524044 | 4.119139327655233 | 2.252980369876934 | 3.138702060940305 | 2.029566290529884 | 1.500993127857997 | 1.7716382421195094 |
| user_004 | Jogging | 1.446046318092e12 | 5.59536 | -5.47921 | 4.55952 | 5.389392875 | -5.934259999999999 | -1.1645778749999995 | 31.308053529600002 | 30.021742224100002 | 20.7892226304 | 9.061954446150125 | 12.216535243673478 | 0.20596712499999992 | 0.45504999999999907 | 5.724097875 | 5.715480538541667 | 4.204357976190476 | 2.761588395684523 | 4.242245658076559e-2 | 0.20707050249999914 | 32.765296482579515 | 52.835246199173355 | 33.24591593153357 | 13.561606868199032 | 7.2687857444812165 | 5.765927152811902 | 3.6826087041931337 |
| user_004 | Jogging | 1.446046319776e12 | 0.575403 | 0.920286 | -2.4531 | 7.037601 | -5.81712190909091 | -0.8889327272727272 | 0.331088612409 | 0.8469263217960001 | 6.01769961 | 2.682482906600711 | 12.387770865882652 | 6.462198000000001 | 6.73740790909091 | 1.5641672727272729 | 6.405192909090909 | 5.481706280991736 | 3.3021975289256202 | 41.76000299120401 | 45.39266533348075 | 2.4466192570710747 | 61.41928641993893 | 42.6502568591944 | 14.266236440739085 | 7.837045771203517 | 6.530716412400281 | 3.777067174507105 |
| user_004 | Jogging | 1.446046320682e12 | 15.40536 | -14.7144 | -4.9056 | 5.773029999999999 | -4.29127709090909 | -0.7077829999999999 | 237.3251167296 | 216.51356736 | 24.064911359999996 | 21.86100627715019 | 11.59551959765128 | 9.63233 | 10.42312290909091 | 4.197817 | 7.104774851239669 | 5.092802545454545 | 2.930712619834711 | 92.78178122889999 | 108.64149117801576 | 17.621667565489 | 68.18608075165695 | 45.272654416877344 | 13.356822954340474 | 8.257486345835332 | 6.728495702374889 | 3.6546987501489743 |
| user_004 | Jogging | 1.44604632094e12 | -7.89339 | -0.229323 | 6.89706 | 5.720775454545455 | -4.859112818181817 | -0.33851381818181814 | 62.3056056921 | 5.2589038329e-2 | 47.5694366436 | 10.484637875197645 | 12.382847689240384 | 13.614165454545455 | 4.629789818181817 | 7.235573818181818 | 7.7625545206611575 | 5.522876561983471 | 3.032688570247934 | 185.34550102373888 | 21.434953760540022 | 52.35352847835821 | 85.137659610978 | 47.519657698112105 | 15.47308031707065 | 9.227007077648635 | 6.893450347838309 | 3.9335836481598623 |
| user_004 | Jogging | 1.44604632107e12 | 8.50771 | -12.98999 | 2.18366 | 6.323450000000002 | -5.085551 | -1.3731619999999998 | 72.3811294441 | 168.73984020010002 | 4.768370995600001 | 15.680859052991964 | 12.889850411129373 | 2.1842599999999974 | 7.904439000000001 | 3.556822 | 7.966191876033057 | 5.821204644628099 | 2.5582766694214873 | 4.770991747599989 | 62.480155904721016 | 12.650982739684 | 87.93005493205713 | 47.961374134025576 | 11.119942162986327 | 9.377102693905892 | 6.925415087489383 | 3.3346577280114262 |
| user_004 | Jogging | 1.4460463223e12 | 7.62634 | -1.11069 | -7.7239e-2 | 6.981861818181819 | -5.113419999999999 | -0.8053254545454547 | 58.1610617956 | 1.2336322760999998 | 5.965863121e-3 | 7.707182360293611 | 13.166977051043297 | 0.6444781818181813 | 4.002729999999999 | 0.7280864545454546 | 7.87466743801653 | 5.880742809917354 | 2.431282611570248 | 0.4153521268396687 | 16.02184745289999 | 0.5301098852925703 | 96.87531135707144 | 50.59894899318002 | 9.012174346215128 | 9.8425256594571 | 7.113293821653933 | 3.002028371987035 |
| user_004 | Jogging | 1.446046322495e12 | 12.4547 | -8.73643 | -4.40743 | 5.699873636363636 | -4.232053636363636 | -1.1153708181818183 | 155.11955209 | 76.3252091449 | 19.425439204899998 | 15.838882550224305 | 12.631746355363127 | 6.754826363636365 | 4.504376363636364 | 3.2920591818181815 | 8.404817429752066 | 5.924762809917355 | 2.7349940082644633 | 45.62767920287688 | 20.289406425285957 | 10.837653656593394 | 99.4303642243738 | 46.63110414598136 | 12.545256132475203 | 9.971477534667258 | 6.828697104571366 | 3.5419283070772627 |
| user_004 | Jogging | 1.446046322818e12 | 2.22318 | 4.714 | -0.690364 | 6.887803636363637 | -5.479206363636364 | -0.9342202727272728 | 4.9425293124000005 | 22.221796000000005 | 0.476602452496 | 5.257464005097515 | 13.652307201253643 | 4.664623636363637 | 10.193206363636364 | 0.24385627272727284 | 7.565568826446282 | 6.1325166942148766 | 3.4842991157024787 | 21.758713668922322 | 103.90145597167687 | 5.9465881748438074e-2 | 85.44219569961149 | 49.359338763878434 | 17.138670611506832 | 9.243494777388664 | 7.025620169342948 | 4.139887753491251 |
| user_004 | Jogging | 1.446046323919e12 | -12.14694 | 3.90927 | 0.152682 | 8.263850909090909 | -4.988009090909092 | -1.6344380000000003 | 147.54815136360003 | 15.282391932899998 | 2.3311793124000002e-2 | 12.761420574905602 | 13.32878135637509 | 20.41079090909091 | 8.897279090909091 | 1.7871200000000003 | 7.503497272727272 | 5.000009421487603 | 2.6817890330578518 | 416.6003855346281 | 79.1615752215281 | 3.1937978944000007 | 83.64378627905913 | 43.36608320371898 | 10.340309980211263 | 9.145697692306427 | 6.585292947448806 | 3.215635237431519 |
| user_004 | Jogging | 1.446046324307e12 | 3.14286 | -4.06135 | -2.41478 | 7.974707272727273 | -5.670806363636364 | -0.7391351818181818 | 9.877568979600001 | 16.4945638225 | 5.8311624484 | 5.674794732014543 | 13.21582437877326 | 4.831847272727273 | 1.6094563636363644 | 1.675644818181818 | 7.802776611570248 | 4.166146611570248 | 3.0396570413223145 | 23.34674806696199 | 2.5903497864495892 | 2.807785556699578 | 88.72848169522757 | 33.40847150884982 | 14.554685959196457 | 9.419579698438119 | 5.7800061858833525 | 3.8150604135709902 |
| user_004 | Jogging | 1.446046326574e12 | 12.0715 | -5.70913 | -2.29982 | 7.38248581818182 | -5.493142727272727 | -0.5266323636363637 | 145.72111225 | 32.5941653569 | 5.2891720324 | 13.55007194221861 | 13.15136563242288 | 4.68901418181818 | 0.21598727272727292 | 1.7731876363636363 | 7.681798446280993 | 5.468403966942149 | 2.036678347107438 | 21.98685399729202 | 4.665050198016537e-2 | 3.1441943937528594 | 85.34078258003082 | 48.436344589920964 | 6.831764064025652 | 9.238007500539865 | 6.959622445932032 | 2.6137643474547687 |
| user_004 | Jogging | 1.446046326639e12 | 1.61005 | 0.268841 | -2.8363 | 7.281459454545455 | -6.033110818181819 | -0.7112664545454546 | 2.5922610025 | 7.2275483281e-2 | 8.04459769 | 3.2724813484237 | 12.828283111638287 | 5.671409454545455 | 6.301951818181819 | 2.1250335454545457 | 7.794859388429751 | 5.05574826446281 | 2.2292298016528926 | 32.16488520110758 | 39.71459671868514 | 4.515767569307116 | 86.48260182948019 | 41.36213986419955 | 7.242283975172693 | 9.299602240390724 | 6.431340440701265 | 2.6911491922917787 |
| user_004 | Jogging | 1.44604632761e12 | 15.32872 | -14.67608 | -5.0972 | 5.4734360909090904 | -3.793111272727273 | -0.34896536363636366 | 234.96965683840003 | 215.38732416640002 | 25.98144784 | 21.8251787815083 | 11.905846007617221 | 9.855283909090911 | 10.882968727272727 | 4.7482346363636365 | 7.350532371900827 | 5.82310479338843 | 3.0558068677685952 | 97.12662092878622 | 118.43900831879617 | 22.545732161963315 | 77.70046956614337 | 45.631422866974525 | 15.833528605261323 | 8.814786983594292 | 6.755103468265643 | 3.9791366658185194 |
| user_004 | Jogging | 1.446046327869e12 | -10.65245 | 4.13919 | 5.36425 | 6.501117909090909 | -4.55603409090909 | -0.4186382727272728 | 113.4746910025 | 17.1328938561 | 28.7751780625 | 12.624688626698878 | 12.71974471624847 | 17.15356790909091 | 8.69522409090909 | 5.782888272727273 | 7.723284404958677 | 6.096096619834711 | 2.903793123966942 | 294.24489201179347 | 75.6069219911258 | 33.441796774846615 | 82.30222588031641 | 49.14387324683357 | 13.352193441058244 | 9.072057422675211 | 7.010269127988852 | 3.654065330704727 |
| user_004 | Jogging | 1.446046327934e12 | 12.22478 | -6.05401 | -4.63736 | 6.678785181818182 | -3.8593013636363636 | -1.0247964545454544 | 149.4452460484 | 36.6510370801 | 21.505107769600002 | 14.408379190530072 | 12.460816653562178 | 5.545994818181819 | 2.1947086363636363 | 3.6125635454545457 | 7.868965173553719 | 5.46998726446281 | 2.945280504132232 | 30.75805852329958 | 4.816745998529132 | 13.050615369947117 | 83.68466370606724 | 42.08347585402162 | 13.633011805578548 | 9.147932209306497 | 6.487177803484472 | 3.6922908614542473 |
| user_004 | Jogging | 1.446046328581e12 | -3.17999 | -1.68549 | -0.1922 | 8.981486363636364 | -6.047043636363636 | -1.160660090909091 | 10.1123364001 | 2.8408765400999996 | 3.694084e-2 | 3.604185591808502 | 13.103828097993564 | 12.161476363636364 | 4.361553636363636 | 0.9684600909090909 | 6.4954519752066116 | 5.569113595041323 | 2.5399093471074377 | 147.90150734328597 | 19.02315012287686 | 0.9379149476836446 | 54.42817332005659 | 46.03700027441227 | 9.23046015355527 | 7.377545209624715 | 6.785057131256322 | 3.0381672359426286 |
| user_004 | Jogging | 1.446046328905e12 | 19.50564 | -5.67081 | 1.03405 | 7.100308181818182 | -4.7511209090909094 | -0.4221227272727273 | 380.4699918096 | 32.158086056100004 | 1.0692594024999997 | 20.33955105866892 | 13.054363658769024 | 12.405331818181818 | 0.9196890909090909 | 1.4561727272727272 | 8.1137726446281 | 5.9206470247933884 | 2.656452793388429 | 153.8922575191942 | 0.84582802393719 | 2.120439011652892 | 88.05080876348978 | 50.20722639022751 | 9.792734096364068 | 9.383539245055129 | 7.08570577925922 | 3.1293344494259587 |
| user_004 | Jogging | 1.446046329617e12 | 3.56439 | -7.05034 | 6.05401 | 5.84618909090909 | -4.925304545454545 | -0.7042981818181816 | 12.7048760721 | 49.70729411560001 | 36.6510370801 | 9.953050148964387 | 12.291283422299722 | 2.28179909090909 | 2.125035454545455 | 6.758308181818181 | 6.877387685950413 | 6.140116363636364 | 2.6222487603305784 | 5.20660709127355 | 4.5157756830752085 | 45.67472948043057 | 77.1762593682838 | 52.764850806034104 | 10.386124266575505 | 8.785001956077403 | 7.263941822869598 | 3.2227510401170467 |
| user_004 | Jogging | 1.446046329683e12 | 19.00747 | -7.08866 | -0.1922 | 6.992314545454545 | -4.904402727272727 | -0.44650727272727264 | 361.28391580090005 | 50.2491005956 | 3.694084e-2 | 20.28718702128267 | 13.209857240769736 | 12.015155454545457 | 2.1842572727272733 | 0.2543072727272726 | 7.863264297520661 | 6.199972148760332 | 2.42336347107438 | 144.36396059689346 | 4.770979833461986 | 6.467218896198342e-2 | 90.17570123666434 | 52.98692180883688 | 9.849859374475129 | 9.496088733613663 | 7.279211620006447 | 3.1384485617061064 |
| user_004 | Jogging | 1.446046330071e12 | 17.05314 | -7.05034 | -3.75599 | 7.476543181818181 | -5.385147272727274 | -0.7356514545454546 | 290.80958385959997 | 49.70729411560001 | 14.107460880100001 | 18.831472031025616 | 13.604796241409561 | 9.576596818181818 | 1.6651927272727267 | 3.0203385454545457 | 6.897656694214876 | 5.849389256198347 | 2.7007900661157027 | 91.71120661801011 | 2.7728668189619814 | 9.122444929158481 | 82.83997545198206 | 51.75787714759219 | 10.641467584078235 | 9.101646853838158 | 7.194294763741071 | 3.2621262366864707 |
| user_004 | Jogging | 1.446046330265e12 | 4.02423 | -3.52487 | -1.76333 | 6.295580454545455 | -4.420171818181819 | -1.073566909090909 | 16.1944270929 | 12.424708516899999 | 3.1093326889000004 | 5.632802881221745 | 11.50053752911144 | 2.2713504545454546 | 0.8953018181818191 | 0.689763090909091 | 6.918558636363636 | 5.134288347107438 | 2.0914646446280996 | 5.159032887363844 | 0.8015653456396711 | 0.475773121580463 | 77.31788883703922 | 43.5971327069604 | 7.869029743331553 | 8.793059128485332 | 6.602812484612932 | 2.8051790929157363 |
| user_004 | Jogging | 1.446046330329e12 | 12.18646 | -8.04667 | 4.48288 | 7.079405 | -4.510747272727273 | -1.2163969090909088 | 148.5098073316 | 64.7488980889 | 20.0962130944 | 15.27595884109734 | 11.984438319305344 | 5.107055 | 3.5359227272727276 | 5.699276909090909 | 7.175400082644629 | 5.26255082644628 | 1.995189074380165 | 26.082010773024997 | 12.502749533243804 | 32.48175728649682 | 79.21565280810752 | 44.32322123879391 | 6.669668634792123 | 8.900317567823494 | 6.657568718293031 | 2.582570160671753 |
| user_004 | Jogging | 1.446046330848e12 | 16.01849 | -5.05768 | -2.33814 | 8.019996363636364 | -5.001943636363635 | -0.8088077272727273 | 256.5920218801 | 25.580126982400003 | 5.466898659600001 | 16.95992474989497 | 12.894361379172755 | 7.998493636363635 | 5.573636363636503e-2 | 1.529332272727273 | 6.6452502066115695 | 5.332539917355372 | 2.045543966942149 | 63.975900450949574 | 3.1065422314051137e-3 | 2.3388572004051658 | 71.60663987257274 | 44.567811622551844 | 7.075141314559144 | 8.462070661048202 | 6.675912793210517 | 2.6599137795348073 |
| user_004 | Jogging | 1.446046331495e12 | 8.62267 | -8.96635 | -0.460442 | 6.427961 | -3.901106363636364 | -0.32458081818181816 | 74.35043792889999 | 80.3954323225 | 0.21200683536400003 | 12.448207786133874 | 11.40122148597141 | 2.1947089999999996 | 5.065243636363636 | 0.13586118181818185 | 6.147401809917355 | 4.683945950413223 | 1.9023978347107438 | 4.816747594680998 | 25.65669309572231 | 1.845826072503307e-2 | 68.3485394276605 | 34.49821943043216 | 5.597293831373793 | 8.267317547285849 | 5.873518488132318 | 2.3658600616633674 |
| user_004 | Jogging | 1.446046331561e12 | 18.04947 | -17.93331 | -5.40376 | 6.612595545454545 | -5.071618181818182 | -0.6032735454545455 | 325.7833672809 | 321.6036075561 | 29.2006221376 | 26.01129748733423 | 12.22407355301134 | 11.436874454545453 | 12.861691818181818 | 4.800486454545455 | 6.45998188429752 | 5.848123719008263 | 2.1997754876033055 | 130.80209728903435 | 165.42311642588513 | 23.044670200274393 | 74.42364823112277 | 49.53640214712795 | 7.47964046772554 | 8.626914177799774 | 7.038210152242398 | 2.734893136436146 |
| user_004 | Jogging | 1.44604633318e12 | 18.31771 | -7.47186 | -3.10454 | 7.713432727272728 | -4.754604545454545 | -0.7042989090909091 | 335.53849964410006 | 55.828691859600006 | 9.638168611600001 | 20.02511822974586 | 13.149988530002542 | 10.604277272727273 | 2.7172554545454553 | 2.400241090909091 | 6.938827685950414 | 5.259700247933884 | 3.0469385041322314 | 112.45069647688018 | 7.383477205257028 | 5.761157294488465 | 77.11467456979197 | 48.156471003066486 | 12.895906901866105 | 8.78149614643154 | 6.939486364498923 | 3.5910871476289885 |
| user_004 | Jogging | 1.446046334476e12 | 6.13185 | -5.93905 | -3.0279 | 7.685562727272727 | -6.245612363636363 | -0.4499909090909093 | 37.5995844225 | 35.2723149025 | 9.16817841 | 9.05759779052923 | 13.862535036033972 | 1.5537127272727274 | 0.30656236363636324 | 2.5779090909090905 | 7.415138760330578 | 5.167541801652892 | 3.0304713057851242 | 2.4140232388892566 | 9.398048279831381e-2 | 6.645615280991733 | 82.42637556285928 | 43.10521410638309 | 12.187500293637099 | 9.078897265794964 | 6.565456123254735 | 3.4910600529978137 |
| user_004 | Jumping | 1.446046440753e12 | 19.54396 | -19.58108 | -6.89825 | 4.1884622857142855 | -2.999333571428571 | -1.5224617142857144 | 381.96637248159993 | 383.4186939664 | 47.5858530625 | 28.512644905559004 | 7.446083883383553 | 15.355497714285713 | 16.581746428571428 | 5.375788285714286 | 3.529288130612245 | 4.4176791921768706 | 1.270878112244898 | 235.79131005343376 | 274.9543146214413 | 28.899099692822944 | 40.42154505965868 | 49.56948207287595 | 4.601596063441028 | 6.357794040361695 | 7.040559784056659 | 2.14513311089103 |
| user_004 | Jumping | 1.446046441725e12 | 0.767005 | 1.30349 | 1.03405 | 3.717668909090908 | -2.779365454545454 | -0.7217180909090909 | 0.5882966700250001 | 1.6990861801000001 | 1.0692594024999997 | 1.832114148361122 | 6.847435681349492 | 2.950663909090908 | 4.082855454545454 | 1.7557680909090907 | 4.667160314049585 | 5.080450140495867 | 1.426719958677686 | 8.706417504411638 | 16.669708662711567 | 3.082721589054553 | 34.66505868718512 | 41.30950542823461 | 3.54571694005584 | 5.887704025100542 | 6.427247111184897 | 1.8830074190124264 |
| user_004 | Jumping | 1.446046441855e12 | 19.50564 | -19.58108 | -6.28513 | 6.448862999999999 | -5.51752609090909 | -1.188529 | 380.4699918096 | 383.4186939664 | 39.5028591169 | 28.34416244825202 | 10.589792564338788 | 13.056777 | 14.06355390909091 | 5.096601 | 6.050492041322314 | 6.463148644628098 | 1.8384256611570249 | 170.479425627729 | 197.7835485539062 | 25.975341753200997 | 53.52722088151006 | 62.083623176617515 | 5.872172381026635 | 7.31622996368417 | 7.879316161737484 | 2.423256565249878 |
| user_004 | Jumping | 1.446046442243e12 | 0.575403 | 1.15021 | -0.690364 | 3.6410292727272737 | -2.856005545454545 | -0.826227181818182 | 0.331088612409 | 1.3229830441 | 0.476602452496 | 1.4596828796026209 | 7.0567311800225605 | 3.0656262727272736 | 4.006215545454545 | 0.13586318181818202 | 4.304226818181818 | 5.246715801652892 | 1.354511909090909 | 9.398064444035716 | 16.049762996641654 | 1.8458804173760387e-2 | 29.45904764510686 | 39.08100872537067 | 3.7849847946391066 | 5.427618966462814 | 6.251480522673862 | 1.9455037380172537 |
| user_004 | Jumping | 1.446046442372e12 | 18.16443 | -17.3585 | 1.60885 | 5.591881181818182 | -4.914851 | -0.906352 | 329.9465172249 | 301.31752224999997 | 2.5883983225 | 25.17642623164376 | 9.544468036485442 | 12.572548818181819 | 12.443649 | 2.515202 | 5.156457942148761 | 5.815502611570248 | 1.753866247933884 | 158.06898378556505 | 154.844400435201 | 6.326241100803999 | 42.76716719331571 | 50.12337746871192 | 4.928973948004476 | 6.539661091625139 | 7.079786541182716 | 2.2201292638052577 |
| user_004 | Jumping | 1.446046443215e12 | -0.880768 | -0.612526 | -1.91661 | 4.623420909090909 | -3.2287572727272735 | -1.8016529999999997 | 0.775752269824 | 0.375188100676 | 3.6733938920999996 | 2.196436719461774 | 8.431501330251807 | 5.504188909090908 | 2.6162312727272736 | 0.1149570000000002 | 6.099263876033058 | 4.956621512396695 | 1.8605928016528923 | 30.296095546959364 | 6.84466607239617 | 1.3215111849000045e-2 | 44.71450237822124 | 32.7193750866241 | 6.223155739080556 | 6.686890336936986 | 5.720085234209723 | 2.494625370487632 |
| user_004 | Jumping | 1.446046444575e12 | 0.881966 | -4.94272 | 3.79311 | 6.790261454545456 | -6.263030272727272 | -0.8749987272727274 | 0.7778640251560001 | 24.430480998399997 | 14.387683472099999 | 6.292537524374089 | 11.126299442112229 | 5.908295454545455 | 1.3203102727272729 | 4.6681087272727275 | 6.9910826033057845 | 6.443830049586777 | 1.6366885537190081 | 34.90795517820249 | 1.7432192162691655 | 21.791239089639802 | 58.32619930429955 | 59.72487196810328 | 3.9530999690558626 | 7.637159112150247 | 7.728186848679532 | 1.9882404203354942 |
| user_004 | Jumping | 1.446046444769e12 | 0.268841 | -1.95374 | 3.7722e-2 | 3.2195042727272734 | -2.3926784545454542 | -0.9725412727272725 | 7.2275483281e-2 | 3.8170999876000002 | 1.422949284e-3 | 1.9725106894932154 | 7.197199404975865 | 2.9506632727272732 | 0.4389384545454542 | 1.0102632727272725 | 5.604267272727273 | 5.468403644628099 | 1.521094487603306 | 8.706413749021623 | 0.1926669668787518 | 1.0206318802216192 | 38.98070197949247 | 47.5268644494463 | 3.7172557078802515 | 6.243452729018814 | 6.893973052561658 | 1.9280185963522891 |
| user_004 | Jumping | 1.446046445287e12 | 0.11556 | -1.11069 | 0.382604 | 3.397171909090909 | -2.2568159090909092 | -0.9237708181818182 | 1.3354113599999998e-2 | 1.2336322760999998 | 0.146385820816 | 1.180411881724341 | 7.075012334908058 | 3.281611909090909 | 1.1461259090909093 | 1.3063748181818182 | 4.881565661157025 | 5.449401462809918 | 1.427669438016529 | 10.768976721887281 | 1.3136045994894632 | 1.7066151655795787 | 33.09439934785701 | 45.55143298526623 | 2.6018435675532396 | 5.752773187590226 | 6.749180171344237 | 1.6130231143890157 |
| user_004 | Jumping | 1.446046445611e12 | 2.8363 | -10.65245 | 1.80046 | 6.518536181818181 | -6.203808181818181 | -0.9899598181818184 | 8.04459769 | 113.4746910025 | 3.2416562115999996 | 11.16964390229608 | 10.831203183334122 | 3.682236181818181 | 4.448641818181819 | 2.7904198181818183 | 6.020091264462809 | 6.157535694214877 | 1.3522956859504134 | 13.558863298690936 | 19.790414026476036 | 7.786442761701852 | 50.54368804665865 | 54.31564822609823 | 2.9312400316853586 | 7.1094084174886625 | 7.36991507590815 | 1.712086455669035 |
| user_004 | Jumping | 1.44604644574e12 | -0.765807 | 2.33814 | -1.64837 | 3.5330348181818176 | -2.9117445454545456 | -0.8122925454545453 | 0.586460361249 | 5.466898659600001 | 2.7171236568999997 | 2.9615000722183007 | 7.545395689822752 | 4.298841818181818 | 5.249884545454545 | 0.8360774545454546 | 4.913235181818182 | 5.647971008264463 | 1.154676611570248 | 18.480040977748757 | 27.561287740602477 | 0.6990255099992067 | 32.672137932914204 | 43.8247559736747 | 2.23586034254455 | 5.715954682545533 | 6.620026886174609 | 1.495279352677803 |
| user_004 | Jumping | 1.446046446387e12 | 1.30349 | -1.57053 | -1.76333 | 3.4668452727272734 | -2.702724454545454 | -0.9307371818181818 | 1.6990861801000001 | 2.4665644809 | 3.1093326889000004 | 2.6972177053215414 | 6.522886174272082 | 2.1633552727272733 | 1.132194454545454 | 0.8325928181818183 | 4.403352826446281 | 4.768820842975207 | 1.425451826446281 | 4.680106036036896 | 1.2818642829034783 | 0.6932108008879423 | 29.814088068487422 | 33.13063707336669 | 2.8420858909499063 | 5.460227840345806 | 5.755921913418101 | 1.6858487153211306 |
| user_004 | Jumping | 1.446046446712e12 | -2.10702 | -2.18366 | 4.06135 | 5.431632909090909 | -5.245799181818182 | -0.7913907272727272 | 4.439533280399999 | 4.768370995600001 | 16.4945638225 | 5.069760161832116 | 9.726178424784191 | 7.538652909090908 | 3.0621391818181816 | 4.852740727272727 | 6.073928570247935 | 6.269962735537191 | 2.0376260165289257 | 56.83128768374481 | 9.376696368826122 | 23.54909256613144 | 51.70801020713879 | 53.619849938282414 | 5.52751424180559 | 7.190828200363209 | 7.322557609079113 | 2.3510666179003925 |
| user_004 | Jumping | 1.446046447294e12 | 0.690364 | 6.13185 | -2.41478 | 4.372599 | -3.9324581818181814 | -0.8088090909090909 | 0.476602452496 | 37.5995844225 | 5.8311624484 | 6.626262092869252 | 9.25374689473398 | 3.6822350000000004 | 10.06430818181818 | 1.605970909090909 | 5.767999487603306 | 6.21485658677686 | 1.7959872231404963 | 13.558854595225002 | 101.29029917861237 | 2.5791425608462806 | 46.44946369914939 | 57.41211919025554 | 6.140247961835453 | 6.815384339796942 | 7.577078539269309 | 2.477952372794008 |
| user_004 | Jumping | 1.446046447618e12 | 14.94552 | -13.18159 | -4.67568 | 4.574651181818182 | -3.8662689090909086 | -1.0700833636363638 | 223.36856807040002 | 173.7543149281 | 21.861983462399998 | 20.469119826238256 | 8.255475939941938 | 10.370868818181819 | 9.31532109090909 | 3.605596636363636 | 5.605217123966942 | 5.653037636363636 | 1.7095292644628097 | 107.55492004393595 | 86.77520702673573 | 13.000327104156765 | 46.00866792942744 | 48.25952326499671 | 5.097796350635938 | 6.782968961260802 | 6.946907460517717 | 2.2578300092424888 |
| user_004 | Jumping | 1.446046449302e12 | 19.08411 | -17.97163 | -4.06255 | 6.292096636363637 | -5.238832727272728 | -1.3313578181818182 | 364.2032544921 | 322.97948485690006 | 16.5043125025 | 26.527100328748713 | 10.742951779313438 | 12.79201336363636 | 12.732797272727273 | 2.731192181818182 | 6.28168061983471 | 6.237977859504133 | 1.770017694214876 | 163.63560589545125 | 162.12412638837108 | 7.459410734024761 | 57.181595329683844 | 55.44002913204303 | 5.704743067659652 | 7.561851316290466 | 7.445806143866696 | 2.3884603969209226 |
| user_004 | Jumping | 1.446046449884e12 | 9.77228 | -10.88237 | 0.305964 | 6.661365181818183 | -5.935567 | -1.4289012727272727 | 95.4974563984 | 118.4259768169 | 9.361396929600001e-2 | 14.629321487498865 | 10.730218018448529 | 3.1109148181818176 | 4.946803 | 1.7348652727272729 | 5.927614074380164 | 5.7094111404958685 | 1.4390697024793389 | 9.677791005983211 | 24.470859920809 | 3.009757514515075 | 50.27496977080698 | 50.6429279554915 | 3.326290719735871 | 7.090484452476218 | 7.1163844721523795 | 1.823812139376167 |
| user_004 | Jumping | 1.446046449949e12 | -2.0687 | 0.575403 | 1.07237 | 5.222611545454545 | -4.946205818181818 | -1.0387312727272728 | 4.279519690000001 | 0.331088612409 | 1.1499774169 | 2.4001220217541026 | 9.358456768935065 | 7.291311545454546 | 5.521608818181818 | 2.111101272727273 | 5.7591315454545455 | 5.5906493801652895 | 1.4365365619834711 | 53.16322405287876 | 30.488163941023213 | 4.456748583710711 | 47.505802565996596 | 49.17626807639828 | 3.315524702677734 | 6.892445325571803 | 7.012579274161418 | 1.8208582324491203 |
| user_004 | Jumping | 1.446046450273e12 | 1.03525 | 0.996927 | -2.33814 | 3.6863156363636365 | -2.5598956363636356 | -1.1815612727272728 | 1.0717425625 | 0.993863443329 | 5.466898659600001 | 2.7445408842698993 | 6.7919229626582425 | 2.6510656363636365 | 3.5568226363636355 | 1.1565787272727273 | 4.373900206611571 | 4.6421415041322325 | 1.104005090909091 | 7.028149008308133 | 12.650987266548762 | 1.3376743523798016 | 30.204080410236873 | 35.12321301457914 | 2.2349330079064456 | 5.495823906407198 | 5.926484034786489 | 1.4949692330969375 |
| user_004 | Jumping | 1.446046450403e12 | 15.2904 | -16.4005 | -3.44943 | 6.609110272727273 | -5.649906818181818 | -1.5125094545454545 | 233.79633216 | 268.97640025000004 | 11.8985673249 | 22.686368147742378 | 10.73749835209278 | 8.681289727272727 | 10.750593181818182 | 1.9369205454545455 | 5.950099537190083 | 6.178438495867769 | 1.3551456363636365 | 75.36479132885098 | 115.57525376095559 | 3.751661199403934 | 52.523254056726394 | 57.86450307308849 | 2.7311461983206873 | 7.247292877807989 | 7.6068720952234035 | 1.6526179831772034 |
| user_004 | Jumping | 1.446046450531e12 | 0.422122 | 6.63001 | -3.37279 | 3.9545577272727273 | -3.2078577272727276 | -0.9237703636363637 | 0.178186982884 | 43.95703260010001 | 11.375712384100002 | 7.450565882339677 | 8.460411268528444 | 3.5324357272727274 | 9.837867727272728 | 2.4490196363636363 | 5.265083752066116 | 5.7806671487603305 | 1.6712091074380166 | 12.478102167312802 | 96.78364141931428 | 5.997697179294677 | 39.42287793470751 | 48.03158359135643 | 4.1075853388824015 | 6.278764045153115 | 6.9304822048221455 | 2.0267178735291207 |
| user_004 | Jumping | 1.446046450921e12 | 19.54396 | -18.12491 | -4.9056 | 6.191071363636365 | -5.245801363636364 | -1.7528832727272723 | 381.96637248159993 | 328.5123625081 | 24.064911359999996 | 27.10246568763993 | 10.580288705372087 | 13.352888636363634 | 12.879108636363636 | 3.1527167272727272 | 6.375738710743801 | 6.294983049586777 | 1.9831561570247935 | 178.29963493512906 | 165.8714392672564 | 9.939622762425255 | 57.35020530279037 | 57.54984604088095 | 5.6981677095347285 | 7.572991833006977 | 7.586161482652538 | 2.3870835154084427 |
| user_004 | Jumping | 1.446046451179e12 | -0.689167 | -1.41725 | -0.537083 | 3.553937181818182 | -2.316038363636364 | -1.435869 | 0.47495115388899994 | 2.0085975625 | 0.28845814888899995 | 1.6649344927888305 | 7.489633255365871 | 4.2431041818181825 | 0.898788363636364 | 0.8987860000000001 | 4.800173760330579 | 5.271100983471074 | 1.8400087190082643 | 18.003933097762946 | 0.807820522608133 | 0.8078162737960002 | 35.33453433582328 | 40.798926170358726 | 4.5008850493141415 | 5.944285855830226 | 6.387403711239703 | 2.1215289414274183 |
| user_004 | Jumping | 1.446046451309e12 | 5.99e-4 | 2.22318 | -0.613724 | 3.790826000000001 | -3.0301892727272732 | -1.1258224545454545 | 3.5880100000000004e-7 | 4.9425293124000005 | 0.37665714817600005 | 2.306336232941112 | 7.049070078541021 | 3.790227000000001 | 5.253369272727273 | 0.5120984545454544 | 4.614273231404958 | 4.632957049586778 | 1.697811479338843 | 14.36582071152901 | 27.597888715635076 | 0.26224482714784286 | 34.150457092262734 | 33.510629728040335 | 4.005569173775021 | 5.843839242506824 | 5.788836647206443 | 2.0013918091605705 |
| user_004 | Jumping | 1.446046451762e12 | 0.920286 | -2.41358 | 0.344284 | 3.5051649090909085 | -2.3717772727272726 | -1.3940651818181817 | 0.8469263217960001 | 5.8253684164 | 0.11853147265599999 | 2.6059213746488976 | 7.351790595676024 | 2.5848789090909086 | 4.180272727272749e-2 | 1.7383491818181818 | 5.23436547107438 | 4.90373214876033 | 1.4349531900826447 | 6.681598974663006 | 1.7474680074380348e-3 | 3.021857877927942 | 39.88697797769975 | 39.20278665489539 | 2.894475197684697 | 6.315613824300829 | 6.2612128741079704 | 1.7013157254562414 |
| user_004 | Jumping | 1.446046451892e12 | -0.382604 | 0.690364 | 7.6042e-2 | 3.557419909090909 | -2.660921272727273 | -1.2129147272727272 | 0.146385820816 | 0.476602452496 | 5.782385764e-3 | 0.7929506031752546 | 6.9635497729535 | 3.9400239090909093 | 3.3512852727272726 | 1.2889567272727271 | 5.185593942148761 | 4.676977388429751 | 1.325692917355372 | 15.523788404208009 | 11.23111297919871 | 1.6614094447816194 | 39.5295646746675 | 36.15230679208021 | 2.620653828008208 | 6.287254144272164 | 6.0126788365985595 | 1.6188433611712432 |
| user_004 | Jumping | 1.446046452021e12 | 19.54396 | -19.08292 | -7.43474 | 6.243325454545455 | -5.102970363636363 | -1.8922294545454543 | 381.96637248159993 | 364.15783572640004 | 55.2753588676 | 28.30900152028679 | 10.349931781329445 | 13.300634545454542 | 13.979949636363639 | 5.542510545454546 | 6.506536148760332 | 5.7812990247933875 | 1.7316982231404958 | 176.90687931173878 | 195.43899183526383 | 30.719423146474846 | 58.50151760448834 | 53.12020252076061 | 5.359819226407735 | 7.648628478654741 | 7.288360756765584 | 2.315128339079226 |
| user_004 | Jumping | 1.446046452538e12 | 18.54763 | -15.40417 | -3.98591 | 4.9648212727272725 | -3.9847141818181813 | -1.3940644545454548 | 344.01457861690005 | 237.28845338890002 | 15.8874785281 | 24.437481673321006 | 8.936126658691224 | 13.58280872727273 | 11.41945581818182 | 2.5918455454545453 | 5.416781504132231 | 5.744247553719009 | 1.6895778347107437 | 184.49269292167622 | 130.4039711834066 | 6.717663331492569 | 45.55107155931277 | 48.760047922413534 | 5.977856426539219 | 6.749153395746222 | 6.982839531480981 | 2.444965526656607 |
| user_004 | Jumping | 1.446046452993e12 | 0.613724 | 2.10822 | -1.38013 | 3.665413818181818 | -2.7480122727272733 | -1.157175909090909 | 0.37665714817600005 | 4.444591568400001 | 1.9047588169000003 | 2.5934547486848505 | 7.334066225954348 | 3.051689818181818 | 4.856232272727274 | 0.22295409090909102 | 4.790039247933884 | 5.016162074380166 | 1.2848392396694217 | 9.312810746394577 | 23.582991886677906 | 4.970852665309922e-2 | 36.88789352842561 | 41.51884982458453 | 3.4438666456618092 | 6.073540444289937 | 6.443512227394662 | 1.8557657841607624 |
| user_004 | Jumping | 1.446046453445e12 | 3.0279 | -1.45557 | -7.7239e-2 | 3.675864363636364 | -2.6922731818181824 | -1.1362745454545455 | 9.16817841 | 2.1186840249000003 | 5.965863121e-3 | 3.3604803671530354 | 7.688581707506595 | 0.6479643636363641 | 1.2367031818181824 | 1.0590355454545455 | 5.11655426446281 | 4.447690247933884 | 1.4346366611570247 | 0.4198578165426783 | 1.5294347599192164 | 1.1215562865362068 | 38.29682912226736 | 35.61574287410279 | 4.090531801082638 | 6.1884431905179 | 5.967892666101058 | 2.0225063166978337 |
| user_004 | Jumping | 1.44604645351e12 | -7.6042e-2 | 0.11556 | -1.26517 | 3.8639823636363633 | -2.8873586363636368 | -1.0631172727272726 | 5.782385764e-3 | 1.3354113599999998e-2 | 1.6006551288999997 | 1.2727103473548094 | 7.464115552925086 | 3.9400243636363634 | 3.0029186363636367 | 0.20205272727272727 | 4.969606842975207 | 4.325445000000001 | 1.371297 | 15.523791986048131 | 9.017520336620043 | 4.082530459834711e-2 | 36.90134728909755 | 34.7171749005811 | 4.02080485908967 | 6.0746479148258095 | 5.892128214879671 | 2.005194469144993 |
| user_004 | Jumping | 1.446046453704e12 | 19.54396 | -17.70339 | -8.46939 | 6.215456545454546 | -4.782473272727273 | -1.665791818181818 | 381.96637248159993 | 313.41001749209994 | 71.73056697210001 | 27.69669577667704 | 10.319630313832954 | 13.328503454545451 | 12.920916727272726 | 6.803598181818183 | 6.116683256198346 | 5.222330438016529 | 1.9416679917355373 | 177.64900433783004 | 166.95008907311612 | 46.288948219639686 | 53.542354201833035 | 48.31052397701225 | 8.222915800069886 | 7.3172641199995665 | 6.950577240561553 | 2.8675626933111484 |
| user_004 | Jumping | 1.446046454158e12 | 0.652044 | 1.72501 | -1.03525 | 2.9965505454545447 | -2.4797716363636364 | -1.2582024545454547 | 0.42516137793599995 | 2.9756595001 | 1.0717425625 | 2.114843597180652 | 6.536228248549308 | 2.3445065454545446 | 4.204781636363636 | 0.22295245454545465 | 4.096157082644628 | 4.746651454545454 | 1.610085991735537 | 5.496710941679202 | 17.680188609500856 | 4.970779698784302e-2 | 26.08166873535217 | 35.08718337156006 | 6.381092379007582 | 5.107021513108416 | 5.923443539999353 | 2.5260824173030425 |
| user_004 | Jumping | 1.446046454222e12 | 18.04947 | -16.01729 | -3.67935 | 4.644324363636363 | -3.946394363636364 | -1.4776733636363637 | 325.7833672809 | 256.55357894409997 | 13.5376164225 | 24.410542039198965 | 8.639667493262413 | 13.405145636363637 | 12.070895636363636 | 2.2016766363636364 | 4.956622652892562 | 5.571013 | 1.7918699834710743 | 179.69792953211905 | 145.70652146398265 | 4.847380011109496 | 41.00659033044952 | 47.5134562013203 | 6.818051897781323 | 6.403638835103798 | 6.89300052236472 | 2.6111399613542976 |
| user_004 | Jumping | 1.446046454805e12 | 19.54396 | -19.54276 | -2.60638 | 5.665038272727272 | -4.580421272727273 | -1.592634181818182 | 381.96637248159993 | 381.91946841760006 | 6.793216704400001 | 27.761106923240654 | 10.240967917439297 | 13.878921727272726 | 14.962338727272728 | 1.0137458181818182 | 5.758814264462809 | 6.220874892561983 | 1.9220324628099172 | 192.62446831176294 | 223.87158018964527 | 1.027680583881124 | 52.80492603983436 | 63.4497100096025 | 6.33968770209533 | 7.266699803888582 | 7.965532625606557 | 2.5178736469678795 |
| user_004 | Jumping | 1.44604645487e12 | 12.22478 | -14.1396 | -1.03525 | 6.717105181818181 | -6.022658545454546 | -1.592634181818182 | 149.4452460484 | 199.92828816 | 1.0717425625 | 18.720183673535363 | 11.750544288016998 | 5.50767481818182 | 8.116941454545454 | 0.557384181818182 | 6.046375016528926 | 6.576525785123966 | 1.9524353471074383 | 30.334481902834142 | 65.88473857651847 | 0.3106771261411241 | 55.06290521812116 | 67.83194182478591 | 6.36341218656381 | 7.420438344068439 | 8.236014923783584 | 2.522580461861189 |
| user_004 | Jumping | 1.446046454999e12 | 1.11189 | 5.2888 | -2.49142 | 3.543486090909091 | -2.371778181818182 | -0.9168032727272727 | 1.2362993721000002 | 27.97140544 | 6.207173616400001 | 5.951040113165093 | 8.1335275932362 | 2.431596090909091 | 7.660578181818182 | 1.5746167272727276 | 4.734616950413223 | 5.498490280991735 | 2.0813304214876034 | 5.912659549324371 | 58.684458079748765 | 2.4794178378070755 | 34.00437737975152 | 47.93823792689663 | 7.181088815206571 | 5.831327239981608 | 6.923744501849893 | 2.679755364806006 |
| user_004 | Jumping | 1.446046455711e12 | 1.64837 | -0.650847 | -0.613724 | 3.362334090909091 | -2.775883545454545 | -1.0178284545454546 | 2.7171236568999997 | 0.42360181740899994 | 0.37665714817600005 | 1.8754686407628893 | 7.188170906724486 | 1.7139640909090912 | 2.1250365454545452 | 0.4041044545454545 | 4.886314818181819 | 4.410319421487603 | 1.4837242231404957 | 2.937672904925827 | 4.515780319517387 | 0.16330041018347932 | 35.089401725842805 | 36.05135667432639 | 3.7502605393120323 | 5.923630789122733 | 6.004278197612631 | 1.9365589428964025 |
| user_004 | Jumping | 1.446046455906e12 | 19.54396 | -18.43147 | -6.89825 | 5.710325181818182 | -4.698866090909092 | -1.634437181818182 | 381.96637248159993 | 339.7190863609 | 47.5858530625 | 27.735740695085102 | 9.91661877902701 | 13.833634818181817 | 13.732603909090908 | 5.263812818181818 | 5.815186834710744 | 5.599832173553719 | 1.9872717603305785 | 191.3694522828123 | 188.5844101239789 | 27.707725384855213 | 50.6927278488261 | 52.74739577233272 | 6.313621098201948 | 7.119882572685177 | 7.262740238527929 | 2.5126920022561356 |
| user_004 | Jumping | 1.44604645597e12 | 8.77595 | -13.18159 | 0.804128 | 6.455829454545454 | -6.026142454545454 | -1.568247545454546 | 77.0172984025 | 173.7543149281 | 0.646621840384 | 15.85617340883304 | 11.218761559924888 | 2.3201205454545457 | 7.155447545454546 | 2.372375545454546 | 5.748047107438017 | 5.903544363636365 | 2.0901981487603303 | 5.382959345440299 | 51.200429575751485 | 5.628165728670754 | 50.331598154741954 | 56.07913702398294 | 6.685448936200232 | 7.094476594840662 | 7.488600471649088 | 2.585623510142231 |
| user_004 | Jumping | 1.446046456423e12 | 15.78857 | -13.10495 | -5.59536 | 4.7453508181818185 | -3.9289750909090913 | -1.4672216363636366 | 249.2789426449 | 171.73971450250002 | 31.308053529600002 | 21.267973826319235 | 8.458796354972607 | 11.04321918181818 | 9.17597490909091 | 4.1281383636363636 | 4.97340756198347 | 5.5171746115702485 | 2.1848901239669414 | 121.952689897677 | 84.19851553226592 | 17.041526349326315 | 38.41901825757145 | 44.11913462622769 | 6.885287929257149 | 6.1983076930377905 | 6.6422236206128815 | 2.623983218173689 |
| user_004 | Jumping | 1.446046457136e12 | -0.842448 | -0.919089 | 3.48655 | 5.034495545454545 | -4.845178090909091 | -0.8018418181818183 | 0.7097186327039999 | 0.8447245899210001 | 12.1560309025 | 3.7027657399739726 | 9.638416900898887 | 5.876943545454545 | 3.9260890909090906 | 4.288391818181818 | 5.478537785123968 | 5.961183520661158 | 2.0959002066115704 | 34.538465436459845 | 15.41417554975537 | 18.39030438624876 | 46.56730431938241 | 52.07823859486755 | 7.056477213755475 | 6.824024056184328 | 7.216525382403054 | 2.6564030593559167 |
| user_004 | Standing | 1.446046405596e12 | 0.958607 | -9.4262 | 3.7722e-2 | 0.9620908181818184 | -9.380912727272724 | 0.17358445454545454 | 0.918927380449 | 88.85324643999999 | 1.422949284e-3 | 9.47489296877453 | 9.43237148191842 | 3.483818181818421e-3 | 4.528727272727551e-2 | 0.13586245454545454 | 6.277442520989121e-2 | 3.564091774891797e-2 | 8.517581524662207e-2 | 1.2136989123968609e-5 | 2.050937071074632e-3 | 1.84586065551157e-2 | 5.963056016724221e-3 | 1.957546352325278e-3 | 1.0145801756643359e-2 | 7.722082631469454e-2 | 4.424416743849157e-2 | 0.1007263707111666 |
| user_004 | Standing | 1.44604640592e12 | 0.920286 | -9.38788 | -5.99e-4 | 0.9551232727272727 | -9.380912727272726 | 7.952554545454546e-2 | 0.8469263217960001 | 88.13229089439999 | 3.5880100000000004e-7 | 9.432879601425908 | 9.430044911924979 | 3.483727272727266e-2 | 6.967272727273155e-3 | 8.012454545454546e-2 | 3.9903884297520766e-2 | 3.578644628099232e-2 | 8.487475206611572e-2 | 1.2136355710743755e-3 | 4.854288925620431e-5 | 6.4199427842975216e-3 | 2.228623198332091e-3 | 1.6228770476333844e-3 | 8.828314650245684e-3 | 4.720829586346124e-2 | 4.028494815229858e-2 | 9.395911158714564e-2 |
| user_004 | Standing | 1.446046406114e12 | 1.03525 | -9.38788 | -0.11556 | 0.9760256363636363 | -9.387879999999997 | 2.7270636363636358e-2 | 1.0717425625 | 88.13229089439999 | 1.3354113599999998e-2 | 9.445495623338141 | 9.439062086037746 | 5.922436363636374e-2 | 1.7763568394002505e-15 | 0.14283063636363635 | 5.573941322314055e-2 | 3.0085950413224018e-2 | 9.37422396694215e-2 | 3.507525248132244e-3 | 3.1554436208840472e-30 | 2.040059068404132e-2 | 4.640469144164543e-3 | 1.4684224000000342e-3 | 1.0355239224131481e-2 | 6.812098901340571e-2 | 3.8320000000000444e-2 | 0.10176069587090823 |
| user_004 | Standing | 1.446046406438e12 | 1.03525 | -9.34956 | -3.8919e-2 | 1.010862818181818 | -9.387879999999997 | -3.543536363636363e-2 | 1.0717425625 | 87.41427219360001 | 1.514688561e-3 | 9.40678103522459 | 9.4424099981621 | 2.4387181818182002e-2 | 3.831999999999702e-2 | 3.4836363636363693e-3 | 4.4338446280991796e-2 | 2.786909090909068e-2 | 6.872322314049586e-2 | 5.947346370330668e-4 | 1.4684223999997718e-3 | 1.2135722314049627e-5 | 3.3783190145950433e-3 | 1.381265848835436e-3 | 6.272074051694967e-3 | 5.812330870309297e-2 | 3.7165385089292916e-2 | 7.919642701343897e-2 |
| user_004 | Standing | 1.446046409221e12 | 1.07357 | -9.34956 | -7.7239e-2 | 1.0387325454545453 | -9.366978181818181 | -9.814136363636364e-2 | 1.1525525448999998 | 87.41427219360001 | 5.965863121e-3 | 9.411311842757152 | 9.4250039711814 | 3.4837454545454616e-2 | 1.741818181818111e-2 | 2.0902363636363636e-2 | 2.977019008264463e-2 | 3.895338842975155e-2 | 3.2303239669421495e-2 | 1.2136482392066164e-3 | 3.0339305785121503e-4 | 4.369088055867768e-4 | 1.2335131438189323e-3 | 2.1502293445529123e-3 | 1.3349670551164533e-3 | 3.5121405777943066e-2 | 4.6370565497445816e-2 | 3.653720097539566e-2 |
| user_004 | Standing | 1.446046409545e12 | 1.03525 | -9.46452 | -0.15388 | 1.0457003636363635 | -9.366978181818181 | -0.12252709090909092 | 1.0717425625 | 89.5771388304 | 2.3679054399999996e-2 | 9.52221405174763 | 9.426078761809261 | 1.0450363636363535e-2 | 9.754181818181884e-2 | 3.135290909090907e-2 | 2.7236024793388432e-2 | 3.2619504132230824e-2 | 2.881955371900826e-2 | 1.092101001322293e-4 | 9.514406294215004e-3 | 9.830049084628087e-4 | 1.2334795734898574e-3 | 1.730995300976674e-3 | 9.763978611780614e-4 | 3.512092785633457e-2 | 4.1605231653923935e-2 | 3.1247365667813685e-2 |
| user_004 | Standing | 1.446046410257e12 | 0.537083 | -9.54116 | -0.11556 | 1.0108632727272726 | -9.373945454545456 | -0.10510872727272727 | 0.28845814888899995 | 91.0337341456 | 1.3354113599999998e-2 | 9.556963241955522 | 9.432190682868356 | 0.47378027272727263 | 0.16721454545454328 | 1.0451272727272726e-2 | 0.12129503305785122 | 7.600661157024775e-2 | 6.999014049586777e-2 | 0.22446774682552884 | 2.7960704211569522e-2 | 1.0922910161983468e-4 | 4.670707975050865e-2 | 7.85070908970695e-3 | 8.712536485643125e-3 | 0.21611820781810276 | 8.860422726770405e-2 | 9.334096895599019e-2 |
| user_004 | Standing | 1.446046411099e12 | 0.843646 | -9.38788 | -0.11556 | 1.0770524545454547 | -9.363494545454545 | -0.15736363636363634 | 0.711738573316 | 88.13229089439999 | 1.3354113599999998e-2 | 9.426419446498016 | 9.42831270330459 | 0.23340645454545472 | 2.4385454545454266e-2 | 4.180363636363635e-2 | 0.15739903305785125 | 7.854016528925617e-2 | 6.492289256198348e-2 | 5.447857302347942e-2 | 5.946503933884161e-4 | 1.7475440132231391e-3 | 3.572726408331256e-2 | 8.139759930278025e-3 | 5.109235888016529e-3 | 0.18901657092253196 | 9.022061809962302e-2 | 7.147891918612459e-2 |
| user_004 | Standing | 1.446046411163e12 | 0.996927 | -9.27292 | -3.8919e-2 | 1.059634 | -9.36001090909091 | -0.13994527272727272 | 0.993863443329 | 85.98704532639998 | 1.514688561e-3 | 9.326436803961627 | 9.422568921205992 | 6.270699999999996e-2 | 8.709090909091088e-2 | 0.10102627272727271 | 0.14884828925619836 | 8.075702479338855e-2 | 6.428947107438017e-2 | 3.932167848999995e-3 | 7.584826446281304e-3 | 1.0206307781165287e-2 | 3.38506136849955e-2 | 8.471837422689771e-3 | 4.9768380135101434e-3 | 0.18398536269224108 | 9.204258483272713e-2 | 7.054670802744904e-2 |
| user_004 | Standing | 1.446046411228e12 | 1.11189 | -9.34956 | -0.307161 | 1.0526667272727273 | -9.353043636363637 | -0.14691254545454543 | 1.2362993721000002 | 87.41427219360001 | 9.434787992100001e-2 | 9.420452189020494 | 9.415007269715698 | 5.922327272727279e-2 | 3.4836363636365775e-3 | 0.16024845454545458 | 0.14061423140495868 | 7.727338842975212e-2 | 6.967328099173555e-2 | 3.507396032528933e-3 | 1.2135722314051077e-5 | 2.5679567184206623e-2 | 3.21295211817701e-2 | 8.314073032607122e-3 | 6.383492949667169e-3 | 0.17924709532310448 | 9.118153888045058e-2 | 7.989676432539161e-2 |
| user_004 | Standing | 1.446046411746e12 | 1.30349 | -9.38788 | -5.99e-4 | 1.0178297272727272 | -9.366978181818181 | -7.375572727272729e-2 | 1.6990861801000001 | 88.13229089439999 | 3.5880100000000004e-7 | 9.477941624282193 | 9.426680900683847 | 0.2856602727272728 | 2.090181818181769e-2 | 7.315672727272729e-2 | 0.19255234710743804 | 8.139041322314065e-2 | 0.12699536363636363 | 8.160179141461987e-2 | 4.368860033057645e-4 | 5.3519067452562005e-3 | 4.840703688697522e-2 | 1.0951937764688268e-2 | 2.426411914712021e-2 | 0.2200159923436822 | 0.104651506270518 | 0.15576944227646258 |
| user_004 | Standing | 1.446046413688e12 | 0.460442 | -9.50284 | -0.575403 | 1.2930383636363636 | -9.293821818181819 | -0.5196647272727272 | 0.21200683536400003 | 90.30396806560002 | 0.331088612409 | 9.531372593355744 | 9.407684402739001 | 0.8325963636363636 | 0.2090181818181822 | 5.573827272727283e-2 | 0.268874479338843 | 0.152963305785124 | 0.31067958677685953 | 0.6932167047404958 | 4.368860033057868e-2 | 3.106755046619846e-3 | 0.1280806416731991 | 3.583678799338847e-2 | 0.12849849671874833 | 0.35788355881934436 | 0.18930606961581678 | 0.3584668697644851 |
| user_004 | Standing | 1.446046414271e12 | 1.30349 | -9.50284 | -0.345482 | 1.2512353636363636 | -9.38787909090909 | -0.3977365454545454 | 1.6990861801000001 | 90.30396806560002 | 0.119357812324 | 9.598042095032925 | 9.491118177753593 | 5.2254636363636475e-2 | 0.11496090909091095 | 5.225454545454539e-2 | 0.2802762314049587 | 0.12826206611570223 | 0.18431735537190086 | 2.7305470214958796e-3 | 1.321601061900869e-2 | 2.73053752066115e-3 | 0.14207760969693764 | 2.8615357226371166e-2 | 7.562875789122314e-2 | 0.3769318369373137 | 0.16916074375093995 | 0.2750068324446197 |
| user_004 | Standing | 1.446046414789e12 | 1.34181 | -9.19628 | -3.8919e-2 | 1.293039090909091 | -9.335625454545454 | -0.4116710909090909 | 1.8004540760999999 | 84.57156583839999 | 1.514688561e-3 | 9.293736310174772 | 9.436144452768678 | 4.8770909090908976e-2 | 0.13934545454545422 | 0.3727520909090909 | 0.10735933057851237 | 0.10387586776859514 | 0.10799344628099172 | 2.3786015735537077e-3 | 1.9417155702479247e-2 | 0.13894412127709915 | 1.9197577323342603e-2 | 1.8777275617580767e-2 | 2.0440168707416224e-2 | 0.13855532224834455 | 0.13703019965533425 | 0.14296911801999837 |
| user_004 | Standing | 1.446046414919e12 | 0.728685 | -9.31124 | -0.728685 | 1.1885289090909092 | -9.363494545454545 | -0.4290895454545454 | 0.530981829225 | 86.6991903376 | 0.530981829225 | 9.36809233494472 | 9.455222723878217 | 0.4598439090909092 | 5.225454545454511e-2 | 0.2995954545454546 | 0.19065084297520657 | 0.13301173553719037 | 0.1374462561983471 | 0.21145642072800838 | 2.730537520661121e-3 | 8.975743638429756e-2 | 8.091771112459506e-2 | 3.0215736309241223e-2 | 3.0178694488630354e-2 | 0.28446038586171374 | 0.17382674221546357 | 0.17372016143392902 |
| user_004 | Standing | 1.446046415436e12 | 1.03525 | -9.34956 | -0.537083 | 1.324391636363636 | -9.321690909090908 | -0.49876290909090915 | 1.0717425625 | 87.41427219360001 | 0.28845814888899995 | 9.422020638110968 | 9.442251657405361 | 0.2891416363636361 | 2.786909090909262e-2 | 3.832009090909083e-2 | 0.33664782644628094 | 9.56416528925622e-2 | 0.20426942148760333 | 8.360288587904117e-2 | 7.766862280992689e-4 | 1.4684293672809858e-3 | 0.17132482064501955 | 1.848160183681444e-2 | 5.515776418483396e-2 | 0.41391402566839836 | 0.13594705527084594 | 0.23485690150564867 |
| user_004 | Standing | 1.446046416278e12 | 5.99e-4 | -8.88971 | 0.114362 | 1.0770511818181818 | -9.339108181818181 | -0.29322672727272725 | 3.5880100000000004e-7 | 79.02694388409998 | 1.3078667044000002e-2 | 8.890445596815999 | 9.41858010801273 | 1.0764521818181818 | 0.44939818181818225 | 0.40758872727272727 | 0.35945021487603307 | 0.13902958677685975 | 0.13681291735537188 | 1.158749299741124 | 0.201958725821488 | 0.16612857059980166 | 0.2264460329577461 | 4.479810685048842e-2 | 3.490508766509166e-2 | 0.47586346041458794 | 0.2116556326925613 | 0.18682903324989844 |
| user_004 | Standing | 1.446046416408e12 | 1.64837 | -9.11964 | 0.114362 | 1.1850444545454544 | -9.363493636363636 | -0.2618737272727273 | 2.7171236568999997 | 83.1678337296 | 1.3078667044000002e-2 | 9.26811933746777 | 9.456573244994104 | 0.4633255454545455 | 0.24385363636363522 | 0.3762357272727273 | 0.3648338347107438 | 0.19540115702479371 | 0.1659490743801653 | 0.21467056107075214 | 5.9464595967768034e-2 | 0.14155332247643804 | 0.21016706771208565 | 6.667774145319322e-2 | 4.803837257303833e-2 | 0.458439819073437 | 0.2582203350884535 | 0.21917657852297615 |
| user_004 | Standing | 1.446046416667e12 | 1.61005 | -9.27292 | -0.652044 | 1.1850441818181816 | -9.346075454545455 | -0.30367763636363637 | 2.5922610025 | 85.98704532639998 | 0.42516137793599995 | 9.434217917073783 | 9.441841544024516 | 0.4250058181818184 | 7.315545454545536e-2 | 0.3483663636363636 | 0.38985298347107433 | 0.18495024793388445 | 0.1963519338842975 | 0.18062994548839686 | 5.351720529752185e-3 | 0.1213591233132231 | 0.20996067308201585 | 6.1537693003906886e-2 | 5.803834992721262e-2 | 0.458214658301124 | 0.24806792014266352 | 0.2409114981216393 |
| user_004 | Standing | 1.446046416797e12 | 0.383802 | -9.2346 | -0.11556 | 1.153691636363636 | -9.363493636363636 | -0.23748799999999995 | 0.147303975204 | 85.27783716 | 1.3354113599999998e-2 | 9.243294610083788 | 9.460083489740654 | 0.769889636363636 | 0.12889363636363527 | 0.12192799999999995 | 0.4987971652892563 | 0.1906507438016531 | 0.23625571900826445 | 0.5927300521801318 | 1.661356949504104e-2 | 1.4866437183999989e-2 | 0.32266433426134267 | 6.264976186055603e-2 | 6.975061971639669e-2 | 0.5680355044020952 | 0.2502993445068445 | 0.26410342617315036 |
| user_004 | Standing | 1.446046417768e12 | 1.30349 | -9.31124 | -0.460442 | 1.0143450909090908 | -9.40529818181818 | -0.19568381818181815 | 1.6990861801000001 | 86.6991903376 | 0.21200683536400003 | 9.413303530273737 | 9.479346163800669 | 0.2891449090909093 | 9.405818181818049e-2 | 0.2647581818181819 | 0.4269073305785124 | 0.12572760330578509 | 0.3046622231404959 | 8.36047784531902e-2 | 8.846941566941898e-3 | 7.009689483966947e-2 | 0.23773179596939376 | 2.566705269421491e-2 | 0.11697479484084222 | 0.48757747688894915 | 0.16020940263984168 | 0.3420157815669362 |
| user_004 | Standing | 1.446046418286e12 | 1.45677 | -9.2346 | -0.805325 | 1.362711090909091 | -9.304272727272727 | -0.4499914545454546 | 2.1221788328999995 | 85.27783716 | 0.6485483556249999 | 9.383419651093359 | 9.425167218813767 | 9.405890909090897e-2 | 6.967272727272622e-2 | 0.3553335454545454 | 0.32904747107438015 | 0.12889454545454546 | 0.2796432561983471 | 8.847078379371879e-3 | 4.854288925619688e-3 | 0.12626192852529747 | 0.14820659277210974 | 2.504923410368146e-2 | 0.10975174829168598 | 0.38497609376701525 | 0.1582694983364813 | 0.33128801410809594 |
| user_004 | Standing | 1.446046418545e12 | 1.03525 | -9.65612 | -0.920286 | 1.3278747272727272 | -9.314723636363636 | -0.6032727272727273 | 1.0717425625 | 93.24065345439999 | 0.8469263217960001 | 9.754963984489947 | 9.439623906047471 | 0.29262472727272715 | 0.3413963636363633 | 0.31701327272727275 | 0.29642714876033055 | 0.14504595041322332 | 0.2695089669421488 | 8.562923101143795e-2 | 0.11655147710413198 | 0.10049741508525621 | 0.12480726170758606 | 3.192136267588283e-2 | 0.10582854600052521 | 0.35328071233452024 | 0.17866550499713937 | 0.325312996974491 |
| user_004 | Standing | 1.446046419257e12 | 0.728685 | -9.50284 | -0.690364 | 1.3243917272727275 | -9.339109090909092 | -0.5858543636363636 | 0.530981829225 | 90.30396806560002 | 0.476602452496 | 9.555707841249701 | 9.469914267682038 | 0.5957067272727274 | 0.16373090909090848 | 0.10450963636363642 | 0.27710938842975197 | 0.13902876033057848 | 0.32113041322314045 | 0.35486650491798366 | 2.680781059173534e-2 | 1.0922264092859514e-2 | 0.15220772971530575 | 2.7787494356724177e-2 | 0.1724028184403606 | 0.390138090572179 | 0.16669581385483012 | 0.41521418381404146 |
| user_004 | Standing | 1.446046419322e12 | 1.83997 | -9.00468 | -1.41845 | 1.359228090909091 | -9.293821818181819 | -0.7147499090909091 | 3.3854896009 | 81.0842619024 | 2.0120004025 | 9.29955654350249 | 9.441341967736482 | 0.4807419090909091 | 0.28914181818181817 | 0.7037000909090909 | 0.3087787520661157 | 0.14979636363636362 | 0.33759865289256197 | 0.2311127831563719 | 8.360299102148759e-2 | 0.4951938179454628 | 0.17162487585933053 | 3.273886906085642e-2 | 0.1925969898494252 | 0.41427632790123375 | 0.18093885448088926 | 0.4388587356421485 |
| user_004 | Standing | 1.446046419451e12 | 1.83997 | -8.50651 | -0.805325 | 1.6135350000000002 | -9.220664545454547 | -0.8192599090909091 | 3.3854896009 | 72.36071238010001 | 0.6485483556249999 | 8.7404090485872 | 9.421110056105645 | 0.22643499999999994 | 0.7141545454545462 | 1.3934909090909109e-2 | 0.3901694462809917 | 0.20996909090909066 | 0.28439353719008265 | 5.127280922499997e-2 | 0.5100167147933894 | 1.9418169137190133e-4 | 0.301298780906686 | 7.874105652922614e-2 | 0.1744526572158077 | 0.5489068963919892 | 0.28060836860155497 | 0.4176753011800048 |
| user_004 | Standing | 1.44604642107e12 | 1.38013 | -9.38788 | -0.345482 | 1.1293065454545452 | -9.342591818181818 | -0.13994536363636365 | 1.9047588169000003 | 88.13229089439999 | 0.119357812324 | 9.495072802439378 | 9.418072771570202 | 0.25082345454545485 | 4.528818181818117e-2 | 0.20553663636363637 | 0.21693703305785128 | 0.12921231404958677 | 0.21440377685950415 | 6.291240535011586e-2 | 2.051019412396636e-3 | 4.2245308887677684e-2 | 5.991265460036592e-2 | 3.2445094302855e-2 | 7.60337220970661e-2 | 0.24477061629281796 | 0.18012521839779957 | 0.27574212971010814 |
| user_004 | Standing | 1.446046421459e12 | 1.45677 | -9.38788 | -0.345482 | 1.2721372727272728 | -9.332141818181817 | -0.32457972727272727 | 2.1221788328999995 | 88.13229089439999 | 0.119357812324 | 9.506515004964962 | 9.426230789766656 | 0.18463272727272706 | 5.573818181818169e-2 | 2.090227272727274e-2 | 0.17544919008264473 | 5.795454545454539e-2 | 0.14979758677685953 | 3.408924398016521e-2 | 3.1067449123966797e-3 | 4.3690500516528987e-4 | 4.6409602075376435e-2 | 5.155366086100664e-3 | 3.4963583735538706e-2 | 0.21542887939033717 | 7.180087803154404e-2 | 0.18698551744864816 |
| user_004 | Walking | 1.446046367239e12 | -2.49022 | -3.37159 | -1.41845 | -0.20493654545454543 | -9.098735454545453 | 0.19797045454545453 | 6.2011956484 | 11.3676191281 | 2.0120004025 | 4.425021489100364 | 9.447952530726882 | 2.2852834545454543 | 5.727145454545454 | 1.6164204545454546 | 1.8382258753541914 | 2.605906067099567 | 0.8803017542174996 | 5.222520467619206 | 32.800195057520654 | 2.612815085872934 | 4.52667831201796 | 11.455561595336873 | 1.0851159381124824 | 2.127599189701378 | 3.384606564334601 | 1.0416889833882677 |
| user_004 | Walking | 1.446046367435e12 | 1.91661 | -5.78577 | -7.31978 | 0.3559323636363636 | -9.499356363636362 | -1.7737836363636363 | 3.6733938920999996 | 33.475134492900004 | 53.579179248399996 | 9.525109323960539 | 10.518010767307546 | 1.5606776363636363 | 3.713586363636362 | 5.545996363636363 | 2.028855586373475 | 3.4606490533254615 | 2.3382468330053783 | 2.435714684645587 | 13.79072368018594 | 30.758075665467764 | 5.0896186853443295 | 16.256597771688835 | 10.290884887381942 | 2.2560183255781254 | 4.031947143960203 | 3.2079409108308 |
| user_004 | Walking | 1.446046367693e12 | 1.80165 | -6.24561 | -1.03525 | -0.5289194545454546 | -8.28704 | -1.4428365454545453 | 3.2459427224999997 | 39.0076442721 | 1.0717425625 | 6.5821979275239055 | 9.789411532907668 | 2.3305694545454547 | 2.041429999999999 | 0.4075865454545453 | 2.358440900826446 | 4.4812586776859495 | 2.8775063223140496 | 5.431553982460298 | 4.167436444899996 | 0.16612679203557013 | 7.056808578148045 | 25.7913773011737 | 13.164478352186688 | 2.6564654295036565 | 5.078521172661753 | 3.6282886258106157 |
| user_004 | Walking | 1.446046368987e12 | -0.152682 | -9.2346 | 0.152682 | -0.47666409090909095 | -8.530897272727273 | -1.5194752727272727 | 2.3311793124000002e-2 | 85.27783716 | 2.3311793124000002e-2 | 9.23712405168665 | 9.76657303180138 | 0.3239820909090909 | 0.7037027272727272 | 1.6721572727272727 | 2.0157751157024792 | 3.4272936363636366 | 2.873388876033058 | 0.10496439522982645 | 0.4951975283710743 | 2.7961099447347104 | 5.828280083975668 | 17.897908770771984 | 13.376540338645782 | 2.414183109040337 | 4.230592011855077 | 3.6573952942833214 |
| user_004 | Walking | 1.446046369959e12 | -2.33694 | -5.93905 | 2.56686 | -0.4209249090909091 | -8.266136363636361 | -1.481156181818182 | 5.461288563599999 | 35.2723149025 | 6.5887702596 | 6.879125941985653 | 9.35353616825607 | 1.9160150909090907 | 2.3270863636363615 | 4.048016181818182 | 2.167155512396694 | 3.179637768595042 | 2.7061742975206613 | 3.671113828591371 | 5.415330943822304 | 16.386435008261856 | 5.794231878774539 | 13.132691215146282 | 12.55247195021168 | 2.4071210768830342 | 3.6239055196219288 | 3.54294678907427 |
| user_004 | Walking | 1.446046371837e12 | 0.1922 | -14.10128 | -7.97122 | -0.5010486363636365 | -9.628250000000001 | -1.139758 | 3.694084e-2 | 198.84609763839998 | 63.5403482884 | 16.199487237773916 | 10.316775974009568 | 0.6932486363636365 | 4.473029999999998 | 6.831462 | 1.8621772231404956 | 2.9760007438016527 | 1.9210819669421488 | 0.4805936718200416 | 20.00799738089998 | 46.668873057444 | 4.533172438923539 | 12.205092426485121 | 7.436123220206778 | 2.1291248058588623 | 3.493578741990099 | 2.726925598582913 |
| user_004 | Walking | 1.446046373132e12 | -3.86975 | -12.83671 | -0.652044 | -0.9399907272727274 | -9.830303636363636 | -2.0594450909090907 | 14.974965062499999 | 164.7811236241 | 0.42516137793599995 | 13.423160956516018 | 10.83597894331644 | 2.9297592727272725 | 3.006406363636364 | 1.4074010909090906 | 1.865976297520661 | 3.1080623966942142 | 2.387260115702479 | 8.583489396131437 | 9.038479223313225 | 1.9807778306920982 | 4.837057946633808 | 14.082332477305709 | 9.694053998251828 | 2.199331249865242 | 3.75264339863325 | 3.113527581097015 |
| user_004 | Walking | 1.446046374362e12 | -3.67815 | -7.7401 | 3.56319 | -1.4416375454545456 | -9.105700909090908 | -1.5891492727272727 | 13.5287874225 | 59.90914801 | 12.696322976100001 | 9.2808544007866 | 10.259898144501834 | 2.2365124545454544 | 1.365600909090908 | 5.152339272727273 | 2.17982432231405 | 3.5286358677685943 | 2.2593143553719006 | 5.001987959336933 | 1.8648658429099145 | 26.546599981287805 | 7.185240938756582 | 15.047537842465964 | 10.801522905784468 | 2.680529973485949 | 3.8791156005545857 | 3.2865670395999027 |
| user_004 | Walking | 1.446046374751e12 | 2.0699 | -11.80206 | -1.03525 | -0.16313245454545458 | -9.88255727272727 | -1.3383258181818178 | 4.28448601 | 139.2886202436 | 1.0717425625 | 12.02683868753963 | 10.911064012777535 | 2.2330324545454547 | 1.9195027272727305 | 0.30307581818181784 | 2.393278776859504 | 2.4176638842975198 | 2.7235916363636363 | 4.9864339430532985 | 3.6844907200074504 | 9.18549515665783e-2 | 7.541399251908342 | 7.92809590244951 | 12.209342634414334 | 2.7461608204743477 | 2.8156874653358654 | 3.494186977597841 |
| user_004 | Walking | 1.44604637488e12 | -6.62882 | -15.59577 | -1.07357 | -1.0967554545454545 | -10.439943636363637 | 1.681954545454547e-2 | 43.9412545924 | 243.2280418929 | 1.1525525448999998 | 16.980042668680195 | 11.156214455696425 | 5.532064545454546 | 5.155826363636363 | 1.0903895454545454 | 2.5699958016528925 | 2.8414043801652884 | 1.8181555867768593 | 30.603738135075208 | 26.582545491967764 | 1.18894936083657 | 9.199901619889102 | 10.711808598846353 | 5.263144219387184 | 3.0331339600962406 | 3.272889946033376 | 2.2941543582303225 |
| user_004 | Walking | 1.44604637501e12 | 2.75966 | -8.04667 | -1.38013 | -0.6612972727272727 | -10.23789090909091 | 0.3756364545454545 | 7.615723315599999 | 64.7488980889 | 1.9047588169000003 | 8.617968450940163 | 10.94460809488589 | 3.4209572727272723 | 2.1912209090909087 | 1.7557664545454545 | 2.729293834710744 | 2.3508403305785124 | 1.8507755371900827 | 11.702948661825618 | 4.8014490724371885 | 3.0827158429071155 | 10.14018488805799 | 7.814975427667318 | 5.375853181447623 | 3.1843656963448765 | 2.795527754766051 | 2.3185886184158724 |
| user_004 | Walking | 1.446046375398e12 | -5.51753 | -15.02096 | 0.459245 | -1.013147181818182 | -10.004486363636364 | -2.010674090909091 | 30.4431373009 | 225.6292393216 | 0.210905970025 | 16.008850133364515 | 11.250955163400983 | 4.504382818181818 | 5.016473636363637 | 2.4699190909090913 | 2.485437545454545 | 3.9216566942148763 | 2.4718181157024794 | 20.289464572731575 | 25.165007744331408 | 6.100500315637192 | 8.475776219770623 | 18.712293954117953 | 10.82504804143981 | 2.911318639340363 | 4.325770908649457 | 3.290144076091473 |
| user_004 | Walking | 1.446046375463e12 | -4.138 | -1.91542 | 1.57053 | -1.5775017272727274 | -9.10570090909091 | -1.773785 | 17.123044 | 3.6688337763999996 | 2.4665644809 | 4.822700722344276 | 10.596033530201407 | 2.5604982727272727 | 7.19028090909091 | 3.344315 | 2.5152071652892563 | 4.400818347107438 | 2.7482944049586777 | 6.556151404639347 | 51.7001395516372 | 11.184442819225 | 8.618477807187535 | 23.07735293881157 | 11.833465120317847 | 2.9357244092706547 | 4.803889355388149 | 3.439980395339172 |
| user_004 | Walking | 1.446046375657e12 | -3.02671 | -7.28026 | 1.95374 | -0.8459320909090909 | -8.321876363636365 | -1.5020585454545454 | 9.1609734241 | 53.0021856676 | 3.8170999876000002 | 8.122823344090403 | 9.687379919583718 | 2.180777909090909 | 1.041616363636365 | 3.4557985454545452 | 2.102550933884298 | 4.184197603305785 | 3.2401248016528927 | 4.755792288778918 | 1.084964648995044 | 11.942543586765751 | 5.808805079582124 | 22.244903839339518 | 14.20277269512039 | 2.410146277631738 | 4.716450343143614 | 3.768656616769481 |
| user_004 | Walking | 1.446046377082e12 | 1.41845 | -13.25823 | 0.842448 | -0.6787151818181819 | -9.809402727272726 | -0.376834 | 2.0120004025 | 175.78066273289997 | 0.7097186327039999 | 13.360478351021118 | 10.527579477583433 | 2.097165181818182 | 3.4488272727272733 | 1.219282 | 1.7934540991735537 | 2.305236611570247 | 2.2209938099173554 | 4.398101799830488 | 11.894409557107442 | 1.4866485955239999 | 5.806652769430188 | 8.155345279416602 | 7.946394923938269 | 2.4096997259887356 | 2.855756516129588 | 2.8189350691242017 |
| user_004 | Walking | 1.446046377211e12 | -2.6435 | -8.77475 | 0.880768 | -0.9121201818181818 | -9.973134545454544 | 0.48711490909090904 | 6.98809225 | 76.99623756249999 | 0.775752269824 | 9.20652388702294 | 10.432936563834183 | 1.7313798181818183 | 1.1983845454545445 | 0.39365309090909095 | 1.9562359256198347 | 2.136437603305785 | 1.7009778595041318 | 2.9976760748073064 | 1.4361255187842954 | 0.15496275598228101 | 6.198894399521225 | 7.204444397644102 | 4.908080225721393 | 2.4897578997808654 | 2.684109609841614 | 2.2154187472623303 |
| user_004 | Walking | 1.446046377729e12 | -3.52487 | -6.93538 | 2.75846 | -1.5496309090909093 | -9.178857272727273 | -1.6100526363636367 | 12.424708516899999 | 48.0994957444 | 7.609101571599999 | 8.254290147123495 | 10.336100336423819 | 1.9752390909090907 | 2.2434772727272723 | 4.368512636363636 | 2.1152172148760333 | 3.44851173553719 | 2.6352338512396694 | 3.901569466255371 | 5.033190273243799 | 19.08390265406877 | 5.595292362165221 | 14.890526513806611 | 11.644638679226706 | 2.365437034073243 | 3.858824498964239 | 3.4124241646118243 |
| user_004 | Walking | 1.446046377988e12 | -0.229323 | -9.19628 | 0.152682 | -1.093271 | -8.489091818181818 | -1.5787000909090911 | 5.2589038329e-2 | 84.57156583839999 | 2.3311793124000002e-2 | 9.20040578832548 | 9.547832791509201 | 0.8639480000000002 | 0.7071881818181822 | 1.7313820909090911 | 1.630355173553719 | 3.065624545454545 | 3.021603834710744 | 0.7464061467040003 | 0.5001151245033063 | 2.997683944720736 | 3.620988301954705 | 13.128522741809842 | 12.93487508524121 | 1.9028894613073837 | 3.6233303384883144 | 3.5965087355991794 |
| user_004 | Walking | 1.446046378441e12 | -1.57053 | -5.86241 | 0.612526 | -0.5707226363636363 | -10.133382727272728 | -4.083000000000018e-3 | 2.4665644809 | 34.3678510081 | 0.375188100676 | 6.099967507263952 | 10.510226404961669 | 0.9998073636363637 | 4.270972727272729 | 0.6166090000000001 | 2.1231347355371906 | 2.47847 | 1.240184652892562 | 0.999614764381496 | 18.24120803710745 | 0.3802066588810001 | 7.28347342161239 | 8.464145353255374 | 2.2367967334880023 | 2.6987911037374475 | 2.909320428082024 | 1.495592435621417 |
| user_004 | Walking | 1.446046379995e12 | -4.98104 | -6.59049 | 3.21831 | -1.6471744545454547 | -8.83049 | -1.676241909090909 | 24.8107594816 | 43.4345584401 | 10.357519256099998 | 8.865824111598426 | 10.258849067062076 | 3.3338655454545454 | 2.2399999999999993 | 4.894551909090909 | 2.429696867768595 | 3.642013553719009 | 2.383144049586777 | 11.114659475168933 | 5.017599999999997 | 23.956638390785464 | 9.371850905559276 | 15.878911996568291 | 10.234845140065339 | 3.061347890318785 | 3.9848352533785247 | 3.199194451743335 |
| user_004 | Walking | 1.446046380074e12 | -0.804128 | -8.96635 | 0.229323 | -0.6578142727272728 | -8.398515454545453 | -1.885262 | 0.646621840384 | 80.3954323225 | 5.2589038329e-2 | 9.00525642062529 | 9.673506752782716 | 0.14631372727272718 | 0.5678345454545468 | 2.114585 | 2.1418190165289257 | 3.4998168595041323 | 2.54719279338843 | 2.140770678843799e-2 | 0.3224360710115718 | 4.471469722225 | 7.368126209589817 | 16.012725198798947 | 10.633785741520972 | 2.714429260376814 | 4.001590333704707 | 3.2609485953508948 |
| user_004 | Walking | 1.446046380082e12 | -1.99206 | -4.44456 | 1.95374 | -0.8250304545454547 | -8.119822727272727 | -1.6239869090909094 | 3.9683030435999997 | 19.7541135936 | 3.8170999876000002 | 5.247810650623744 | 9.462589951607004 | 1.1670295454545454 | 3.6752627272727265 | 3.5777269090909094 | 2.1741222314049584 | 3.6116102479338834 | 2.755262933884298 | 1.361957959963843 | 13.50755611448016 | 12.800129836033193 | 7.432045347332212 | 16.696989967439066 | 11.646397005756084 | 2.7261777908515454 | 4.08619504764996 | 3.4126817908729907 |
| user_004 | Walking | 1.446046380171e12 | 0.690364 | -9.8094 | -0.767005 | -1.7934879090909093 | -7.3255481818181805 | 1.2535205454545453 | 0.476602452496 | 96.22432836 | 0.5882966700250001 | 9.863530173448094 | 8.124101615562969 | 2.4838519090909092 | 2.4838518181818197 | 2.020525545454545 | 1.3906151570247933 | 2.361923801652893 | 2.43508226446281 | 6.1695203062945545 | 6.169519854685131 | 4.0825234798343875 | 2.519668729341154 | 6.412058420537043 | 7.494884325004188 | 1.5873464427594732 | 2.5322042612192726 | 2.73767863800779 |
| user_004 | Walking | 1.44604638022e12 | -0.842448 | -8.81307 | 0.689167 | 4.082909090909087e-3 | -9.530707272727271 | -0.42908963636363634 | 0.7097186327039999 | 77.6702028249 | 0.47495115388899994 | 8.880026610967615 | 9.590578855630675 | 0.8465309090909091 | 0.7176372727272717 | 1.1182566363636364 | 1.4267191570247935 | 1.7006610743801664 | 1.466306438016529 | 0.716614580046281 | 0.5150032552074366 | 1.250497904771314 | 2.718214575984966 | 3.7857442812020308 | 2.529815208583772 | 1.6487008752302421 | 1.9456989184357458 | 1.590539282313949 |
| user_004 | Walking | 1.446046380365e12 | -1.07237 | -11.15061 | -2.18486 | 2.5227720909090903 | -10.830114545454544 | -0.9481552727272727 | 1.1499774169 | 124.33610337210001 | 4.7736132196000005 | 11.41313690483909 | 11.350884475526653 | 3.59514209090909 | 0.3204954545454566 | 1.2367047272727274 | 2.564294082644628 | 0.8972012396694219 | 0.9469231818181818 | 12.925046653826184 | 0.10271733638429882 | 1.5294385824587111 | 7.856641240894614 | 0.9666613927906087 | 1.3448840650196614 | 2.802970074919569 | 0.9831893982293588 | 1.1596913662779686 |
| user_004 | Walking | 1.446046380398e12 | -1.14901 | -14.02463 | 0.229323 | 0.5510166363636363 | -11.5582 | -0.8959008181818181 | 1.3202239801000002 | 196.6902466369 | 5.2589038329e-2 | 14.073487828371793 | 11.88263241539135 | 1.7000266363636363 | 2.466430000000001 | 1.1252238181818182 | 2.447750256198347 | 0.912086033057852 | 0.6447948925619834 | 2.890090564345859 | 6.083276944900004 | 1.2661286410036694 | 7.293689833785208 | 1.2568161384298284 | 0.589618921873151 | 2.7006832161112877 | 1.1210781143300534 | 0.7678664739869497 |
| user_004 | Walking | 1.446046380437e12 | 1.87829 | -12.53014 | 1.80046 | -0.45924609090909113 | -12.65903909090909 | -0.17478218181818161 | 3.5279733241 | 157.0044084196 | 3.2416562115999996 | 12.797423098237395 | 12.823886745883119 | 2.337536090909091 | 0.12889909090909057 | 1.9752421818181816 | 2.2504479256198344 | 1.0511178512396695 | 1.3377275619834708 | 5.4640749763025545 | 1.6614975637189996e-2 | 3.90158167683385 | 6.601606710358293 | 1.8721697833812165 | 2.122333148807431 | 2.569359202283381 | 1.3682725544938832 | 1.4568229641268808 |
| user_004 | Walking | 1.446046380551e12 | -6.05401 | -8.50651 | 3.75479 | -6.193358272727273 | -13.34532 | 9.69440909090911e-2 | 36.6510370801 | 72.36071238010001 | 14.098447944099998 | 11.095503476827899 | 15.273407674630205 | 0.1393482727272728 | 4.838809999999999 | 3.657845909090909 | 4.122443181818182 | 1.642073057851239 | 2.524388297520661 | 1.94179411120744e-2 | 23.414082216099988 | 13.379836694653097 | 22.11843500412494 | 4.832016730455144 | 7.712770051733102 | 4.703024027593836 | 2.1981848717646892 | 2.77718743547012 |
| user_004 | Walking | 1.446046380607e12 | 2.8363 | -8.12331 | -1.45677 | -3.0023209999999994 | -8.910614545454546 | 0.8947030000000002 | 8.04459769 | 65.9881653561 | 2.1221788328999995 | 8.726679888651812 | 10.40415012803502 | 5.838621 | 0.7873045454545462 | 2.3514730000000004 | 4.679512611570247 | 3.070059256198347 | 2.48100147107438 | 34.089495181641 | 0.6198484472933896 | 5.529425269729002 | 26.36158899282286 | 12.161785970066035 | 7.33708094032403 | 5.13435380479597 | 3.487375226451268 | 2.708704660963249 |
| user_004 | Walking | 1.446046380632e12 | 1.91661 | -9.04299 | -1.95493 | 6.33044545454546e-2 | -8.078019090909093 | -0.29670972727272726 | 3.6733938920999996 | 81.7756681401 | 3.8217513049000003 | 9.448323308243637 | 8.76231745154077 | 1.8533055454545453 | 0.9649709090909067 | 1.6582202727272728 | 4.297892743801652 | 2.7109246280991726 | 1.9780874628099172 | 3.4347414448125697 | 0.9311688553917309 | 2.749694472883711 | 23.239252805113832 | 10.62169050586551 | 4.62037458539075 | 4.820710819486462 | 3.2590935098375913 | 2.149505660702188 |
| user_004 | Walking | 1.446046380712e12 | -2.79678 | -4.59784 | 0.497565 | -0.2920294545454546 | -7.078208181818183 | -0.1991674545454547 | 7.8219783684 | 21.140132665599996 | 0.24757092922499999 | 5.404598224033402 | 7.330404214633282 | 2.5047505454545456 | 2.480368181818183 | 0.6967324545454547 | 1.6601237107438016 | 1.2601368595041322 | 1.0260964876033059 | 6.273775294954843 | 6.152226317376039 | 0.48543611321693414 | 3.3548485403621124 | 2.1182480306740796 | 1.2529168440708105 | 1.8316245631575572 | 1.4554202247715535 | 1.1193376809840767 |
| user_004 | Walking | 1.446046380907e12 | 0.805325 | -13.90968 | -7.08986 | 2.522717363636364 | -13.554396363636364 | -7.925881818181819 | 0.6485483556249999 | 193.4791977024 | 50.2661148196 | 15.633101447813386 | 16.06838302763538 | 1.717392363636364 | 0.3552836363636356 | 0.8360218181818189 | 1.7950017685950412 | 5.5650118181818184 | 1.963494917355372 | 2.949436530676497 | 0.12622646226776804 | 0.6989324804760342 | 3.9698473877730645 | 38.85068078559317 | 5.016328901239086 | 1.992447587208523 | 6.233031428253284 | 2.2397162546267073 |
| user_004 | Walking | 1.446046381198e12 | -0.727487 | -5.59417 | 1.07237 | 1.7598499090909088 | -12.40124818181818 | -0.16084736363636362 | 0.529237335169 | 31.2947379889 | 1.1499774169 | 5.7422950760971 | 13.05166984113652 | 2.4873369090909088 | 6.807078181818181 | 1.2332173636363637 | 2.9763167438016533 | 6.872004049586777 | 2.498737305785124 | 6.186844899325916 | 46.33631337338511 | 1.5208250659742233 | 12.050094489829663 | 55.48691985462485 | 8.704269508248906 | 3.471324601622508 | 7.448954279267987 | 2.9502999014081444 |
| user_004 | Walking | 1.446046381207e12 | -2.14534 | -5.24928 | 1.91542 | 1.7737844545454542 | -11.662710909090912 | -0.3141282727272728 | 4.6024837156 | 27.5549405184 | 3.6688337763999996 | 5.985503989673718 | 12.319523421340348 | 3.919124454545454 | 6.413430909090912 | 2.2295482727272726 | 3.257860371900826 | 6.713022644628097 | 2.518372826446281 | 15.359536490216204 | 41.13209602568268 | 4.970885500421165 | 13.384968565883804 | 53.1696613832105 | 8.78758510020929 | 3.6585473300046023 | 7.291752970528452 | 2.9643861253570343 |
| user_004 | Walking | 1.446046381312e12 | 0.268841 | -10.15428 | -1.41845 | -1.8283258181818183 | -8.03970090909091 | 0.5846565454545453 | 7.2275483281e-2 | 103.1094023184 | 2.0120004025 | 10.256396940650308 | 8.585679636059387 | 2.0971668181818184 | 2.114579090909089 | 2.0031065454545454 | 2.033827181818182 | 1.8767451239669422 | 1.6579069421487602 | 4.398108663282852 | 4.4714447317099095 | 4.012435832442843 | 5.23885645013378 | 4.130406108873178 | 3.3989535320892843 | 2.288854833783432 | 2.0323400573902926 | 1.8436251061670006 |
| user_004 | Walking | 1.446046381368e12 | -1.34061 | -8.77475 | 0.344284 | -0.598591 | -9.471485454545455 | -0.1608472727272727 | 1.7972351721000002 | 76.99623756249999 | 0.11853147265599999 | 8.883242888003005 | 9.552652784403914 | 0.7420190000000001 | 0.6967354545454558 | 0.5051312727272727 | 1.2291001239669423 | 1.2876880165289257 | 1.3139746198347106 | 0.5505921963610001 | 0.48544029362066293 | 0.2551576026870743 | 1.9203610615438131 | 2.456178409308113 | 2.234292934221153 | 1.3857709267926692 | 1.56721996200537 | 1.49475514189487 |
| user_004 | Walking | 1.446046381433e12 | 5.21216 | -10.65245 | 1.57053 | 0.6415934545454545 | -9.499355454545453 | 5.8623090909090896e-2 | 27.1666118656 | 113.4746910025 | 2.4665644809 | 11.962770053336309 | 9.817063986868506 | 4.570566545454545 | 1.1530945454545467 | 1.5119069090909092 | 1.6252866528925622 | 0.5504190082644638 | 0.9212699834710744 | 20.890078546428292 | 1.3296270307570277 | 2.2858625017568266 | 4.794645449754335 | 0.436512278266267 | 1.0913816417132172 | 2.189667885720192 | 0.6606907584235359 | 1.0446921277166863 |
| user_004 | Walking | 1.446046381627e12 | -11.07397 | -13.64143 | 1.41725 | -5.562814181818182 | -14.87813272727273 | -0.7809399090909093 | 122.63281156089998 | 186.08861244489998 | 2.0085975625 | 17.627535890427225 | 16.445116127399935 | 5.511155818181817 | 1.2367027272727302 | 2.1981899090909094 | 3.8383654462809917 | 0.9557900826446282 | 1.4979756033057854 | 30.372838452279293 | 1.529433635643809 | 4.832038876429101 | 21.521791730657057 | 1.2418094264423745 | 2.890757771440496 | 4.639158515362141 | 1.1143650328516121 | 1.7002228593453554 |
| user_004 | Walking | 1.446046381708e12 | 3.41111 | -8.62147 | -2.49142 | -3.5527397272727272 | -9.22762909090909 | 0.16313245454545455 | 11.635671432099999 | 74.3297449609 | 6.207173616400001 | 9.600655707262916 | 10.955537007966514 | 6.963849727272727 | 0.6061590909090899 | 2.654552454545455 | 5.124472049586776 | 3.176785041322314 | 1.944833743801653 | 48.495203024036435 | 0.3674288434917343 | 7.046648733933299 | 31.53765594406279 | 12.860114444126896 | 4.145003702053794 | 5.615839736322858 | 3.586100172070894 | 2.035928216331262 |
| user_004 | Walking | 1.446046381862e12 | -2.87342 | -1.8771 | -1.95493 | -2.542477 | -3.479583636363636 | 0.19797027272727277 | 8.2565424964 | 3.52350441 | 3.8217513049000003 | 3.949911164988398 | 4.69983628260057 | 0.330943 | 1.6024836363636359 | 2.152900272727273 | 2.015458446280992 | 2.2865500826446286 | 1.045415347107438 | 0.109523269249 | 2.5679538048132216 | 4.634979584309167 | 4.580855063376293 | 5.364486133914651 | 1.4248327615049055 | 2.1402932190184347 | 2.3161360352782934 | 1.1936635880786954 |
| user_004 | Walking | 1.44604638191e12 | 0.460442 | -5.01936 | -5.51872 | -1.9920577272727273 | -2.6922745454545454 | -2.6899873636363636 | 0.21200683536400003 | 25.193974809599997 | 30.4562704384 | 7.474105436998062 | 4.892939744498533 | 2.4524997272727274 | 2.3270854545454545 | 2.8287326363636365 | 1.5277448429752065 | 1.5746160330578511 | 2.156387975206611 | 6.014754912272802 | 5.415326712757024 | 8.001728328028769 | 2.8772256045321325 | 2.9820860129181073 | 5.597216075395045 | 1.696238663788835 | 1.7268717418841815 | 2.3658436286861915 |
| user_004 | Walking | 1.446046381968e12 | 3.41111 | -17.09026 | -7.81794 | 1.5717320000000001 | -8.436836363636363 | -5.766062727272727 | 11.635671432099999 | 292.0769868676 | 61.120185843600005 | 19.100598004861 | 10.599165234665834 | 1.8393779999999997 | 8.653423636363637 | 2.051877272727273 | 2.4604179834710744 | 4.235818429752066 | 2.2330285702479333 | 3.383311426883999 | 74.88174063037688 | 4.210200342334711 | 6.282738465958101 | 25.915239859435236 | 5.413362502127287 | 2.5065391411183073 | 5.0907013131232945 | 2.3266633839314372 |
| user_004 | Walking | 1.446046382032e12 | 1.76333 | -8.42987 | -4.48408 | 2.6586345454545453 | -14.299844545454544 | -7.828394545454546 | 3.1093326889000004 | 71.06270821689999 | 20.106973446399998 | 9.709738119650806 | 16.551329933455662 | 0.8953045454545452 | 5.869974545454545 | 3.344314545454546 | 1.1743123305785124 | 5.242281074380166 | 2.570311074380166 | 0.8015702291115697 | 34.45660116428429 | 11.184439778938849 | 2.279430665595875 | 35.05983585959181 | 7.693321614627501 | 1.5097783498235344 | 5.921134676697686 | 2.773683762548914 |
| user_004 | Walking | 1.44604638204e12 | 1.57173 | -6.01569 | -5.2888 | 2.4600654545454543 | -13.64839909090909 | -7.682080909090908 | 2.4703351929000004 | 36.188526176100005 | 27.97140544 | 8.16273647798335 | 15.898460266477004 | 0.8883354545454543 | 7.632709090909089 | 2.393280909090908 | 0.9633922644628098 | 5.271417024793389 | 2.6086320661157028 | 0.7891398798024788 | 58.25824806644626 | 5.727793509819004 | 1.4153351027918004 | 35.49527038710307 | 7.860593928020138 | 1.1896785712081228 | 5.957790730388495 | 2.803675075328833 |
| user_004 | Walking | 1.446046382072e12 | -1.26397 | -6.13065 | -6.63001 | 1.4950900909090912 | -8.994222727272728 | -7.093341818181818 | 1.5976201609 | 37.5848694225 | 43.95703260010001 | 9.11808763850732 | 11.953554612570894 | 2.759060090909091 | 2.863572727272728 | 0.46333181818181757 | 0.9143041239669419 | 4.723848512396693 | 1.769703140495868 | 7.612412585247283 | 8.200048764380169 | 0.21467637373966886 | 1.320082722573171 | 29.308365139374224 | 4.4012970859469585 | 1.1489485291226804 | 5.413720083212119 | 2.0979268542890046 |
| user_004 | Walking | 1.446046382316e12 | -0.229323 | -4.82776 | -0.575403 | 1.6448873636363635 | -12.763548181818182 | 0.10739427272727241 | 5.2589038329e-2 | 23.307266617599996 | 0.331088612409 | 4.867334410982874 | 13.864333837520315 | 1.8742103636363634 | 7.935788181818182 | 0.6827972727272724 | 3.4006886776859506 | 7.745771223140497 | 3.2724279338842974 | 3.5126644871619495 | 62.97673406668513 | 0.4662121156438012 | 17.78203322292181 | 69.75729677875341 | 15.412142378741247 | 4.216874817079802 | 8.35208337953791 | 3.9258301515400853 |
| user_004 | Walking | 1.446046382348e12 | -5.2876 | -4.21464 | 3.56319 | 0.8610633636363635 | -8.966354545454545 | -0.6137239090909095 | 27.958713760000002 | 17.7631903296 | 12.696322976100001 | 7.6431817370582 | 10.495678238910019 | 6.148663363636364 | 4.751714545454544 | 4.17691390909091 | 4.76945173553719 | 6.548972297520662 | 3.775976297520661 | 37.806061159324045 | 22.578791121484286 | 17.446609803957106 | 27.11017661326442 | 48.57075699291013 | 17.554459215252283 | 5.206743378856348 | 6.969272343143875 | 4.189804197722404 |
| user_004 | Walking | 1.446046382364e12 | -4.82776 | -5.51753 | 1.83878 | -0.8424475454545456 | -6.322253636363637 | -8.420663636363647e-2 | 23.307266617599996 | 30.4431373009 | 3.3811118884000004 | 7.558539264097263 | 8.21948649895524 | 3.985312454545454 | 0.8047236363636374 | 1.9229866363636365 | 4.404617619834711 | 5.154555454545456 | 3.5530224628099174 | 15.882715360355112 | 0.6475801309223157 | 3.697877603633133 | 22.99147869562731 | 32.18715296810331 | 15.914985124369508 | 4.79494303361649 | 5.673372274767742 | 3.9893589866505508 |
| user_004 | Walking | 1.446046382444e12 | 1.80165 | -9.77108 | -1.26517 | -0.9365071818181817 | -8.966354545454546 | 0.40002172727272717 | 3.2459427224999997 | 95.4740043664 | 1.6006551288999997 | 10.01601728322191 | 9.432739406241023 | 2.7381571818181816 | 0.8047254545454532 | 1.665191727272727 | 2.62224879338843 | 2.3023836363636363 | 1.404233611570248 | 7.497504752342486 | 0.6475830571933863 | 2.7728634885775283 | 7.855936617997744 | 6.33018332636108 | 2.3984448982685023 | 2.8028443799108334 | 2.5159855576614665 | 1.5486913502271853 |
| user_004 | Walking | 1.446046382526e12 | 1.49509 | -9.77108 | -0.537083 | 0.18871663636363636 | -9.586446363636364 | 0.35473372727272723 | 2.2352941081 | 95.4740043664 | 0.28845814888899995 | 9.899381628333611 | 9.712099484579783 | 1.3063733636363637 | 0.1846336363636354 | 0.8918167272727272 | 1.3019397933884298 | 0.37655123966942144 | 0.9212700578512397 | 1.706611365218587 | 3.408957967685915e-2 | 0.7953370750434379 | 2.2620555639238655 | 0.17967579373283246 | 1.0729043552679676 | 1.5040131528427088 | 0.4238818157609883 | 1.0358109650259393 |
| user_004 | Walking | 1.446046382542e12 | 3.06622 | -9.92436 | 0.727487 | 0.3419984545454545 | -9.628250000000001 | 0.5498190909090909 | 9.4017050884 | 98.4929214096 | 0.529237335169 | 10.412678033684179 | 9.782887078405635 | 2.7242215454545455 | 0.29610999999999876 | 0.17766790909090913 | 1.2880055454545454 | 0.33443033057851257 | 0.7949080743801654 | 7.421383028718752 | 8.768113209999927e-2 | 3.156588592073555e-2 | 2.203681134481383 | 0.1306830720105938 | 0.8324067890313998 | 1.4844800889474346 | 0.3615011369423252 | 0.9123632988187325 |
| user_004 | Walking | 1.44604638255e12 | 3.44943 | -10.15428 | 1.11069 | 0.5614693636363636 | -9.711857272727274 | 0.5951067272727273 | 11.8985673249 | 103.1094023184 | 1.2336322760999998 | 10.781539867727615 | 9.916425850711208 | 2.8879606363636365 | 0.44242272727272614 | 0.5155832727272727 | 1.4675725537190083 | 0.34519809917355376 | 0.798075049586777 | 8.340316637185861 | 0.195737869607437 | 0.2658261111161652 | 2.8861586223685416 | 0.13893548986183332 | 0.8355621408631894 | 1.6988698073626896 | 0.37274051277240217 | 0.9140908821682827 |
| user_004 | Walking | 1.446046382599e12 | 0.422122 | -11.03565 | -1.53341 | 2.1674385454545453 | -10.547936363636364 | -0.2583901818181818 | 0.178186982884 | 121.78557092250001 | 2.3513462280999997 | 11.149668341860398 | 10.854335944055732 | 1.7453165454545454 | 0.48771363636363674 | 1.2750198181818182 | 1.5822173305785123 | 0.5909556198347105 | 0.7198518347107438 | 3.046129843837388 | 0.23786459109504168 | 1.6256755367563966 | 3.3174814332004483 | 0.4400590329217879 | 0.6180679803025185 | 1.8213954631546792 | 0.6633694543177187 | 0.7861729964216009 |
| user_004 | Walking | 1.446046382687e12 | 2.79798 | -11.41886 | 1.72382 | 0.2549070909090908 | -12.028497272727272 | -0.1469123636363636 | 7.8286920804 | 130.39036369960002 | 2.9715553923999996 | 11.88236555456867 | 12.250013800084162 | 2.543072909090909 | 0.6096372727272712 | 1.8707323636363635 | 2.0300252066115703 | 0.8880161983471073 | 1.666774603305785 | 6.4672198209521 | 0.3716576042983452 | 3.4996395763564956 | 4.895253277219688 | 1.4451457322972197 | 3.0411812288386826 | 2.2125219269466436 | 1.20214214313334 | 1.7438982851183387 |
| user_004 | Walking | 1.446046382744e12 | -5.97737 | -15.67241 | -1.15021 | 0.4290891818181816 | -13.857420000000001 | 0.3651851818181818 | 35.7289521169 | 245.62443520809998 | 1.3229830441 | 16.812982197370577 | 14.241890340397338 | 6.406459181818181 | 1.814989999999998 | 1.5153951818181817 | 2.7090246694214875 | 1.3513440495867766 | 1.5945680743801653 | 41.042719248302475 | 3.294188700099993 | 2.2964225570777597 | 10.086290074801907 | 2.7503802530840717 | 3.074455089508204 | 3.1758920124591623 | 1.6584270418333367 | 1.7534124128419428 |
| user_004 | Walking | 1.446046382874e12 | 1.34181 | -8.46819 | -0.881966 | -0.7936763636363637 | -8.287039090909092 | -0.1712992727272726 | 1.8004540760999999 | 71.7102418761 | 0.7778640251560001 | 8.619081156211259 | 9.0857188458721 | 2.135486363636364 | 0.18115090909090803 | 0.7106667272727274 | 4.147777768595042 | 3.0995113223140494 | 1.454587743801653 | 4.56030200927686 | 3.2815651864462426e-2 | 0.5050471972525291 | 22.151839089038155 | 13.248915653020816 | 3.235745610163372 | 4.706574028849238 | 3.639905995080205 | 1.7988178368482373 |
| user_004 | Walking | 1.446046383109e12 | 3.25783 | -15.05928 | -6.89825 | 0.4116725454545454 | -7.186202727272726 | -5.65806909090909 | 10.613456308899998 | 226.78191411839998 | 47.5858530625 | 16.881386894737055 | 9.385308156033904 | 2.8461574545454544 | 7.873077272727273 | 1.2401809090909097 | 1.822907561983471 | 2.95668132231405 | 2.6529682479338845 | 8.100612256064661 | 61.985345742334715 | 1.5380486872735553 | 3.784964526944364 | 15.164207426378965 | 7.807534354208834 | 1.9454985291550246 | 3.894124731743831 | 2.7941965489580065 |
| user_004 | Walking | 1.446046383238e12 | -2.60518 | -17.89499 | 2.22198 | -1.7795530909090909 | -10.203056363636364 | -5.9890181818181825 | 6.7869628323999995 | 320.2306671001 | 4.937195120399999 | 18.21962746745663 | 12.910209327806722 | 0.825626909090909 | 7.691933636363636 | 8.210998181818182 | 2.0654962975206614 | 5.344258347107439 | 2.0062738842975207 | 0.681659793015008 | 59.16584306622231 | 67.42049114182149 | 4.659550859556643 | 34.04221112690023 | 9.778387378413225 | 2.1585992818391846 | 5.834570346383718 | 3.12704131383217 |
| user_005 | Jogging | 1.446046533265e12 | -2.68182 | -12.95167 | -2.49142 | -1.756662142857143 | -10.679820571428573 | -3.099071 | 7.192158512400001 | 167.7457557889 | 6.207173616400001 | 13.459015116928134 | 13.466680230426723 | 0.9251578571428571 | 2.271849428571427 | 0.6076509999999997 | 2.4322976343537417 | 4.564965994557824 | 2.023260533333333 | 0.8559170606331633 | 5.1612998261003185 | 0.36923973780099967 | 13.150513999738278 | 36.98679497306271 | 8.17024775704289 | 3.6263637434402907 | 6.0816769869060545 | 2.8583645248713276 |
| user_005 | Jogging | 1.4460465343e12 | -3.14167 | -17.74171 | 3.21831 | 0.5684354545454545 | -8.31839309090909 | -0.5754035454545446 | 9.8700903889 | 314.7682737241 | 10.357519256099998 | 18.30289275959131 | 13.293004784553316 | 3.7101054545454546 | 9.42331690909091 | 3.7937135454545445 | 4.222201925619835 | 7.810693338842976 | 4.760586694214876 | 13.764882483847934 | 88.79890156915867 | 14.39226246496529 | 25.92933548225511 | 79.23515047717568 | 32.34629293348333 | 5.092085572950941 | 8.901412836015172 | 5.687380146735695 |
| user_005 | Jogging | 1.446046535336e12 | 13.79591 | -19.58108 | -3.33447 | 0.8018412727272726 | -8.168594818181818 | -0.40818800000000005 | 190.32713272809997 | 383.4186939664 | 11.1186901809 | 24.183972313815612 | 13.264153394433299 | 12.994068727272726 | 11.412485181818182 | 2.926282 | 4.401135628099173 | 7.751153892561984 | 4.965488429752067 | 168.84582208908705 | 130.2448180252196 | 8.563126343524 | 30.875727116802615 | 81.80166573789738 | 34.38620489012605 | 5.5565931214011535 | 9.044427330566451 | 5.863975178164217 |
| user_005 | Jogging | 1.446046535466e12 | 2.60638 | 11.61165 | 7.58682 | 0.7356521818181817 | -8.203432090909091 | -0.5266325454545453 | 6.793216704400001 | 134.8304157225 | 57.559837712400004 | 14.113237408167553 | 13.255970167815521 | 1.8707278181818183 | 19.81508209090909 | 8.113452545454546 | 4.314676371900827 | 7.832861909090908 | 4.815374297520662 | 3.4996225697193064 | 392.63747826946616 | 65.82811220734285 | 30.481313367488237 | 83.02335248056642 | 32.21978126860708 | 5.520988441165969 | 9.111715122882542 | 5.676247111305768 |
| user_005 | Jogging | 1.446046535855e12 | -1.72382 | -1.03405 | -6.36177 | 1.2233646363636366 | -8.213882727272726 | -0.599789818181818 | 2.9715553923999996 | 1.0692594024999997 | 40.4721175329 | 6.671801280598816 | 13.037914555049912 | 2.9471846363636365 | 7.179832727272726 | 5.761980181818182 | 3.9606093884297526 | 8.004827487603306 | 4.7821222314049585 | 8.68589728081786 | 51.54999799161651 | 33.20041561566549 | 27.13730258213853 | 83.43492617701699 | 30.17409682118895 | 5.2093476157901515 | 9.134272066071658 | 5.493095377033695 |
| user_005 | Jogging | 1.446046536437e12 | 2.49142 | -13.67975 | 6.62882 | 1.3627123636363636 | -7.778423909090909 | -0.9481554545454542 | 6.207173616400001 | 187.13556006250002 | 43.9412545924 | 15.40402506721214 | 13.505095304889357 | 1.1287076363636366 | 5.901326090909091 | 7.576975454545455 | 4.147145239669422 | 7.830643504132232 | 5.837039223140496 | 1.2739809283855872 | 34.825649631244374 | 57.4105570387843 | 31.226787615125485 | 80.20664756182254 | 42.00361180619343 | 5.588093379241751 | 8.955816409564376 | 6.481019349314846 |
| user_005 | Jogging | 1.446046536566e12 | 2.72134 | -6.85874 | -8.54603 | 1.4358696363636363 | -8.659791181818182 | -0.5997890909090908 | 7.405691395600001 | 47.0423143876 | 73.0346287609 | 11.290820809139609 | 14.161374394521529 | 1.2854703636363638 | 1.801051181818182 | 7.946240909090909 | 4.340646545454546 | 7.698897280991735 | 5.504824561983471 | 1.6524340557874053 | 3.2437853595286703 | 63.14274458530991 | 33.926766159153416 | 82.07243264506836 | 39.69246186369529 | 5.824668759608001 | 9.059383679095856 | 6.30019538297784 |
| user_005 | Jogging | 1.446046537191e12 | -8.88971 | -19.58108 | 8.58315 | -3.7200084545454546 | -12.683423636363635 | 8.053690000000001 | 79.02694388409998 | 383.4186939664 | 73.67046392249999 | 23.154181086209892 | 19.006897586499694 | 5.1697015454545445 | 6.897656363636365 | 0.5294599999999985 | 3.929191355371901 | 8.388347768595041 | 4.496189636363636 | 26.725814069075106 | 47.57766331081324 | 0.2803278915999984 | 37.982441675489866 | 95.07989014595297 | 45.417514090548984 | 6.1629896702404 | 9.75089176157509 | 6.739251745598245 |
| user_005 | Jogging | 1.446046537199e12 | -7.39522 | -19.58108 | 2.6435 | -2.8212230000000003 | -15.661957272727271 | 6.855309090909091 | 54.6892788484 | 383.4186939664 | 6.98809225 | 21.097299947263394 | 18.48057756225118 | 4.573997 | 3.919122727272729 | 4.211809090909091 | 2.83911620661157 | 7.010399669421488 | 3.3991572892561988 | 20.921448556009004 | 15.359522951425634 | 17.73933581826446 | 14.939538625687815 | 63.39304411683022 | 22.938252788811482 | 3.865169934904262 | 7.961974887980382 | 4.789389605034391 |
| user_005 | Jogging | 1.446046537247e12 | 2.33814 | -11.8787 | -8.46939 | -3.695568181818182 | -17.215671818181818 | -4.0451345454545455 | 5.466898659600001 | 141.10351369 | 71.73056697210001 | 14.775011990577198 | 20.703948053627844 | 6.033708181818183 | 5.3369718181818175 | 4.424255454545455 | 4.618996033057852 | 5.3781446280991725 | 7.32362917355372 | 36.40563442333968 | 28.483268188066937 | 19.574036327075213 | 25.153184588790236 | 36.76646394369556 | 94.20583036550964 | 5.015295064977757 | 6.063535597627474 | 9.705968800975493 |
| user_005 | Jogging | 1.446046537506e12 | 4.25415 | -19.58108 | -7.12818 | 11.86596181818182 | -19.25013181818181 | -3.574839363636364 | 18.0977922225 | 383.4186939664 | 50.81095011240001 | 21.267990885396298 | 24.067887578832163 | 7.6118118181818195 | 0.3309481818181901 | 3.5533406363636364 | 8.285105297520662 | 6.445729578512396 | 4.262738148760331 | 57.93967915541241 | 0.1095266990487658 | 12.626229678033132 | 94.68215108093536 | 61.12387683692416 | 22.080502893756435 | 9.730475377952269 | 7.818176055636261 | 4.698989560932907 |
| user_005 | Jogging | 1.446046537628e12 | 1.95493 | 13.60431 | 7.7401 | 1.090985181818182 | 2.644700909090909 | 4.172831818181819 | 3.8217513049000003 | 185.0772505761 | 59.90914801 | 15.773653663340019 | 11.77319875359906 | 0.8639448181818181 | 10.95960909090909 | 3.5672681818181813 | 3.8969519008264473 | 13.113463801652891 | 4.991775173553719 | 0.7464006488632148 | 120.11303142553717 | 12.725402281012393 | 23.85319022932108 | 201.51069996120503 | 25.8405193778445 | 4.88397279162375 | 14.195446451633885 | 5.0833570972187765 |
| user_005 | Jogging | 1.4460465377e12 | -3.14167 | -3.90807 | -1.38013 | 0.41863736363636356 | 5.396795727272727 | 3.998649181818182 | 9.8700903889 | 15.2730111249 | 1.9047588169000003 | 5.200755746110366 | 8.354313194227272 | 3.5603073636363636 | 9.304865727272727 | 5.3787791818181825 | 1.4473028016528926 | 7.501913107438015 | 3.2230250247933885 | 12.675788523563314 | 86.58052620257462 | 28.931265486760676 | 3.4301140495762428 | 69.1345025313832 | 12.806767356329642 | 1.8520567079806824 | 8.314716022293437 | 3.5786544058248544 |
| user_005 | Jogging | 1.446046537798e12 | 12.60798 | -11.95534 | 0.229323 | 7.236171454545453 | -15.864010909090911 | -2.282398818181818 | 158.9611596804 | 142.93015451559998 | 5.2589038329e-2 | 17.376533118960438 | 23.27245020551651 | 5.371808545454546 | 3.9086709090909117 | 2.511721818181818 | 6.8476180165289255 | 8.136574719008266 | 12.404698256198346 | 28.856327049018486 | 15.277708275573575 | 6.308746491930577 | 72.38351563915937 | 83.04199427584591 | 203.43031800479287 | 8.507850236056072 | 9.112738023000876 | 14.262900055907034 |
| user_005 | Jogging | 1.446046537806e12 | 9.77228 | -10.57581 | 5.13432 | 8.497257818181817 | -16.17057363636364 | -0.5788870000000004 | 95.4974563984 | 111.84775715610002 | 26.361241862399996 | 15.287460724950368 | 23.214003915053574 | 1.2750221818181835 | 5.594763636363638 | 5.713207000000001 | 6.64493258677686 | 7.828745190082646 | 11.612955909090909 | 1.625681564128401 | 31.301380146776875 | 32.640734224849005 | 71.41476358365733 | 78.55517815891763 | 187.48812677917655 | 8.45072562468202 | 8.863135909987934 | 13.692630382040427 |
| user_005 | Jogging | 1.446046537927e12 | -3.83143 | -18.50811 | -14.17911 | -4.218119 | -18.00646363636363 | 1.1385608181818176 | 14.6798558449 | 342.55013577209996 | 201.04716039209998 | 23.62788928383363 | 20.373295370199216 | 0.3866889999999996 | 0.5016463636363682 | 15.317670818181817 | 6.313667504132232 | 4.587668512396696 | 4.9331842727272734 | 0.1495283827209997 | 0.2516490741495913 | 234.6310392941788 | 50.65188862554589 | 26.09155367340835 | 49.933594930582096 | 7.11701402454329 | 5.107989200596292 | 7.066370704299492 |
| user_005 | Jogging | 1.446046538089e12 | 0.805325 | -0.650847 | -0.728685 | -6.789063272727273 | -8.649263636363637e-2 | -4.107839545454545 | 0.6485483556249999 | 0.42360181740899994 | 0.530981829225 | 1.2661484913938805 | 9.135670010436984 | 7.594388272727273 | 0.5643543636363636 | 3.379154545454545 | 7.67958094214876 | 3.2204892809917354 | 3.0206535537190082 | 57.674733236937534 | 0.3184958477554049 | 11.418685442066112 | 73.21571867055573 | 14.941258901488242 | 12.832239867491966 | 8.556618413284289 | 3.8653924640957538 | 3.5822115888780166 |
| user_005 | Jogging | 1.446046538178e12 | 15.86521 | -19.58108 | -6.13185 | 3.5330351818181813 | -11.899599272727274 | -0.930738 | 251.70488834409997 | 383.4186939664 | 37.5995844225 | 25.936907424228508 | 13.572404537765589 | 12.332174818181818 | 7.681480727272726 | 5.201112 | 4.570884867768594 | 7.491462446280992 | 4.1911655206611576 | 152.08253574619775 | 59.00514616346233 | 27.051566036544003 | 38.07839845566661 | 76.72006506801198 | 21.170354081173457 | 6.1707696809771315 | 8.758999090536086 | 4.601125305963038 |
| user_005 | Jogging | 1.446046538493e12 | 9.77228 | -12.83671 | 11.61046 | 1.1467243636363635 | -8.997707272727272 | -2.7074071818181817 | 95.4974563984 | 164.7811236241 | 134.8027814116 | 19.876653677973565 | 13.231876040647904 | 8.625555636363636 | 3.839002727272728 | 14.317867181818182 | 2.7254921900826443 | 6.384607595041322 | 8.92989958677686 | 74.40021003600448 | 14.737941940007444 | 205.0013206361861 | 13.09131786918851 | 45.14691348141067 | 102.19151263494622 | 3.618192624666148 | 6.719145293964901 | 10.108981780325168 |
| user_005 | Jogging | 1.44604653851e12 | 19.54396 | -19.58108 | -17.24474 | 4.961337090909091 | -11.714966363636364 | -3.1637672727272728 | 381.96637248159993 | 383.4186939664 | 297.38105766760003 | 32.60009392801806 | 17.782204210454342 | 14.582622909090908 | 7.866113636363636 | 14.080972727272727 | 4.728282619834711 | 6.480883942148759 | 10.929839975206612 | 212.652890908743 | 61.875743740185946 | 198.2737929461983 | 47.499936104067004 | 46.55667068685511 | 135.73274442981278 | 6.892019740545365 | 6.823244879590289 | 11.650439666802828 |
| user_005 | Jogging | 1.446046538793e12 | -0.574206 | -3.98471 | -7.28146 | -7.8376469090909096 | -0.5324014545454545 | -6.713621181818181 | 0.329712530436 | 15.877913784100002 | 53.0196597316 | 8.320293627398975 | 12.435762186053363 | 7.263440909090909 | 3.452308545454546 | 0.5678388181818192 | 7.900635925619834 | 5.027245347107438 | 2.382826198347108 | 52.757573839855375 | 11.918434293018482 | 0.32244092343412517 | 85.47902965290235 | 37.76294123158168 | 10.598142654253946 | 9.245486988412365 | 6.145155915970048 | 3.2554788671183146 |
| user_005 | Jogging | 1.446046538826e12 | -7.6042e-2 | -3.48655 | -0.1922 | -5.848474090909092 | -1.5147946363636364 | -3.268276545454546 | 5.782385764e-3 | 12.1560309025 | 3.694084e-2 | 3.4926714887409607 | 8.79684486571532 | 5.772432090909092 | 1.9717553636363634 | 3.076076545454546 | 7.780923876033058 | 2.39739494214876 | 3.527368603305786 | 33.32097224415711 | 3.8878192140287675 | 9.462246913495573 | 76.2985903174133 | 7.323591190988912 | 15.880524485696595 | 8.73490642865814 | 2.706213441506215 | 3.9850375764472528 |
| user_005 | Jogging | 1.446046538939e12 | 0.11556 | -19.58108 | -11.68829 | 10.354050363636363 | -19.23271363636363 | -4.720963272727273 | 1.3354113599999998e-2 | 383.4186939664 | 136.6161231241 | 22.804564701043954 | 23.674808329678697 | 10.238490363636362 | 0.3483663636363694 | 6.967326727272727 | 8.065950421487605 | 6.036874181818183 | 4.233919008264463 | 104.82668492627465 | 0.12135912331322717 | 48.54364172456889 | 87.57518667666108 | 52.2563347149799 | 22.62891425410126 | 9.358161500885796 | 7.228854315517771 | 4.756985837071754 |
| user_005 | Jogging | 1.446046538947e12 | -3.94639 | -19.58108 | -8.85259 | 10.044004545454545 | -19.570629090909087 | -5.835736 | 15.5739940321 | 383.4186939664 | 78.36834970809998 | 21.84859349492777 | 24.18511751724803 | 13.990394545454546 | 1.0450909090913285e-2 | 3.0168539999999995 | 9.269081396694215 | 5.096284652892563 | 4.0549853140495875 | 195.7311395374843 | 1.0922150082653395e-4 | 9.101408057315997 | 105.31697516413934 | 42.50487940249286 | 21.1970906556682 | 10.26240591499573 | 6.519576627549742 | 4.604029827843017 |
| user_005 | Jogging | 1.446046538979e12 | -4.9044 | -13.48815 | 4.48288 | 3.7316036363636367 | -18.936601818181813 | -4.727930727272727 | 24.05313936 | 181.93019042249998 | 20.0962130944 | 15.03594170236437 | 22.799217118887203 | 8.636003636363636 | 5.448451818181814 | 9.210810727272726 | 10.874417636363638 | 2.1022333057851252 | 4.469224636363635 | 74.58055880728594 | 29.68562721504872 | 84.83903425364232 | 137.5093735084357 | 9.196215785575577 | 26.529507394367275 | 11.726439080489682 | 3.0325263041852706 | 5.150680284619428 |
| user_005 | Jogging | 1.446046539076e12 | 2.22318 | 4.714 | 7.58682 | 3.337951181818182 | 8.69234272727273 | 7.287228181818182 | 4.9425293124000005 | 22.221796000000005 | 57.559837712400004 | 9.204572940924528 | 12.255265287128658 | 1.114771181818182 | 3.9783427272727288 | 0.2995918181818187 | 3.6958518016528927 | 12.376827272727269 | 4.278889537190082 | 1.2427147878123062 | 15.827210855643813 | 8.97552575214879e-2 | 22.984673684992117 | 209.9559773255614 | 26.829853647783384 | 4.794233378235995 | 14.489857740004261 | 5.179754207275031 |
| user_005 | Jogging | 1.446046539133e12 | -1.83878 | -5.82409 | -7.7413 | -0.1352640909090908 | 1.7459141818181818 | 1.9432845454545458 | 3.3811118884000004 | 33.9200243281 | 59.927725689999995 | 9.860469659529407 | 6.6261052838034376 | 1.7035159090909093 | 7.570004181818182 | 9.684584545454545 | 2.386311570247934 | 5.564680247933885 | 4.438188 | 2.9019664525258273 | 57.30496331274476 | 93.79117781805701 | 7.486160788873022 | 35.40458672840595 | 28.815742980033075 | 2.7360849381685908 | 5.950175352744316 | 5.36802971117272 |
| user_005 | Jogging | 1.446046539158e12 | 1.91661 | -15.67241 | -16.55497 | -0.5393703636363636 | -3.3263021818181815 | -4.480592727272727 | 3.6733938920999996 | 245.62443520809998 | 274.06703170090003 | 22.877168985718054 | 9.004548610586447 | 2.4559803636363635 | 12.346107818181817 | 12.074377272727274 | 2.4233651074380167 | 7.4813292396694235 | 7.8037251239669425 | 6.031839546567404 | 152.4263782581702 | 145.79058652415293 | 7.831012942022457 | 59.9454553949351 | 78.68714381250022 | 2.7983947080464646 | 7.742445052755305 | 8.870577422721714 |
| user_005 | Jogging | 1.446046539262e12 | -3.29495 | -8.96635 | 8.08499 | 7.434739363636364 | -11.014747272727275 | 7.938672727272728 | 10.856695502500001 | 80.3954323225 | 65.36706330009999 | 12.514758931961094 | 18.097212429523772 | 10.729689363636364 | 2.048397272727275 | 0.14631727272727169 | 8.480824768595042 | 2.806567272727272 | 8.609085867768597 | 115.12623384013132 | 4.195931386916539 | 2.1408744298346803e-2 | 87.08888798453219 | 11.310359394584937 | 97.52241800503252 | 9.332142732756084 | 3.363087776818342 | 9.875343943632167 |
| user_005 | Jogging | 1.446046539302e12 | -5.90073 | -19.58108 | 9.00468 | 0.3803184545454543 | -14.035085454545454 | 8.408968181818182 | 34.8186145329 | 383.4186939664 | 81.0842619024 | 22.345504478567943 | 17.498293291626553 | 6.281048454545455 | 5.545994545454546 | 0.5957118181818188 | 7.303660975206612 | 3.1999061157024795 | 3.8785865289256205 | 39.451569688347845 | 30.758055498211576 | 0.3548725703214884 | 61.10605286841874 | 15.428117018312696 | 27.387089683350485 | 7.8170360667211165 | 3.927864180227302 | 5.233267591414611 |
| user_005 | Jogging | 1.446046539319e12 | -12.76007 | -19.58108 | -0.996927 | -3.8209815454545453 | -14.57853727272727 | 6.8204166363636345 | 162.81938640490003 | 383.4186939664 | 0.993863443329 | 23.39298920220819 | 17.550602729410397 | 8.939088454545455 | 5.002542727272729 | 7.817343636363635 | 8.1581096446281 | 3.2246087603305784 | 3.2974476859504134 | 79.90730239818785 | 25.025433738189275 | 61.11086152899502 | 70.00862498085486 | 15.035771101271676 | 18.016229528635986 | 8.367115690657974 | 3.877598625602149 | 4.244552924471079 |
| user_005 | Jogging | 1.4460465394e12 | 7.39642 | -2.56686 | -6.85994 | 2.146536363636363 | -14.540217272727268 | -8.664473363636363 | 54.7070288164 | 6.5887702596 | 47.0587768036 | 10.409350406226125 | 20.08056548210617 | 5.249883636363637 | 11.973357272727268 | 1.804533363636363 | 8.138792611570247 | 5.077282975206612 | 7.848378743801652 | 27.561278195358685 | 143.36128438037096 | 3.256340660476766 | 80.77637375669282 | 37.16680859849946 | 89.7226633472793 | 8.987567733079558 | 6.0964586932496685 | 9.472204777520348 |
| user_005 | Jogging | 1.446046539578e12 | 7.20482 | -19.58108 | -5.59536 | 0.6590119999999999 | -9.952231818181817 | -0.3907689090909091 | 51.909431232399996 | 383.4186939664 | 31.308053529600002 | 21.601763324515893 | 10.362929003394015 | 6.545808 | 9.628848181818183 | 5.2045910909090916 | 4.776739033057851 | 5.110536950413223 | 4.43185461983471 | 42.847602372864 | 92.71471730850334 | 27.08776842357029 | 36.474788326173105 | 42.3693493244356 | 21.37286011656183 | 6.039436093392586 | 6.509174242900216 | 4.623079073146147 |
| user_005 | Jogging | 1.44604653961e12 | 19.16076 | -19.58108 | 1.83878 | 6.097012454545455 | -15.902331818181814 | -1.007377 | 367.1347237776 | 383.4186939664 | 3.3811118884000004 | 27.457868264532117 | 18.36803215234251 | 13.063747545454545 | 3.678748181818186 | 2.846157 | 5.852874611570247 | 6.96764694214876 | 3.697434991735537 | 170.66149993136963 | 13.533188185230609 | 8.100609668649 | 58.13333525565159 | 55.72582041356123 | 16.12637407479173 | 7.624521968992652 | 7.464972901060072 | 4.0157656897273935 |
| user_005 | Jogging | 1.446046539682e12 | -6.89706 | -9.50284 | 5.67081 | 3.9998454545454547 | -18.448889090909088 | -3.1358959090909093 | 47.5694366436 | 90.30396806560002 | 32.158086056100004 | 13.03961237020871 | 22.043440536798908 | 10.896905454545454 | 8.946049090909087 | 8.80670590909091 | 10.263827082644628 | 2.2273277685950434 | 3.8956880661157025 | 118.74254848530246 | 80.0317943369553 | 77.55806896921675 | 120.76651362675165 | 12.032200956290906 | 21.95008966866136 | 10.989381858264442 | 3.468746309012942 | 4.685092279631358 |
| user_005 | Jogging | 1.446046539755e12 | 0.345482 | 6.70665 | 10.88237 | 0.10510900000000002 | 6.06217509090909 | 8.36019909090909 | 0.119357812324 | 44.9791542225 | 118.4259768169 | 12.78766940656991 | 14.882968130300673 | 0.240373 | 0.6444749090909099 | 2.52217090909091 | 4.839443595041322 | 13.605293487603307 | 7.101292727272729 | 5.7779179129e-2 | 0.41534790844773656 | 6.361346094664468 | 39.33490163750689 | 251.6198299976049 | 54.91098830766759 | 6.27175427113554 | 15.862529117313068 | 7.4101948899922725 |
| user_005 | Jogging | 1.446046539772e12 | 1.87829 | 3.87095 | 8.73643 | 1.6692744545454545 | 9.27411418181818 | 9.293819999999998 | 3.5279733241 | 14.9842539025 | 76.3252091449 | 9.738451436008704 | 13.889704399334034 | 0.20901554545454548 | 5.403164181818179 | 0.557389999999998 | 2.7011064214876033 | 13.387405983471075 | 5.818670082644629 | 4.368749824166117e-2 | 29.19418317568291 | 0.31068361209999784 | 12.831011815806407 | 247.56773383231638 | 43.55359550083381 | 3.5820401750687285 | 15.734285297792091 | 6.599514792833926 |
| user_005 | Jogging | 1.446046539942e12 | 0.537083 | -9.27292 | 11.76374 | 6.584724818181818 | -9.77108 | 4.778988181818182 | 0.28845814888899995 | 85.98704532639998 | 138.3855787876 | 14.988698484621304 | 16.5450927999781 | 6.047641818181818 | 0.4981600000000004 | 6.9847518181818185 | 4.964222173553718 | 3.317082661157025 | 9.577860421487607 | 36.57397156102149 | 0.24816338560000037 | 48.78675796159422 | 37.43595558677263 | 25.67015866625859 | 127.0524021856521 | 6.118492917931068 | 5.066572674526498 | 11.271752400831563 |
| user_005 | Jogging | 1.44604653999e12 | -0.804128 | -19.58108 | 5.40257 | 3.156799727272728 | -10.986879090909092 | 7.015502727272727 | 0.646621840384 | 383.4186939664 | 29.187762604899998 | 20.32862706657004 | 15.52897945485492 | 3.960927727272728 | 8.594200909090908 | 1.6129327272727272 | 5.840839694214875 | 2.2605820661157026 | 4.385301074380165 | 15.6889484606779 | 73.86028926581899 | 2.601551982707438 | 40.40700436750239 | 12.320737220128098 | 25.776883221690685 | 6.356650404694472 | 3.5100907709243216 | 5.077093974085046 |
| user_005 | Jogging | 1.446046540096e12 | 9.8106 | 0.613724 | -5.25048 | 2.376456363636364 | -13.192039636363639 | -8.350943636363636 | 96.24787236000002 | 0.37665714817600005 | 27.567540230399995 | 11.14414957448867 | 18.601696635532743 | 7.434143636363637 | 13.805763636363638 | 3.100463636363637 | 6.501151743801654 | 6.114781735537192 | 7.790107867768596 | 55.266491606085964 | 190.59910958314055 | 9.612874760413225 | 54.78844817181653 | 52.86952324153863 | 86.24638087968755 | 7.401921924190806 | 7.271143186703081 | 9.286892961571569 |
| user_005 | Jogging | 1.446046540104e12 | 2.2615 | 1.95493 | -8.66099 | 3.400654545454545 | -11.234220545454544 | -8.657506363636365 | 5.114382249999999 | 3.8217513049000003 | 75.0127477801 | 9.16236221369795 | 17.417205621653796 | 1.139154545454545 | 13.189150545454545 | 3.483636363634801e-3 | 5.963083008264463 | 6.803280214876033 | 6.720622851239669 | 1.2976730784297512 | 173.95369211066392 | 1.21357223140387e-5 | 50.3778636743197 | 65.81661171360078 | 73.65714927952101 | 7.097736517673765 | 8.112743784540516 | 8.582374338114192 |
| user_005 | Jogging | 1.44604654012e12 | -13.06663 | -2.2603 | -14.94552 | 2.752692727272727 | -7.8515822727272715 | -9.939495454545453 | 170.7368195569 | 5.1089560899999995 | 223.36856807040002 | 19.98034893882737 | 16.54993111786699 | 15.819322727272727 | 5.5912822727272715 | 5.006024545454547 | 7.762237380165288 | 7.418938900826446 | 5.458586206611571 | 250.25097154960744 | 31.26243745331424 | 25.060281749693406 | 79.2542529639753 | 73.90660281971599 | 53.25524326196705 | 8.902485774432627 | 8.59689495223223 | 7.297619013210203 |
| user_005 | Jogging | 1.446046540161e12 | -9.69444 | -3.14167 | -4.9056 | -5.496624545454545 | -2.319522272727273 | -7.13166090909091 | 93.9821669136 | 9.8700903889 | 24.064911359999996 | 11.310047244043679 | 15.320231174103524 | 4.197815454545455 | 0.8221477272727271 | 2.2260609090909105 | 11.350412371900823 | 6.493867867768595 | 3.1625339338842977 | 17.621654590420665 | 0.6759268854597105 | 4.955347170982651 | 159.6364509893014 | 66.65468024152023 | 12.332436510854023 | 12.63473193183383 | 8.164231760644734 | 3.5117568980289655 |
| user_005 | Jogging | 1.446046540193e12 | 1.41845 | -3.52487 | -0.307161 | -9.380910363636366 | -3.1695362727272727 | -7.542732818181817 | 2.0120004025 | 12.424708516899999 | 9.434787992100001e-2 | 3.8119623292106386 | 14.2613084737019 | 10.799360363636366 | 0.3553337272727273 | 7.235571818181818 | 10.904820504132232 | 2.8220859338842974 | 3.948575702479339 | 116.62618426368017 | 0.12626205773752894 | 52.353499536066934 | 151.38493928892447 | 15.014976780614317 | 17.83407046135147 | 12.303858715416252 | 3.8749163578862342 | 4.22304042857175 |
| user_005 | Jogging | 1.446046540217e12 | -2.29862 | -6.7821 | 0.689167 | -6.287415909090908 | -4.050903636363636 | -3.8430809090909097 | 5.2836539044 | 45.996880409999996 | 0.47495115388899994 | 7.1941285412681495 | 10.53827068016974 | 3.9887959090909075 | 2.7311963636363634 | 4.53224790909091 | 8.581849818181821 | 1.5055775371900824 | 4.445789826446281 | 15.91049280438036 | 7.459433576740495 | 20.541271109458926 | 97.87125545609757 | 3.4811748435715795 | 22.0431335507613 | 9.892990218134129 | 1.865790675175428 | 4.695011560237238 |
| user_005 | Jogging | 1.446046540339e12 | -2.10702 | -19.58108 | -9.65732 | 9.967363636363636 | -19.52882545454545 | -3.710701727272727 | 4.439533280399999 | 383.4186939664 | 93.2638295824 | 21.934494679139522 | 23.708771637632957 | 12.074383636363637 | 5.225454545454866e-2 | 5.946618272727273 | 9.42869852892562 | 4.468590330578514 | 4.145560669421488 | 145.79074019808596 | 2.730537520661492e-3 | 35.36226888153389 | 110.2410839227691 | 32.07802959815651 | 22.283775548103858 | 10.499575416309419 | 5.6637469574616865 | 4.720569409308993 |
| user_005 | Jogging | 1.446046540514e12 | 5.13552 | -2.79678 | -7.89458 | -2.3648110000000004 | -7.228005272727272 | -7.724018181818183e-2 | 26.373565670399998 | 7.8219783684 | 62.3243933764 | 9.824456087499195 | 16.574582047151853 | 7.500331 | 4.431225272727272 | 7.817339818181819 | 9.026175991735538 | 8.765533487603308 | 6.827665371900826 | 56.254965109561 | 19.63575741765689 | 61.11080183293095 | 104.91049056532684 | 167.91245901524488 | 57.88847597379886 | 10.2425822215556 | 12.958103989984217 | 7.608447671752685 |
| user_005 | Jogging | 1.446046540563e12 | 4.33079 | -10.57581 | 1.14901 | 1.3104566363636363 | -4.935754363636364 | -4.727931818181819 | 18.7557420241 | 111.84775715610002 | 1.3202239801000002 | 11.485805290022116 | 16.556757493226794 | 3.020333363636364 | 5.640055636363637 | 5.87694181818182 | 3.7015521404958673 | 12.720126892561984 | 11.229750066115702 | 9.122413627494954 | 31.81022758127723 | 34.53844513429423 | 17.429640415905563 | 216.0190225727229 | 139.08506675730447 | 4.174882084072023 | 14.697585603517432 | 11.793433204851947 |
| user_005 | Jogging | 1.446046540717e12 | -6.51385 | -19.58108 | -6.43841 | -2.0268926363636366 | -14.97219181818182 | 4.50029790909091 | 42.430241822499994 | 383.4186939664 | 41.453123328100006 | 21.61717046972152 | 17.517867329058955 | 4.486957363636363 | 4.60888818181818 | 10.93870790909091 | 5.2619166611570245 | 3.4266593388429754 | 4.726067801652892 | 20.132786383090583 | 21.241850272503292 | 119.65533072040803 | 35.40847356546848 | 18.10052390631796 | 32.93065278676701 | 5.950501959118111 | 4.254471048945798 | 5.738523572032009 |
| user_005 | Jogging | 1.446046540725e12 | -2.2603 | -19.58108 | -13.68095 | -1.9711544545454545 | -15.864009999999997 | 2.100052454545455 | 5.1089560899999995 | 383.4186939664 | 187.1683929025 | 23.993666726011263 | 18.21761657089616 | 0.2891455454545455 | 3.717070000000003 | 15.781002454545455 | 4.390684289256199 | 3.647080165289256 | 5.649555338842974 | 8.360514645620665e-2 | 13.816609384900024 | 249.04003847036967 | 26.55514334235929 | 19.20472444099144 | 52.69664942424522 | 5.153168281975594 | 4.382319527486722 | 7.259245788940144 |
| user_005 | Jogging | 1.446046540822e12 | -17.85667 | -2.6435 | -5.71033 | -1.9746390909090907 | -4.9949745454545456 | -9.228827272727273 | 318.86066348890006 | 6.98809225 | 32.6078687089 | 18.932950759134194 | 15.92906364072432 | 15.882030909090911 | 2.3514745454545456 | 3.5184972727272728 | 9.308036595041322 | 7.645694710743802 | 3.4605465619834708 | 252.2389057973191 | 5.529432537920662 | 12.379823058189256 | 111.33812321837313 | 84.26482645558666 | 22.85573815537694 | 10.551688169121238 | 9.179587488312677 | 4.780767527853341 |
| user_005 | Jogging | 1.44604654083e12 | -14.75272 | -3.21831 | -3.14286 | -3.5666736363636358 | -3.8558163636363645 | -8.222047272727272 | 217.6427473984 | 10.357519256099998 | 9.877568979600001 | 15.42328874248615 | 15.386100335157364 | 11.186046363636365 | 0.6375063636363647 | 5.079187272727271 | 9.959798661157025 | 7.569370495867769 | 2.874974024793388 | 125.12763324942235 | 0.40641436367686085 | 25.798143351434696 | 121.24667289853097 | 84.10343286667974 | 13.135425839644437 | 11.011206695840878 | 9.170792379433728 | 3.6242828034860133 |
| user_005 | Jogging | 1.446046540838e12 | -10.72909 | -3.25663 | -3.2195 | -4.94968909090909 | -2.831619090909091 | -7.389450909090908 | 115.11337222809999 | 10.6056389569 | 10.36518025 | 11.66551290921235 | 14.664609256904741 | 5.779400909090909 | 0.4250109090909091 | 4.169950909090908 | 9.997485338842976 | 7.3236142148760335 | 2.544659586776859 | 33.401474868000825 | 0.180634272846281 | 17.388490584228094 | 121.66666259466352 | 83.23017701976578 | 9.180451949874753 | 11.030261220599607 | 9.123057438148999 | 3.029926063433686 |
| user_005 | Jogging | 1.446046540927e12 | -4.40624 | -6.24561 | 0.497565 | -2.3682936363636364 | -2.302102909090909 | -3.285694818181818 | 19.414950937600004 | 39.0076442721 | 0.24757092922499999 | 7.659645301117083 | 5.719106541657371 | 2.037946363636364 | 3.9435070909090912 | 3.783259818181818 | 4.485693983471075 | 1.3127083719008263 | 3.0485239504132235 | 4.153225381058679 | 15.551248176050283 | 14.313054851869122 | 31.307420746990218 | 2.6778831208879526 | 12.53341286799084 | 5.595303454415159 | 1.6364238817885641 | 3.540256045541175 |
| user_005 | Jogging | 1.446046540976e12 | 3.2195 | -19.58108 | -6.9749 | -3.1137981818181815 | -10.206538636363636 | -1.711078363636364 | 10.36518025 | 383.4186939664 | 48.64923001 | 21.034093853227905 | 11.824510576150509 | 6.333298181818181 | 9.374541363636364 | 5.263821636363636 | 2.541175404958677 | 6.296882462809918 | 2.6064149173553717 | 40.11066585982148 | 87.88202577852914 | 27.707818219449944 | 9.363454819684579 | 59.75930482568696 | 9.126199517026393 | 3.0599762776342856 | 7.730414272578602 | 3.020960032345081 |
| user_005 | Jogging | 1.446046541161e12 | 3.33447 | 3.52607 | 4.7128 | 1.1502080909090908 | 9.664284545454546 | 10.102029090909092 | 11.1186901809 | 12.4331696449 | 22.21048384 | 6.764787037727056 | 14.376779236094603 | 2.1842619090909094 | 6.138214545454546 | 5.389229090909092 | 3.1340315041322313 | 12.022759669421486 | 6.602495041322314 | 4.771000087505464 | 37.677677806029756 | 29.04379019430084 | 12.984159483889442 | 206.43890998226743 | 57.00039092718214 | 3.60335392154163 | 14.367982112400734 | 7.549860325011459 |
| user_005 | Jogging | 1.446046541235e12 | 7.58802 | -19.58108 | -19.54396 | 2.4949023636363634 | -3.928976181818182 | -3.397172818181819 | 57.578047520400006 | 383.4186939664 | 381.96637248159993 | 28.687333685241644 | 9.474860604030479 | 5.093117636363637 | 15.652103818181818 | 16.14678718181818 | 2.017675578512397 | 8.562214818181818 | 8.995139520661157 | 25.93984725783832 | 244.98835393514184 | 260.71873629492785 | 5.3878930097540945 | 83.3646869357908 | 98.37953343385551 | 2.3211835364214726 | 9.130426437784317 | 9.918645745960257 |
| user_005 | Jogging | 1.44604654134e12 | -1.57053 | -7.85507 | 11.4955 | 3.2404081818181822 | -8.774752727272727 | 9.586448181818183 | 2.4665644809 | 61.70212470490001 | 132.14652025 | 14.011252957383933 | 15.148865444150692 | 4.810938181818182 | 0.9196827272727264 | 1.9090518181818172 | 4.711814024793388 | 2.3742772479338843 | 9.278583223140494 | 23.145126189276034 | 0.8458163188438 | 3.6444788445033023 | 27.561006826288562 | 7.091636321663279 | 126.34631874555262 | 5.249857791053826 | 2.66301264016213 | 11.240387837861851 |
| user_005 | Jogging | 1.446046541412e12 | 6.59169 | -19.58108 | -15.52033 | -1.9885736363636364 | -16.68963909090909 | -0.8122936363636359 | 43.450377056099995 | 383.4186939664 | 240.88064330889998 | 25.840853591385095 | 21.518287655835156 | 8.580263636363636 | 2.8914409090909103 | 14.708036363636364 | 5.809802975206612 | 4.999057190082644 | 9.224111239669421 | 73.62092406950413 | 8.36043053076447 | 216.32633367404958 | 39.01585487781022 | 30.637290880641924 | 151.2366587860819 | 6.246267275566281 | 5.535096284676711 | 12.297831466810802 |
| user_005 | Jogging | 1.446046541517e12 | -9.6178 | -1.57053 | -5.4804 | -3.333268181818182 | -1.7725854545454547 | -7.323260909090909 | 92.50207684000002 | 2.4665644809 | 30.034784160000005 | 11.180493078612411 | 16.414639162431733 | 6.284531818181819 | 0.2020554545454547 | 1.8428609090909083 | 12.730577107438016 | 7.694465123966942 | 2.576645785123967 | 39.49534017373968 | 4.082640671157031e-2 | 3.396136330255369 | 192.85396690384394 | 111.32270980228442 | 8.473371684080687 | 13.887187148729721 | 10.550957767060032 | 2.9109056467155865 |
| user_005 | Jogging | 1.446046541526e12 | -5.24928 | -1.37893 | -9.619 | -4.914851818181818 | -0.8563818181818182 | -7.8109745454545445 | 27.5549405184 | 1.9014479449 | 92.525161 | 11.044525769054097 | 15.8521414573526 | 0.3344281818181818 | 0.5225481818181817 | 1.8080254545454553 | 11.748183140495867 | 7.11649305785124 | 2.5883634710743793 | 0.11184220879421486 | 0.27305660232148754 | 3.2689560442843 | 181.58080947832903 | 107.04410449639293 | 8.514233087209762 | 13.475192372590792 | 10.34621208444873 | 2.9179158807631453 |
| user_005 | Jogging | 1.446046541542e12 | 0.307161 | -2.68182 | -3.87095 | -7.788874454545455 | -1.243068181818182 | -8.194178181818181 | 9.434787992100001e-2 | 7.192158512400001 | 14.9842539025 | 4.719190639804775 | 14.113925983821156 | 8.096035454545456 | 1.4387518181818182 | 4.32322818181818 | 10.823112314049588 | 4.726066363636364 | 2.2985858677685944 | 65.54579008125705 | 2.0700067943214875 | 18.69030191206693 | 164.5818232479703 | 64.88748743124589 | 7.553243462068892 | 12.828944744131151 | 8.055276992831836 | 2.74831647778579 |
| user_005 | Jogging | 1.446046541567e12 | -3.67815 | -1.53221 | -1.64837 | -7.945640363636364 | -2.6504690909090916 | -5.546593272727272 | 13.5287874225 | 2.3476674841 | 2.7171236568999997 | 4.312027198835833 | 10.93577854426604 | 4.267490363636364 | 1.1182590909090915 | 3.8982232727272725 | 10.032953694214877 | 1.1673442975206612 | 3.1954715867768595 | 18.211474003729226 | 1.2505033944008277 | 15.196144684032527 | 144.1504480278062 | 1.9028610728867017 | 14.167965256505674 | 12.006267031338517 | 1.3794423050228313 | 3.7640357671660976 |
| user_005 | Jogging | 1.44604654159e12 | -7.3569 | -7.81675 | 3.94639 | -4.799891272727273 | -2.9047763636363633 | -2.5401904545454546 | 54.123977610000004 | 61.1015805625 | 15.5739940321 | 11.436763187397036 | 7.609283436074653 | 2.557008727272727 | 4.911973636363637 | 6.486580454545455 | 5.092799677685949 | 1.3836494214876036 | 4.091723454545454 | 6.538293631348892 | 24.12748500433141 | 42.075725993291115 | 37.86155491294937 | 3.7278022676234417 | 20.641978826787177 | 6.153174376933371 | 1.9307517364030626 | 4.543344453900362 |
| user_005 | Jogging | 1.446046541647e12 | 12.68462 | -19.58108 | -8.23947 | -3.9045899999999993 | -12.63116909090909 | 1.8352919090909092 | 160.89958454440003 | 383.4186939664 | 67.88886588090001 | 24.742820057376242 | 15.833626551545684 | 16.58921 | 6.94991090909091 | 10.07476190909091 | 5.423432454545455 | 6.832734214876033 | 5.923814867768595 | 275.20188842410005 | 48.301261644300844 | 101.50082752486911 | 49.805850736926025 | 62.230873282694525 | 41.61672815250327 | 7.057326033061391 | 7.888654719449605 | 6.451102863270998 |
| user_005 | Jogging | 1.446046541704e12 | -0.804128 | -19.58108 | -2.03158 | 8.35791109090909 | -19.581079999999996 | -0.5022472727272728 | 0.646621840384 | 383.4186939664 | 4.127317296399999 | 19.70260472889775 | 23.37056805963689 | 9.16203909090909 | 3.552713678800501e-15 | 1.529332727272727 | 10.50261695041322 | 4.642774710743804 | 4.055621512396694 | 83.94296030334627 | 1.2621774483536189e-29 | 2.3388585907074373 | 146.0293819989988 | 37.699085315443725 | 26.298503606760573 | 12.084261748199548 | 6.1399580874338 | 5.128206665761489 |
| user_005 | Jogging | 1.446046541777e12 | 2.4531 | 19.54396 | 15.94065 | -1.5252460909090912 | -3.827948181818181 | 6.688037636363637 | 6.01769961 | 381.96637248159993 | 254.1043224225 | 25.33946318519988 | 18.518431558431583 | 3.978346090909091 | 23.371908181818178 | 9.252612363636363 | 7.945605561983471 | 11.386833388429755 | 5.833873041322314 | 15.827237619051646 | 546.2460920593395 | 85.61083555171648 | 88.88585335568426 | 235.8254541440598 | 43.61219456277871 | 9.42792943098771 | 15.356609461207894 | 6.603952949770214 |
- Prediction using Random Forests
Instead of just stopping at ETL and feature engineering let's quickly see without understanding the random forest model in detail how simple it is to predict the activity using random forest model.
val splits = dataFeatDF.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))
splits: Array[org.apache.spark.sql.Dataset[org.apache.spark.sql.Row]] = Array([user_id: string, activity: string ... 27 more fields], [user_id: string, activity: string ... 27 more fields])
trainingData: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [user_id: string, activity: string ... 27 more fields]
testData: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [user_id: string, activity: string ... 27 more fields]
See
import org.apache.spark.ml.feature.{StringIndexer,VectorAssembler}
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.RandomForestClassifier
val transformers = Array(
new StringIndexer().setInputCol("activity").setOutputCol("label"),
new VectorAssembler()
.setInputCols(Array("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ"))
.setOutputCol("features")
)
// Train a RandomForest model.
val rf = new RandomForestClassifier()
.setLabelCol("label")
.setFeaturesCol("features")
.setNumTrees(10)
.setFeatureSubsetStrategy("auto")
.setImpurity("gini")
.setMaxDepth(20)
.setMaxBins(32)
.setSeed(12345)
val model = new Pipeline().setStages(transformers :+ rf).fit(trainingData)
import org.apache.spark.ml.feature.{StringIndexer, VectorAssembler}
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.RandomForestClassifier
transformers: Array[org.apache.spark.ml.PipelineStage with org.apache.spark.ml.param.shared.HasOutputCol with org.apache.spark.ml.param.shared.HasInputCols with org.apache.spark.ml.param.shared.HasHandleInvalid with org.apache.spark.ml.util.DefaultParamsWritable{def copy(extra: org.apache.spark.ml.param.ParamMap): org.apache.spark.ml.PipelineStage with org.apache.spark.ml.param.shared.HasOutputCol with org.apache.spark.ml.param.shared.HasInputCols with org.apache.spark.ml.param.shared.HasHandleInvalid with org.apache.spark.ml.util.DefaultParamsWritable{def copy(extra: org.apache.spark.ml.param.ParamMap): org.apache.spark.ml.PipelineStage with org.apache.spark.ml.param.shared.HasOutputCol with org.apache.spark.ml.param.shared.HasInputCols with org.apache.spark.ml.param.shared.HasHandleInvalid with org.apache.spark.ml.util.DefaultParamsWritable}}] = Array(strIdx_84996728c97a, VectorAssembler: uid=vecAssembler_e11ceeee69a9, handleInvalid=error, numInputCols=6)
rf: org.apache.spark.ml.classification.RandomForestClassifier = rfc_6ab6b8d21734
model: org.apache.spark.ml.PipelineModel = pipeline_dd3e9eb4d47c
Before we go further let's find quickly which label is mapped to which activity by the StringIndexer and quickly check that the estimator we have built can transform the union of the training and test data. This is mostly for debugging... not something you would do in a real pipeline except during debugging stages.
val activityLabelDF = model.transform(trainingData.union(testData)).select("activity","label").distinct
display(activityLabelDF) // this just shows what activity is associated with what label
| activity | label |
|---|---|
| Jogging | 0.0 |
| Jumping | 4.0 |
| Standing | 1.0 |
| Walking | 2.0 |
| Sitting | 3.0 |
val myPredictionsOnTestData = model.transform(testData)
display(myPredictionsOnTestData)
Let's evaluate accuracy at a lower level...
val accuracy: Double = 1.0 * model.transform(testData)
.select("activity","label","prediction")
.filter($"label"===$"prediction").count() / testData.count()
accuracy: Double = 0.9828725226327379
We get 98% correct predictions and here are the mis-predicted ones.
display(model.transform(testData).select("activity","label","prediction").filter(not($"label"===$"prediction")))
| activity | label | prediction |
|---|---|---|
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Walking | 2.0 | 1.0 |
| Walking | 2.0 | 1.0 |
| Walking | 2.0 | 1.0 |
| Jogging | 0.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Standing | 1.0 | 2.0 |
| Jumping | 4.0 | 2.0 |
| Walking | 2.0 | 1.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jumping | 4.0 | 2.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Jogging | 0.0 | 2.0 |
| Jogging | 0.0 | 2.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 2.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jogging | 0.0 | 4.0 |
| Jumping | 4.0 | 2.0 |
| Jumping | 4.0 | 0.0 |
| Jumping | 4.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 1.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
| Walking | 2.0 | 0.0 |
testData.printSchema()
root
|-- user_id: string (nullable = true)
|-- activity: string (nullable = true)
|-- timeStampAsLong: long (nullable = true)
|-- x: double (nullable = true)
|-- y: double (nullable = true)
|-- z: double (nullable = true)
|-- meanX: double (nullable = true)
|-- meanY: double (nullable = true)
|-- meanZ: double (nullable = true)
|-- SqX: double (nullable = true)
|-- SqY: double (nullable = true)
|-- SqZ: double (nullable = true)
|-- resultant: double (nullable = true)
|-- meanResultant: double (nullable = true)
|-- absDevFromMeanX: double (nullable = true)
|-- absDevFromMeanY: double (nullable = true)
|-- absDevFromMeanZ: double (nullable = true)
|-- meanAbsDevFromMeanX: double (nullable = true)
|-- meanAbsDevFromMeanY: double (nullable = true)
|-- meanAbsDevFromMeanZ: double (nullable = true)
|-- sqrDevFromMeanX: double (nullable = true)
|-- sqrDevFromMeanY: double (nullable = true)
|-- sqrDevFromMeanZ: double (nullable = true)
|-- varianceX: double (nullable = true)
|-- varianceY: double (nullable = true)
|-- varianceZ: double (nullable = true)
|-- stddevX: double (nullable = true)
|-- stddevY: double (nullable = true)
|-- stddevZ: double (nullable = true)
Let's understand the mis-predicitons better by seeing the user_id also.
display(model.transform(testData).select("user_id","activity","label","prediction").filter(not($"label"===$"prediction")))
| user_id | activity | label | prediction |
|---|---|---|---|
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jogging | 0.0 | 4.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Jumping | 4.0 | 0.0 |
| user_001 | Walking | 2.0 | 1.0 |
| user_001 | Walking | 2.0 | 1.0 |
| user_001 | Walking | 2.0 | 1.0 |
| user_002 | Jogging | 0.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_002 | Standing | 1.0 | 2.0 |
| user_003 | Jumping | 4.0 | 2.0 |
| user_003 | Walking | 2.0 | 1.0 |
| user_004 | Jogging | 0.0 | 4.0 |
| user_004 | Jogging | 0.0 | 4.0 |
| user_004 | Jumping | 4.0 | 2.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Jumping | 4.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_004 | Walking | 2.0 | 0.0 |
| user_005 | Jogging | 0.0 | 2.0 |
| user_005 | Jogging | 0.0 | 2.0 |
| user_005 | Jogging | 0.0 | 4.0 |
| user_005 | Jogging | 0.0 | 4.0 |
| user_005 | Jogging | 0.0 | 2.0 |
| user_005 | Jogging | 0.0 | 4.0 |
| user_005 | Jogging | 0.0 | 4.0 |
| user_005 | Jogging | 0.0 | 4.0 |
| user_005 | Jumping | 4.0 | 2.0 |
| user_005 | Jumping | 4.0 | 0.0 |
| user_005 | Jumping | 4.0 | 0.0 |
| user_005 | Walking | 2.0 | 0.0 |
| user_006 | Walking | 2.0 | 1.0 |
| user_006 | Walking | 2.0 | 0.0 |
| user_006 | Walking | 2.0 | 0.0 |
| user_006 | Walking | 2.0 | 0.0 |
| user_006 | Walking | 2.0 | 0.0 |
| user_006 | Walking | 2.0 | 0.0 |
Let's evaluate our model's accuracy using ML pipeline's evaluation.MulticlassClassificationEvaluator:
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
// Select (prediction, true label) and compute test error.
val evaluator = new MulticlassClassificationEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
.setMetricName("accuracy")
val accuracy = evaluator.evaluate(myPredictionsOnTestData)
println(s"Test Error = ${(1.0 - accuracy)}")
Test Error = 0.017127477367262056
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
evaluator: org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator = MulticlassClassificationEvaluator: uid=mcEval_0923535d6e49, metricName=accuracy, metricLabel=0.0, beta=1.0, eps=1.0E-15
accuracy: Double = 0.9828725226327379
Note, it's the same 98% we got from our slightly lower-level operations earlier for accuracy.
Think!
What else do you need to investigate to try to understand the mis-predicitons better?
- time-line?
- are mis-predictions happening during transitions between users or activities of the same user or both, as our Markov process simply uses the same sliding window over the time-odered, user-grouped, activity streams...
- ...
Typically, in inustry you will have to improve on an existing algorithm that is in production and getting into the details under the given constraints needs some thinking from scratch over the entire data science pipeline and process.
Explaining the Model's Predictions to a Curious Client whose Activity is being Predicted
Now, let's try to explain the decision branches in each tree of our best-fitted random forest model. Often, being able to "explain" why the model "predicts" is critical for various businesses. how it predicts.
import org.apache.spark.ml.classification.RandomForestClassificationModel
val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
println(s"Learned classification forest model:\n ${rfModel.toDebugString}")
Learned classification forest model:
RandomForestClassificationModel: uid=rfc_6ab6b8d21734, numTrees=10, numClasses=5, numFeatures=6
Tree 0 (weight 1.0):
If (feature 3 <= 0.45832723868728054)
If (feature 1 <= -8.097179727272728)
If (feature 0 <= -7.121753363636364)
Predict: 2.0
Else (feature 0 > -7.121753363636364)
If (feature 5 <= 0.7848413904200169)
If (feature 1 <= -8.4067287012987)
If (feature 1 <= -11.596521363636366)
Predict: 2.0
Else (feature 1 > -11.596521363636366)
If (feature 1 <= -9.49238863636364)
If (feature 0 <= -1.976380909090909)
Predict: 2.0
Else (feature 0 > -1.976380909090909)
Predict: 1.0
Else (feature 1 > -9.49238863636364)
Predict: 1.0
Else (feature 1 > -8.4067287012987)
If (feature 0 <= -3.502226363636364)
Predict: 2.0
Else (feature 0 > -3.502226363636364)
Predict: 1.0
Else (feature 5 > 0.7848413904200169)
Predict: 2.0
Else (feature 1 > -8.097179727272728)
If (feature 2 <= 2.943098181818182)
If (feature 4 <= 0.904496986362019)
If (feature 3 <= 0.027620762421071275)
If (feature 0 <= -3.502226363636364)
Predict: 0.0
Else (feature 0 > -3.502226363636364)
If (feature 2 <= -0.045886318181818195)
If (feature 1 <= -6.820416818181818)
Predict: 4.0
Else (feature 1 > -6.820416818181818)
Predict: 0.0
Else (feature 2 > -0.045886318181818195)
Predict: 4.0
Else (feature 3 > 0.027620762421071275)
Predict: 4.0
Else (feature 4 > 0.904496986362019)
Predict: 2.0
Else (feature 2 > 2.943098181818182)
If (feature 3 <= 0.2198889879412786)
If (feature 0 <= -1.105464818181818)
Predict: 2.0
Else (feature 0 > -1.105464818181818)
If (feature 3 <= 0.12199305012399356)
Predict: 3.0
Else (feature 3 > 0.12199305012399356)
If (feature 1 <= -0.6543302727272726)
Predict: 3.0
Else (feature 1 > -0.6543302727272726)
Predict: 2.0
Else (feature 3 > 0.2198889879412786)
If (feature 0 <= -0.1735843181818182)
Predict: 2.0
Else (feature 0 > -0.1735843181818182)
Predict: 3.0
Else (feature 3 > 0.45832723868728054)
If (feature 4 <= 2.2658035906051546)
If (feature 5 <= 2.8353340431585474)
If (feature 5 <= 0.7848413904200169)
If (feature 1 <= -8.097179727272728)
If (feature 3 <= 1.5843984037196344)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 0 <= 2.8563333333333336)
If (feature 0 <= -4.357466363636364)
If (feature 1 <= -8.4067287012987)
If (feature 2 <= -1.0073777272727273)
Predict: 1.0
Else (feature 2 > -1.0073777272727273)
Predict: 2.0
Else (feature 1 > -8.4067287012987)
Predict: 1.0
Else (feature 0 > -4.357466363636364)
If (feature 0 <= -2.5093826818181824)
If (feature 2 <= -0.5126976818181816)
Predict: 1.0
Else (feature 2 > -0.5126976818181816)
Predict: 2.0
Else (feature 0 > -2.5093826818181824)
Predict: 1.0
Else (feature 0 > 2.8563333333333336)
If (feature 4 <= 0.904496986362019)
Predict: 1.0
Else (feature 4 > 0.904496986362019)
Predict: 2.0
Else (feature 3 > 1.5843984037196344)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
Predict: 2.0
Else (feature 1 > -8.097179727272728)
If (feature 3 <= 2.037322122633077)
If (feature 1 <= -6.820416818181818)
If (feature 4 <= 0.904496986362019)
Predict: 4.0
Else (feature 4 > 0.904496986362019)
Predict: 2.0
Else (feature 1 > -6.820416818181818)
Predict: 2.0
Else (feature 3 > 2.037322122633077)
Predict: 0.0
Else (feature 5 > 0.7848413904200169)
If (feature 3 <= 3.2305826019123103)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 1 <= -17.407273636363634)
Predict: 0.0
Else (feature 1 > -17.407273636363634)
If (feature 1 <= -2.2306882272727275)
If (feature 0 <= -0.5846568636363636)
If (feature 4 <= 0.904496986362019)
If (feature 2 <= 0.41744104545454547)
If (feature 5 <= 1.6130554927490437)
Predict: 2.0
Else (feature 5 > 1.6130554927490437)
If (feature 3 <= 1.7795641834853977)
Predict: 2.0
Else (feature 3 > 1.7795641834853977)
If (feature 1 <= -9.105701681818182)
If (feature 5 <= 1.7614806708061252)
Predict: 2.0
Else (feature 5 > 1.7614806708061252)
If (feature 5 <= 1.9145738226248412)
If (feature 3 <= 2.283530423945612)
Predict: 2.0
Else (feature 3 > 2.283530423945612)
Predict: 1.0
Else (feature 5 > 1.9145738226248412)
Predict: 2.0
Else (feature 1 > -9.105701681818182)
If (feature 0 <= -3.065027181818182)
If (feature 0 <= -4.988009090909092)
Predict: 2.0
Else (feature 0 > -4.988009090909092)
If (feature 0 <= -3.502226363636364)
Predict: 1.0
Else (feature 0 > -3.502226363636364)
Predict: 2.0
Else (feature 0 > -3.065027181818182)
Predict: 1.0
Else (feature 2 > 0.41744104545454547)
Predict: 2.0
Else (feature 4 > 0.904496986362019)
Predict: 2.0
Else (feature 0 > -0.5846568636363636)
If (feature 3 <= 1.0884225728208126)
If (feature 2 <= -0.045886318181818195)
Predict: 2.0
Else (feature 2 > -0.045886318181818195)
Predict: 1.0
Else (feature 3 > 1.0884225728208126)
If (feature 4 <= 0.904496986362019)
If (feature 1 <= -9.49238863636364)
Predict: 2.0
Else (feature 1 > -9.49238863636364)
If (feature 3 <= 1.7795641834853977)
If (feature 0 <= 0.32806340909090914)
Predict: 2.0
Else (feature 0 > 0.32806340909090914)
Predict: 1.0
Else (feature 3 > 1.7795641834853977)
Predict: 1.0
Else (feature 4 > 0.904496986362019)
Predict: 2.0
Else (feature 1 > -2.2306882272727275)
If (feature 2 <= 1.1333349545454543)
If (feature 0 <= 0.32806340909090914)
If (feature 1 <= 1.425418909090909)
Predict: 0.0
Else (feature 1 > 1.425418909090909)
Predict: 4.0
Else (feature 0 > 0.32806340909090914)
Predict: 0.0
Else (feature 2 > 1.1333349545454543)
Predict: 2.0
Else (feature 3 > 3.2305826019123103)
If (feature 2 <= -0.9551237727272728)
If (feature 5 <= 1.4557098485258626)
If (feature 5 <= 1.2559600735949519)
Predict: 0.0
Else (feature 5 > 1.2559600735949519)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
Predict: 1.0
Else (feature 5 > 1.4557098485258626)
If (feature 3 <= 4.092746328718364)
If (feature 4 <= 1.9510708064802356)
If (feature 1 <= -11.596521363636366)
Predict: 0.0
Else (feature 1 > -11.596521363636366)
If (feature 4 <= 1.278422893229283)
Predict: 2.0
Else (feature 4 > 1.278422893229283)
Predict: 0.0
Else (feature 4 > 1.9510708064802356)
Predict: 2.0
Else (feature 3 > 4.092746328718364)
Predict: 0.0
Else (feature 2 > -0.9551237727272728)
If (feature 0 <= 2.8563333333333336)
If (feature 4 <= 0.904496986362019)
If (feature 3 <= 3.5937950153207483)
Predict: 1.0
Else (feature 3 > 3.5937950153207483)
Predict: 0.0
Else (feature 4 > 0.904496986362019)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 5 <= 2.600144428803959)
Predict: 2.0
Else (feature 5 > 2.600144428803959)
If (feature 4 <= 1.9510708064802356)
Predict: 0.0
Else (feature 4 > 1.9510708064802356)
Predict: 2.0
Else (feature 0 > 2.8563333333333336)
If (feature 3 <= 3.5937950153207483)
If (feature 0 <= 3.5330349999999995)
Predict: 2.0
Else (feature 0 > 3.5330349999999995)
Predict: 0.0
Else (feature 3 > 3.5937950153207483)
Predict: 0.0
Else (feature 5 > 2.8353340431585474)
If (feature 3 <= 1.0884225728208126)
If (feature 0 <= -1.976380909090909)
Predict: 0.0
Else (feature 0 > -1.976380909090909)
Predict: 3.0
Else (feature 3 > 1.0884225728208126)
If (feature 4 <= 1.9510708064802356)
If (feature 3 <= 2.283530423945612)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
Predict: 2.0
Else (feature 3 > 2.283530423945612)
Predict: 0.0
Else (feature 4 > 1.9510708064802356)
If (feature 1 <= -10.088093363636364)
If (feature 1 <= -17.407273636363634)
Predict: 0.0
Else (feature 1 > -17.407273636363634)
Predict: 2.0
Else (feature 1 > -10.088093363636364)
Predict: 0.0
Else (feature 4 > 2.2658035906051546)
If (feature 3 <= 3.2305826019123103)
If (feature 4 <= 4.527012376192168)
If (feature 0 <= 3.5330349999999995)
If (feature 0 <= -1.2744209090909089)
If (feature 2 <= -1.8347482272727271)
If (feature 5 <= 1.4557098485258626)
If (feature 3 <= 1.5843984037196344)
Predict: 4.0
Else (feature 3 > 1.5843984037196344)
Predict: 0.0
Else (feature 5 > 1.4557098485258626)
If (feature 0 <= -4.357466363636364)
If (feature 0 <= -6.212517272727274)
Predict: 0.0
Else (feature 0 > -6.212517272727274)
If (feature 5 <= 1.9145738226248412)
If (feature 0 <= -4.988009090909092)
If (feature 2 <= -2.198210545454545)
Predict: 0.0
Else (feature 2 > -2.198210545454545)
Predict: 2.0
Else (feature 0 > -4.988009090909092)
Predict: 2.0
Else (feature 5 > 1.9145738226248412)
Predict: 2.0
Else (feature 0 > -4.357466363636364)
If (feature 1 <= -9.356527272727273)
If (feature 4 <= 4.0067789684062625)
If (feature 5 <= 2.0408593821519982)
Predict: 0.0
Else (feature 5 > 2.0408593821519982)
Predict: 2.0
Else (feature 4 > 4.0067789684062625)
Predict: 4.0
Else (feature 1 > -9.356527272727273)
Predict: 0.0
Else (feature 2 > -1.8347482272727271)
If (feature 1 <= -3.8209812727272725)
If (feature 1 <= -11.596521363636366)
Predict: 0.0
Else (feature 1 > -11.596521363636366)
If (feature 5 <= 0.16873060661442005)
Predict: 1.0
Else (feature 5 > 0.16873060661442005)
If (feature 5 <= 3.469288509552827)
Predict: 2.0
Else (feature 5 > 3.469288509552827)
If (feature 4 <= 3.5166543239603483)
Predict: 2.0
Else (feature 4 > 3.5166543239603483)
Predict: 0.0
Else (feature 1 > -3.8209812727272725)
If (feature 4 <= 3.5166543239603483)
If (feature 2 <= 0.6682646818181818)
Predict: 0.0
Else (feature 2 > 0.6682646818181818)
Predict: 2.0
Else (feature 4 > 3.5166543239603483)
Predict: 0.0
Else (feature 0 > -1.2744209090909089)
If (feature 2 <= 0.6682646818181818)
If (feature 1 <= -3.8209812727272725)
If (feature 5 <= 4.913251092952768)
Predict: 2.0
Else (feature 5 > 4.913251092952768)
Predict: 0.0
Else (feature 1 > -3.8209812727272725)
If (feature 3 <= 2.696622839121874)
Predict: 0.0
Else (feature 3 > 2.696622839121874)
If (feature 0 <= -0.1735843181818182)
Predict: 0.0
Else (feature 0 > -0.1735843181818182)
If (feature 5 <= 0.7848413904200169)
Predict: 4.0
Else (feature 5 > 0.7848413904200169)
Predict: 0.0
Else (feature 2 > 0.6682646818181818)
If (feature 1 <= -8.948937727272728)
If (feature 0 <= 0.32806340909090914)
Predict: 0.0
Else (feature 0 > 0.32806340909090914)
Predict: 2.0
Else (feature 1 > -8.948937727272728)
Predict: 0.0
Else (feature 0 > 3.5330349999999995)
If (feature 0 <= 4.459689318181818)
If (feature 1 <= -13.810390454545457)
Predict: 0.0
Else (feature 1 > -13.810390454545457)
If (feature 4 <= 2.891537202575433)
Predict: 2.0
Else (feature 4 > 2.891537202575433)
Predict: 0.0
Else (feature 0 > 4.459689318181818)
Predict: 0.0
Else (feature 4 > 4.527012376192168)
If (feature 4 <= 8.132821942578682)
If (feature 0 <= -3.502226363636364)
If (feature 1 <= -11.596521363636366)
If (feature 2 <= -3.569613363636363)
Predict: 4.0
Else (feature 2 > -3.569613363636363)
If (feature 3 <= 2.9289256343028125)
Predict: 2.0
Else (feature 3 > 2.9289256343028125)
Predict: 0.0
Else (feature 1 > -11.596521363636366)
If (feature 2 <= 0.6682646818181818)
Predict: 0.0
Else (feature 2 > 0.6682646818181818)
If (feature 4 <= 6.684054463098245)
Predict: 0.0
Else (feature 4 > 6.684054463098245)
Predict: 2.0
Else (feature 0 > -3.502226363636364)
If (feature 1 <= -3.8209812727272725)
If (feature 0 <= 3.5330349999999995)
If (feature 5 <= 3.1250203637838814)
If (feature 2 <= -5.720776181818183)
Predict: 2.0
Else (feature 2 > -5.720776181818183)
If (feature 2 <= 1.1333349545454543)
If (feature 1 <= -5.109937500000001)
If (feature 3 <= 1.7795641834853977)
Predict: 4.0
Else (feature 3 > 1.7795641834853977)
If (feature 0 <= 0.9882181363636364)
If (feature 0 <= 0.5266320454545453)
If (feature 2 <= -1.2442670454545455)
If (feature 4 <= 5.398982323543355)
Predict: 0.0
Else (feature 4 > 5.398982323543355)
If (feature 5 <= 2.8353340431585474)
Predict: 4.0
Else (feature 5 > 2.8353340431585474)
If (feature 0 <= -1.4538298181818181)
If (feature 1 <= -9.49238863636364)
Predict: 0.0
Else (feature 1 > -9.49238863636364)
Predict: 4.0
Else (feature 0 > -1.4538298181818181)
Predict: 4.0
Else (feature 2 > -1.2442670454545455)
Predict: 0.0
Else (feature 0 > 0.5266320454545453)
Predict: 0.0
Else (feature 0 > 0.9882181363636364)
If (feature 4 <= 6.291886217849433)
Predict: 0.0
Else (feature 4 > 6.291886217849433)
If (feature 1 <= -5.750931181818182)
Predict: 4.0
Else (feature 1 > -5.750931181818182)
If (feature 3 <= 2.283530423945612)
Predict: 0.0
Else (feature 3 > 2.283530423945612)
If (feature 3 <= 2.696622839121874)
If (feature 3 <= 2.4887269112897967)
Predict: 4.0
Else (feature 3 > 2.4887269112897967)
Predict: 0.0
Else (feature 3 > 2.696622839121874)
Predict: 4.0
Else (feature 1 > -5.109937500000001)
If (feature 4 <= 5.019055660088064)
Predict: 0.0
Else (feature 4 > 5.019055660088064)
If (feature 0 <= -3.065027181818182)
Predict: 2.0
Else (feature 0 > -3.065027181818182)
If (feature 4 <= 7.3665835405207885)
Predict: 4.0
Else (feature 4 > 7.3665835405207885)
Predict: 0.0
Else (feature 2 > 1.1333349545454543)
If (feature 3 <= 1.355530712657193)
Predict: 2.0
Else (feature 3 > 1.355530712657193)
If (feature 5 <= 1.6130554927490437)
Predict: 0.0
Else (feature 5 > 1.6130554927490437)
If (feature 0 <= -0.3791205454545455)
If (feature 4 <= 5.398982323543355)
Predict: 0.0
Else (feature 4 > 5.398982323543355)
Predict: 2.0
Else (feature 0 > -0.3791205454545455)
Predict: 4.0
Else (feature 5 > 3.1250203637838814)
If (feature 2 <= -1.8347482272727271)
If (feature 1 <= -8.4067287012987)
If (feature 3 <= 2.283530423945612)
If (feature 0 <= 0.45695872727272724)
If (feature 3 <= 2.037322122633077)
If (feature 4 <= 5.019055660088064)
Predict: 4.0
Else (feature 4 > 5.019055660088064)
Predict: 0.0
Else (feature 3 > 2.037322122633077)
Predict: 2.0
Else (feature 0 > 0.45695872727272724)
Predict: 4.0
Else (feature 3 > 2.283530423945612)
Predict: 0.0
Else (feature 1 > -8.4067287012987)
If (feature 5 <= 8.699793726565169)
If (feature 1 <= -6.820416818181818)
If (feature 4 <= 7.033163268276285)
Predict: 4.0
Else (feature 4 > 7.033163268276285)
If (feature 3 <= 2.4887269112897967)
Predict: 0.0
Else (feature 3 > 2.4887269112897967)
Predict: 4.0
Else (feature 1 > -6.820416818181818)
Predict: 4.0
Else (feature 5 > 8.699793726565169)
Predict: 0.0
Else (feature 2 > -1.8347482272727271)
If (feature 5 <= 3.8343025836297375)
If (feature 1 <= -8.097179727272728)
Predict: 2.0
Else (feature 1 > -8.097179727272728)
If (feature 3 <= 2.9289256343028125)
If (feature 3 <= 2.4887269112897967)
If (feature 0 <= -3.065027181818182)
Predict: 2.0
Else (feature 0 > -3.065027181818182)
Predict: 0.0
Else (feature 3 > 2.4887269112897967)
Predict: 0.0
Else (feature 3 > 2.9289256343028125)
Predict: 4.0
Else (feature 5 > 3.8343025836297375)
If (feature 2 <= -0.9551237727272728)
If (feature 5 <= 6.108758959317355)
Predict: 4.0
Else (feature 5 > 6.108758959317355)
Predict: 0.0
Else (feature 2 > -0.9551237727272728)
Predict: 0.0
Else (feature 0 > 3.5330349999999995)
If (feature 4 <= 7.72195028414196)
Predict: 0.0
Else (feature 4 > 7.72195028414196)
If (feature 1 <= -8.712045)
Predict: 0.0
Else (feature 1 > -8.712045)
Predict: 4.0
Else (feature 1 > -3.8209812727272725)
If (feature 0 <= -3.065027181818182)
Predict: 2.0
Else (feature 0 > -3.065027181818182)
If (feature 1 <= -2.2306882272727275)
If (feature 3 <= 2.037322122633077)
If (feature 5 <= 1.9145738226248412)
Predict: 4.0
Else (feature 5 > 1.9145738226248412)
Predict: 0.0
Else (feature 3 > 2.037322122633077)
Predict: 0.0
Else (feature 1 > -2.2306882272727275)
Predict: 0.0
Else (feature 4 > 8.132821942578682)
If (feature 4 <= 8.97158739539847)
If (feature 2 <= -1.2442670454545455)
If (feature 1 <= -3.8209812727272725)
If (feature 2 <= -5.720776181818183)
Predict: 0.0
Else (feature 2 > -5.720776181818183)
If (feature 0 <= 0.43257299999999993)
Predict: 4.0
Else (feature 0 > 0.43257299999999993)
If (feature 2 <= -2.810174181818182)
Predict: 4.0
Else (feature 2 > -2.810174181818182)
Predict: 0.0
Else (feature 1 > -3.8209812727272725)
Predict: 0.0
Else (feature 2 > -1.2442670454545455)
If (feature 5 <= 1.9145738226248412)
If (feature 2 <= 0.1387476818181818)
Predict: 0.0
Else (feature 2 > 0.1387476818181818)
If (feature 0 <= -0.7518725909090909)
Predict: 0.0
Else (feature 0 > -0.7518725909090909)
Predict: 4.0
Else (feature 5 > 1.9145738226248412)
Predict: 0.0
Else (feature 4 > 8.97158739539847)
Predict: 0.0
Else (feature 3 > 3.2305826019123103)
If (feature 1 <= -10.088093363636364)
If (feature 3 <= 5.42131601555171)
If (feature 0 <= 2.0089040454545453)
If (feature 5 <= 4.262646949929151)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 4 <= 4.527012376192168)
If (feature 0 <= -0.7518725909090909)
If (feature 5 <= 1.2559600735949519)
Predict: 0.0
Else (feature 5 > 1.2559600735949519)
Predict: 2.0
Else (feature 0 > -0.7518725909090909)
Predict: 0.0
Else (feature 4 > 4.527012376192168)
If (feature 4 <= 6.684054463098245)
Predict: 0.0
Else (feature 4 > 6.684054463098245)
If (feature 0 <= 0.32806340909090914)
If (feature 4 <= 7.3665835405207885)
If (feature 3 <= 4.596493775946534)
Predict: 2.0
Else (feature 3 > 4.596493775946534)
Predict: 0.0
Else (feature 4 > 7.3665835405207885)
Predict: 0.0
Else (feature 0 > 0.32806340909090914)
Predict: 2.0
Else (feature 5 > 4.262646949929151)
Predict: 0.0
Else (feature 0 > 2.0089040454545453)
Predict: 0.0
Else (feature 3 > 5.42131601555171)
Predict: 0.0
Else (feature 1 > -10.088093363636364)
If (feature 5 <= 1.0680362943269028)
If (feature 4 <= 5.830630767197693)
*** WARNING: skipped 325736 bytes of output ***
Predict: 2.0
Else (feature 4 > 5.398982323543355)
If (feature 0 <= -2.5093826818181824)
Predict: 4.0
Else (feature 0 > -2.5093826818181824)
Predict: 2.0
Else (feature 1 > -7.698300909090908)
If (feature 2 <= -1.073569090909091)
If (feature 3 <= 2.696622839121874)
If (feature 1 <= -7.295937636363637)
If (feature 3 <= 1.7795641834853977)
Predict: 4.0
Else (feature 3 > 1.7795641834853977)
Predict: 0.0
Else (feature 1 > -7.295937636363637)
If (feature 4 <= 4.527012376192168)
Predict: 2.0
Else (feature 4 > 4.527012376192168)
Predict: 0.0
Else (feature 3 > 2.696622839121874)
Predict: 0.0
Else (feature 2 > -1.073569090909091)
Predict: 2.0
Else (feature 1 > -5.750931181818182)
If (feature 3 <= 5.42131601555171)
If (feature 2 <= 6.177680772727272)
If (feature 1 <= -2.2306882272727275)
If (feature 3 <= 1.7795641834853977)
Predict: 4.0
Else (feature 3 > 1.7795641834853977)
If (feature 5 <= 1.2559600735949519)
If (feature 4 <= 2.891537202575433)
Predict: 2.0
Else (feature 4 > 2.891537202575433)
Predict: 0.0
Else (feature 5 > 1.2559600735949519)
If (feature 0 <= 2.0089040454545453)
Predict: 0.0
Else (feature 0 > 2.0089040454545453)
If (feature 4 <= 2.891537202575433)
Predict: 0.0
Else (feature 4 > 2.891537202575433)
Predict: 4.0
Else (feature 1 > -2.2306882272727275)
If (feature 4 <= 3.5166543239603483)
If (feature 0 <= 0.9882181363636364)
Predict: 0.0
Else (feature 0 > 0.9882181363636364)
If (feature 5 <= 0.7848413904200169)
Predict: 4.0
Else (feature 5 > 0.7848413904200169)
Predict: 0.0
Else (feature 4 > 3.5166543239603483)
Predict: 0.0
Else (feature 2 > 6.177680772727272)
Predict: 2.0
Else (feature 3 > 5.42131601555171)
If (feature 4 <= 5.019055660088064)
Predict: 0.0
Else (feature 4 > 5.019055660088064)
If (feature 3 <= 7.291873594931593)
If (feature 1 <= -5.109937500000001)
Predict: 0.0
Else (feature 1 > -5.109937500000001)
If (feature 1 <= -2.2306882272727275)
Predict: 4.0
Else (feature 1 > -2.2306882272727275)
Predict: 0.0
Else (feature 3 > 7.291873594931593)
Predict: 0.0
Else (feature 4 > 5.830630767197693)
If (feature 0 <= -3.065027181818182)
If (feature 4 <= 7.3665835405207885)
If (feature 5 <= 1.4557098485258626)
If (feature 3 <= 3.5937950153207483)
If (feature 4 <= 6.291886217849433)
If (feature 0 <= -3.502226363636364)
Predict: 0.0
Else (feature 0 > -3.502226363636364)
Predict: 4.0
Else (feature 4 > 6.291886217849433)
Predict: 0.0
Else (feature 3 > 3.5937950153207483)
If (feature 2 <= 0.24674104545454545)
Predict: 4.0
Else (feature 2 > 0.24674104545454545)
If (feature 3 <= 5.42131601555171)
Predict: 4.0
Else (feature 3 > 5.42131601555171)
Predict: 0.0
Else (feature 5 > 1.4557098485258626)
If (feature 2 <= 0.1387476818181818)
Predict: 0.0
Else (feature 2 > 0.1387476818181818)
If (feature 3 <= 1.0884225728208126)
Predict: 2.0
Else (feature 3 > 1.0884225728208126)
Predict: 4.0
Else (feature 4 > 7.3665835405207885)
If (feature 3 <= 4.092746328718364)
Predict: 0.0
Else (feature 3 > 4.092746328718364)
Predict: 4.0
Else (feature 0 > -3.065027181818182)
If (feature 1 <= -9.433167272727271)
If (feature 2 <= -3.569613363636363)
If (feature 3 <= 2.037322122633077)
Predict: 4.0
Else (feature 3 > 2.037322122633077)
Predict: 2.0
Else (feature 2 > -3.569613363636363)
If (feature 0 <= -1.976380909090909)
If (feature 0 <= -2.5093826818181824)
Predict: 4.0
Else (feature 0 > -2.5093826818181824)
Predict: 2.0
Else (feature 0 > -1.976380909090909)
Predict: 0.0
Else (feature 1 > -9.433167272727271)
If (feature 3 <= 8.670695907958262)
If (feature 2 <= 1.1333349545454543)
If (feature 1 <= -1.1942972727272725)
If (feature 4 <= 8.97158739539847)
If (feature 2 <= -0.6624949545454546)
If (feature 0 <= 7.344164590909091)
If (feature 2 <= -5.720776181818183)
Predict: 0.0
Else (feature 2 > -5.720776181818183)
If (feature 1 <= -2.2306882272727275)
If (feature 1 <= -5.750931181818182)
Predict: 4.0
Else (feature 1 > -5.750931181818182)
If (feature 3 <= 2.9289256343028125)
If (feature 0 <= 1.2895552272727273)
Predict: 4.0
Else (feature 0 > 1.2895552272727273)
Predict: 0.0
Else (feature 3 > 2.9289256343028125)
Predict: 4.0
Else (feature 1 > -2.2306882272727275)
If (feature 5 <= 2.179859408203087)
Predict: 4.0
Else (feature 5 > 2.179859408203087)
Predict: 0.0
Else (feature 0 > 7.344164590909091)
If (feature 5 <= 1.7614806708061252)
Predict: 4.0
Else (feature 5 > 1.7614806708061252)
Predict: 0.0
Else (feature 2 > -0.6624949545454546)
If (feature 4 <= 7.033163268276285)
If (feature 2 <= -0.5126976818181816)
Predict: 4.0
Else (feature 2 > -0.5126976818181816)
If (feature 1 <= -3.8209812727272725)
If (feature 2 <= 0.29899645454545454)
If (feature 5 <= 1.7614806708061252)
Predict: 0.0
Else (feature 5 > 1.7614806708061252)
Predict: 4.0
Else (feature 2 > 0.29899645454545454)
Predict: 4.0
Else (feature 1 > -3.8209812727272725)
Predict: 0.0
Else (feature 4 > 7.033163268276285)
If (feature 5 <= 2.0408593821519982)
If (feature 1 <= -5.109937500000001)
If (feature 3 <= 1.5843984037196344)
Predict: 0.0
Else (feature 3 > 1.5843984037196344)
Predict: 4.0
Else (feature 1 > -5.109937500000001)
Predict: 0.0
Else (feature 5 > 2.0408593821519982)
If (feature 0 <= 3.5330349999999995)
If (feature 1 <= -5.750931181818182)
If (feature 3 <= 2.037322122633077)
Predict: 0.0
Else (feature 3 > 2.037322122633077)
Predict: 4.0
Else (feature 1 > -5.750931181818182)
Predict: 0.0
Else (feature 0 > 3.5330349999999995)
If (feature 5 <= 2.179859408203087)
Predict: 4.0
Else (feature 5 > 2.179859408203087)
Predict: 0.0
Else (feature 4 > 8.97158739539847)
Predict: 0.0
Else (feature 1 > -1.1942972727272725)
Predict: 0.0
Else (feature 2 > 1.1333349545454543)
If (feature 1 <= -5.750931181818182)
If (feature 0 <= 0.9882181363636364)
If (feature 5 <= 1.7614806708061252)
Predict: 0.0
Else (feature 5 > 1.7614806708061252)
Predict: 2.0
Else (feature 0 > 0.9882181363636364)
Predict: 4.0
Else (feature 1 > -5.750931181818182)
Predict: 0.0
Else (feature 3 > 8.670695907958262)
Predict: 0.0
Else (feature 5 > 2.600144428803959)
If (feature 3 <= 3.2305826019123103)
If (feature 0 <= 2.0089040454545453)
If (feature 1 <= -7.698300909090908)
If (feature 5 <= 6.108758959317355)
If (feature 1 <= -11.596521363636366)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
If (feature 0 <= -1.976380909090909)
If (feature 3 <= 2.9289256343028125)
If (feature 5 <= 3.8343025836297375)
Predict: 4.0
Else (feature 5 > 3.8343025836297375)
Predict: 2.0
Else (feature 3 > 2.9289256343028125)
Predict: 0.0
Else (feature 0 > -1.976380909090909)
If (feature 4 <= 7.033163268276285)
Predict: 2.0
Else (feature 4 > 7.033163268276285)
Predict: 0.0
Else (feature 1 > -11.596521363636366)
If (feature 2 <= -2.4757429090909095)
If (feature 3 <= 2.037322122633077)
Predict: 4.0
Else (feature 3 > 2.037322122633077)
If (feature 4 <= 6.291886217849433)
Predict: 2.0
Else (feature 4 > 6.291886217849433)
Predict: 0.0
Else (feature 2 > -2.4757429090909095)
If (feature 4 <= 6.684054463098245)
Predict: 2.0
Else (feature 4 > 6.684054463098245)
If (feature 1 <= -10.088093363636364)
Predict: 2.0
Else (feature 1 > -10.088093363636364)
If (feature 0 <= -4.214637727272727)
Predict: 2.0
Else (feature 0 > -4.214637727272727)
Predict: 0.0
Else (feature 5 > 6.108758959317355)
If (feature 1 <= -9.276403181818182)
Predict: 0.0
Else (feature 1 > -9.276403181818182)
If (feature 2 <= -3.569613363636363)
Predict: 4.0
Else (feature 2 > -3.569613363636363)
Predict: 0.0
Else (feature 1 > -7.698300909090908)
If (feature 4 <= 6.684054463098245)
If (feature 4 <= 3.5166543239603483)
If (feature 1 <= -5.109937500000001)
Predict: 2.0
Else (feature 1 > -5.109937500000001)
If (feature 2 <= 1.1333349545454543)
Predict: 0.0
Else (feature 2 > 1.1333349545454543)
Predict: 2.0
Else (feature 4 > 3.5166543239603483)
If (feature 3 <= 1.7795641834853977)
If (feature 5 <= 3.469288509552827)
If (feature 0 <= -3.065027181818182)
If (feature 1 <= -3.8209812727272725)
Predict: 2.0
Else (feature 1 > -3.8209812727272725)
Predict: 0.0
Else (feature 0 > -3.065027181818182)
Predict: 4.0
Else (feature 5 > 3.469288509552827)
Predict: 0.0
Else (feature 3 > 1.7795641834853977)
If (feature 1 <= -7.295937636363637)
Predict: 2.0
Else (feature 1 > -7.295937636363637)
If (feature 4 <= 4.0067789684062625)
If (feature 0 <= -1.105464818181818)
Predict: 0.0
Else (feature 0 > -1.105464818181818)
Predict: 2.0
Else (feature 4 > 4.0067789684062625)
Predict: 0.0
Else (feature 4 > 6.684054463098245)
If (feature 1 <= -3.8209812727272725)
If (feature 5 <= 8.699793726565169)
If (feature 3 <= 1.5843984037196344)
Predict: 0.0
Else (feature 3 > 1.5843984037196344)
If (feature 2 <= -1.073569090909091)
If (feature 5 <= 4.262646949929151)
Predict: 4.0
Else (feature 5 > 4.262646949929151)
If (feature 0 <= 0.9882181363636364)
If (feature 2 <= -2.198210545454545)
Predict: 4.0
Else (feature 2 > -2.198210545454545)
If (feature 0 <= 0.43257299999999993)
Predict: 4.0
Else (feature 0 > 0.43257299999999993)
Predict: 0.0
Else (feature 0 > 0.9882181363636364)
Predict: 4.0
Else (feature 2 > -1.073569090909091)
If (feature 3 <= 2.4887269112897967)
Predict: 2.0
Else (feature 3 > 2.4887269112897967)
Predict: 0.0
Else (feature 5 > 8.699793726565169)
Predict: 0.0
Else (feature 1 > -3.8209812727272725)
If (feature 5 <= 3.469288509552827)
Predict: 2.0
Else (feature 5 > 3.469288509552827)
Predict: 0.0
Else (feature 0 > 2.0089040454545453)
If (feature 5 <= 2.8353340431585474)
If (feature 2 <= -5.720776181818183)
Predict: 2.0
Else (feature 2 > -5.720776181818183)
Predict: 0.0
Else (feature 5 > 2.8353340431585474)
If (feature 4 <= 4.0067789684062625)
If (feature 3 <= 2.283530423945612)
If (feature 1 <= -13.810390454545457)
Predict: 0.0
Else (feature 1 > -13.810390454545457)
If (feature 2 <= -1.2442670454545455)
Predict: 2.0
Else (feature 2 > -1.2442670454545455)
Predict: 0.0
Else (feature 3 > 2.283530423945612)
Predict: 0.0
Else (feature 4 > 4.0067789684062625)
If (feature 5 <= 3.1250203637838814)
If (feature 3 <= 2.9289256343028125)
Predict: 0.0
Else (feature 3 > 2.9289256343028125)
If (feature 0 <= 2.8563333333333336)
Predict: 4.0
Else (feature 0 > 2.8563333333333336)
Predict: 0.0
Else (feature 5 > 3.1250203637838814)
Predict: 0.0
Else (feature 3 > 3.2305826019123103)
If (feature 3 <= 4.596493775946534)
If (feature 1 <= -8.097179727272728)
If (feature 5 <= 3.469288509552827)
If (feature 3 <= 3.5937950153207483)
If (feature 5 <= 2.8353340431585474)
Predict: 0.0
Else (feature 5 > 2.8353340431585474)
If (feature 0 <= 2.0089040454545453)
If (feature 4 <= 5.398982323543355)
If (feature 0 <= -8.90539)
Predict: 0.0
Else (feature 0 > -8.90539)
Predict: 2.0
Else (feature 4 > 5.398982323543355)
Predict: 2.0
Else (feature 0 > 2.0089040454545453)
Predict: 0.0
Else (feature 3 > 3.5937950153207483)
If (feature 2 <= -0.6624949545454546)
Predict: 0.0
Else (feature 2 > -0.6624949545454546)
If (feature 0 <= 2.0089040454545453)
If (feature 4 <= 7.3665835405207885)
If (feature 5 <= 3.1250203637838814)
Predict: 2.0
Else (feature 5 > 3.1250203637838814)
Predict: 0.0
Else (feature 4 > 7.3665835405207885)
Predict: 0.0
Else (feature 0 > 2.0089040454545453)
Predict: 0.0
Else (feature 5 > 3.469288509552827)
If (feature 3 <= 4.092746328718364)
Predict: 0.0
Else (feature 3 > 4.092746328718364)
If (feature 4 <= 7.72195028414196)
Predict: 0.0
Else (feature 4 > 7.72195028414196)
If (feature 1 <= -11.596521363636366)
If (feature 5 <= 4.262646949929151)
Predict: 2.0
Else (feature 5 > 4.262646949929151)
Predict: 0.0
Else (feature 1 > -11.596521363636366)
If (feature 1 <= -8.4067287012987)
Predict: 0.0
Else (feature 1 > -8.4067287012987)
If (feature 0 <= 1.2895552272727273)
Predict: 0.0
Else (feature 0 > 1.2895552272727273)
Predict: 4.0
Else (feature 1 > -8.097179727272728)
If (feature 1 <= -3.8209812727272725)
If (feature 2 <= -1.5194759545454544)
If (feature 4 <= 5.830630767197693)
Predict: 0.0
Else (feature 4 > 5.830630767197693)
If (feature 5 <= 6.108758959317355)
If (feature 2 <= -5.720776181818183)
Predict: 0.0
Else (feature 2 > -5.720776181818183)
If (feature 0 <= -0.7518725909090909)
Predict: 0.0
Else (feature 0 > -0.7518725909090909)
Predict: 4.0
Else (feature 5 > 6.108758959317355)
Predict: 0.0
Else (feature 2 > -1.5194759545454544)
If (feature 2 <= -0.18174913636363638)
If (feature 1 <= -5.750931181818182)
Predict: 0.0
Else (feature 1 > -5.750931181818182)
If (feature 0 <= -7.121753363636364)
Predict: 0.0
Else (feature 0 > -7.121753363636364)
Predict: 4.0
Else (feature 2 > -0.18174913636363638)
If (feature 0 <= 2.0089040454545453)
If (feature 2 <= 1.1333349545454543)
If (feature 0 <= -1.976380909090909)
If (feature 0 <= -4.165866818181819)
Predict: 2.0
Else (feature 0 > -4.165866818181819)
Predict: 0.0
Else (feature 0 > -1.976380909090909)
Predict: 2.0
Else (feature 2 > 1.1333349545454543)
Predict: 0.0
Else (feature 0 > 2.0089040454545453)
If (feature 0 <= 4.459689318181818)
Predict: 4.0
Else (feature 0 > 4.459689318181818)
Predict: 0.0
Else (feature 1 > -3.8209812727272725)
If (feature 2 <= 0.6682646818181818)
Predict: 0.0
Else (feature 2 > 0.6682646818181818)
If (feature 4 <= 3.5166543239603483)
Predict: 2.0
Else (feature 4 > 3.5166543239603483)
Predict: 0.0
Else (feature 3 > 4.596493775946534)
If (feature 4 <= 6.291886217849433)
If (feature 2 <= -0.6624949545454546)
If (feature 5 <= 2.8353340431585474)
If (feature 0 <= 0.43257299999999993)
Predict: 0.0
Else (feature 0 > 0.43257299999999993)
If (feature 3 <= 8.670695907958262)
If (feature 3 <= 6.490709433033732)
Predict: 4.0
Else (feature 3 > 6.490709433033732)
If (feature 3 <= 7.291873594931593)
Predict: 0.0
Else (feature 3 > 7.291873594931593)
Predict: 4.0
Else (feature 3 > 8.670695907958262)
Predict: 0.0
Else (feature 5 > 2.8353340431585474)
If (feature 1 <= -6.820416818181818)
If (feature 1 <= -7.295937636363637)
Predict: 0.0
Else (feature 1 > -7.295937636363637)
If (feature 2 <= -1.8347482272727271)
Predict: 0.0
Else (feature 2 > -1.8347482272727271)
If (feature 2 <= -1.073569090909091)
Predict: 2.0
Else (feature 2 > -1.073569090909091)
Predict: 0.0
Else (feature 1 > -6.820416818181818)
Predict: 0.0
Else (feature 2 > -0.6624949545454546)
If (feature 1 <= -8.948937727272728)
Predict: 0.0
Else (feature 1 > -8.948937727272728)
If (feature 3 <= 5.42131601555171)
If (feature 0 <= 0.32806340909090914)
If (feature 1 <= -6.2386456818181815)
If (feature 0 <= -3.065027181818182)
Predict: 0.0
Else (feature 0 > -3.065027181818182)
If (feature 5 <= 4.262646949929151)
Predict: 2.0
Else (feature 5 > 4.262646949929151)
Predict: 0.0
Else (feature 1 > -6.2386456818181815)
Predict: 2.0
Else (feature 0 > 0.32806340909090914)
Predict: 0.0
Else (feature 3 > 5.42131601555171)
If (feature 2 <= -0.5126976818181816)
If (feature 3 <= 5.87583214563006)
If (feature 4 <= 5.019055660088064)
Predict: 0.0
Else (feature 4 > 5.019055660088064)
Predict: 2.0
Else (feature 3 > 5.87583214563006)
Predict: 0.0
Else (feature 2 > -0.5126976818181816)
Predict: 0.0
Else (feature 4 > 6.291886217849433)
If (feature 5 <= 2.8353340431585474)
If (feature 4 <= 7.72195028414196)
If (feature 0 <= 5.969858363636364)
If (feature 0 <= -7.121753363636364)
Predict: 0.0
Else (feature 0 > -7.121753363636364)
Predict: 4.0
Else (feature 0 > 5.969858363636364)
Predict: 0.0
Else (feature 4 > 7.72195028414196)
Predict: 0.0
Else (feature 5 > 2.8353340431585474)
If (feature 3 <= 5.42131601555171)
If (feature 2 <= -1.073569090909091)
If (feature 5 <= 4.913251092952768)
If (feature 0 <= 2.8563333333333336)
If (feature 4 <= 7.3665835405207885)
Predict: 0.0
Else (feature 4 > 7.3665835405207885)
If (feature 0 <= -5.695193636363635)
Predict: 2.0
Else (feature 0 > -5.695193636363635)
Predict: 4.0
Else (feature 0 > 2.8563333333333336)
Predict: 0.0
Else (feature 5 > 4.913251092952768)
Predict: 0.0
Else (feature 2 > -1.073569090909091)
If (feature 5 <= 4.262646949929151)
If (feature 2 <= -0.18174913636363638)
If (feature 5 <= 3.8343025836297375)
Predict: 0.0
Else (feature 5 > 3.8343025836297375)
Predict: 2.0
Else (feature 2 > -0.18174913636363638)
Predict: 0.0
Else (feature 5 > 4.262646949929151)
Predict: 0.0
Else (feature 3 > 5.42131601555171)
If (feature 1 <= -8.948937727272728)
Predict: 0.0
Else (feature 1 > -8.948937727272728)
If (feature 1 <= -8.712045)
If (feature 3 <= 5.87583214563006)
Predict: 4.0
Else (feature 3 > 5.87583214563006)
Predict: 0.0
Else (feature 1 > -8.712045)
If (feature 2 <= -0.6624949545454546)
If (feature 5 <= 3.1250203637838814)
If (feature 3 <= 8.670695907958262)
If (feature 3 <= 6.490709433033732)
Predict: 4.0
Else (feature 3 > 6.490709433033732)
If (feature 4 <= 7.033163268276285)
Predict: 0.0
Else (feature 4 > 7.033163268276285)
If (feature 2 <= -1.0073777272727273)
If (feature 1 <= -5.109937500000001)
Predict: 4.0
Else (feature 1 > -5.109937500000001)
Predict: 0.0
Else (feature 2 > -1.0073777272727273)
Predict: 0.0
Else (feature 3 > 8.670695907958262)
Predict: 0.0
Else (feature 5 > 3.1250203637838814)
Predict: 0.0
Else (feature 2 > -0.6624949545454546)
Predict: 0.0
import org.apache.spark.ml.classification.RandomForestClassificationModel
rfModel: org.apache.spark.ml.classification.RandomForestClassificationModel = RandomForestClassificationModel: uid=rfc_6ab6b8d21734, numTrees=10, numClasses=5, numFeatures=6
Save and Load later to Serve Predictions
When you are satisfied with the model you have trained/fitted then you can save it to a file and load it again from another low-latency prediciton-serving system to serve predictions.
model
res24: org.apache.spark.ml.PipelineModel = pipeline_dd3e9eb4d47c
import org.apache.spark.ml.PipelineModel
import org.apache.spark.ml.PipelineModel
Save the model to a file in stable storage.
model.write.overwrite().save("/datasets/sds/ActivityRecognition/preTrainedModels/myRandomForestClassificationModelAsPipeLineModel")
Load the model, typically from a prediction or model serving layer/system.
val same_rfModelFromSavedFile = PipelineModel.load("/datasets/sds/ActivityRecognition/preTrainedModels/myRandomForestClassificationModelAsPipeLineModel")
same_rfModelFromSavedFile: org.apache.spark.ml.PipelineModel = pipeline_dd3e9eb4d47c
same_rfModelFromSavedFile
res26: org.apache.spark.ml.PipelineModel = pipeline_dd3e9eb4d47c
Suppose new data is coming at us and we need to serve our predicitons of the activities
Note: We only need accelerometer readings so prediciton pipeline can be simplified to only take in the needed features for prediction. For simplicity let's assume the test data is representative of the new data (previously unseen) that is coming at us for serving our activity predicitons.
val newDataForPrediction = testData.sample(0.0004,12348L) // get a random test data as new data for prediction
display(newDataForPrediction.select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ")) // showing only features that will be used by model for prediction
| meanX | meanY | meanZ | stddevX | stddevY | stddevZ |
|---|---|---|---|---|---|
| -1.9955399999999999 | -9.258980909090907 | 1.0410163636363636 | 1.0549255357138732 | 0.7680301427703919 | 1.8853035398286844 |
Here is another new data coming at you... you can predict the current activity being done by simply transforming the loaded model (estimator).
val newDataForPrediction = testData.sample(0.0004,1234L) // get a random test data as new data for prediction
display(newDataForPrediction.select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ")) // showing only features that will be used by model for prediction
| meanX | meanY | meanZ | stddevX | stddevY | stddevZ |
|---|---|---|---|---|---|
| 0.42212218181818173 | -0.7135524545454545 | 9.440134545454546 | 8.409472091286836e-2 | 9.073949633706861e-2 | 9.389382640736067e-2 |
display(same_rfModelFromSavedFile.transform(newDataForPrediction).select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ","prediction"))
| meanX | meanY | meanZ | stddevX | stddevY | stddevZ | prediction |
|---|---|---|---|---|---|---|
| 0.42212218181818173 | -0.7135524545454545 | 9.440134545454546 | 8.409472091286836e-2 | 9.073949633706861e-2 | 9.389382640736067e-2 | 3.0 |
Some minor post-processing to connect prediction label to the activity string.
Typically one prediction is just a small part of larger business or science use-case.
activityLabelDF.show
+--------+-----+
|activity|label|
+--------+-----+
| Jogging| 0.0|
| Jumping| 4.0|
|Standing| 1.0|
| Walking| 2.0|
| Sitting| 3.0|
+--------+-----+
display(same_rfModelFromSavedFile
.transform(newDataForPrediction)
.select("meanX", "meanY", "meanZ", "stddevX", "stddevY","stddevZ","prediction")
.join(activityLabelDF,$"prediction"===$"label")
.drop("prediction","label")
.withColumnRenamed("activity","Your current activity is likely to be")
)
| meanX | meanY | meanZ | stddevX | stddevY | stddevZ | Your current activity is likely to be |
|---|---|---|---|---|---|---|
| 0.42212218181818173 | -0.7135524545454545 | 9.440134545454546 | 8.409472091286836e-2 | 9.073949633706861e-2 | 9.389382640736067e-2 | Sitting |
An activity-based music-recommendation system - a diatribe:
For example, you may want to correlate historical music-listening habits for a user with their historical physical activities and then recommend music to them using a subsequent recommender system pipeline that uses activity prediciton as one of its input.
NOTE: One has to be legally compliant for using the data in such manner with client consent, depending on the jurisdiction of your operations - GDPR applies for EU operations, for example --- this is coming attraction!
display(same_rfModelFromSavedFile.transform(testData.sample(0.01))) // note more can be done with such data, time of day can be informative for other decision problems
- Download and Load Data
The following anonymized dataset is used with kind permission of Amira Lakhal.
wget http://lamastex.org/datasets/public/ActivityRecognition/dataTraining.csv
--2020-11-13 09:48:32-- http://lamastex.org/datasets/public/ActivityRecognition/dataTraining.csv
Resolving lamastex.org (lamastex.org)... 166.62.28.100
Connecting to lamastex.org (lamastex.org)|166.62.28.100|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 931349 (910K) [text/csv]
Saving to: ‘dataTraining.csv’
0K .......... .......... .......... .......... .......... 5% 140K 6s
50K .......... .......... .......... .......... .......... 10% 282K 4s
100K .......... .......... .......... .......... .......... 16% 288K 4s
150K .......... .......... .......... .......... .......... 21% 14.8M 3s
200K .......... .......... .......... .......... .......... 27% 33.2M 2s
250K .......... .......... .......... .......... .......... 32% 287K 2s
300K .......... .......... .......... .......... .......... 38% 35.0M 1s
350K .......... .......... .......... .......... .......... 43% 35.7M 1s
400K .......... .......... .......... .......... .......... 49% 48.3M 1s
450K .......... .......... .......... .......... .......... 54% 293K 1s
500K .......... .......... .......... .......... .......... 60% 20.8M 1s
550K .......... .......... .......... .......... .......... 65% 44.5M 1s
600K .......... .......... .......... .......... .......... 71% 39.1M 0s
650K .......... .......... .......... .......... .......... 76% 44.6M 0s
700K .......... .......... .......... .......... .......... 82% 41.9M 0s
750K .......... .......... .......... .......... .......... 87% 42.0M 0s
800K .......... .......... .......... .......... .......... 93% 42.4M 0s
850K .......... .......... .......... .......... .......... 98% 297K 0s
900K ......... 100% 35.8M=1.2s
2020-11-13 09:48:33 (734 KB/s) - ‘dataTraining.csv’ saved [931349/931349]
pwd && ls
/databricks/driver
conf
dataTraining.csv
derby.log
eventlogs
ganglia
logs
//dbutils.fs.mkdirs("dbfs:///datasets/sds/ActivityRecognition") // make directory if needed
dbutils.fs.mv("file:///databricks/driver/dataTraining.csv","dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv")
res25: Boolean = true
display(dbutils.fs.ls("dbfs:///datasets/sds/ActivityRecognition"))
| path | name | size |
|---|---|---|
| dbfs:/datasets/sds/ActivityRecognition/dataTraining.csv | dataTraining.csv | 931349.0 |
dbutils.fs.head("dbfs:///datasets/sds/ActivityRecognition/dataTraining.csv")
[Truncated to first 65536 bytes]
res27: String =
"user_id","activity","timeStampAsLong","x","y","z"
"user_001","Jumping",1446047227606,"4.33079","-12.72175","-3.18118"
"user_001","Jumping",1446047227671,"0.575403","-0.727487","2.95007"
"user_001","Jumping",1446047227735,"-1.60885","3.52607","-0.1922"
"user_001","Jumping",1446047227799,"0.690364","-0.037722","1.72382"
"user_001","Jumping",1446047227865,"3.44943","-1.68549","2.29862"
"user_001","Jumping",1446047227930,"1.87829","-1.91542","0.880768"
"user_001","Jumping",1446047227995,"1.57173","-5.86241","-3.75599"
"user_001","Jumping",1446047228059,"3.41111","-17.93331","0.535886"
"user_001","Jumping",1446047228123,"3.18118","-19.58108","5.74745"
"user_001","Jumping",1446047228189,"7.85626","-19.2362","0.804128"
"user_001","Jumping",1446047228253,"1.26517","-8.85139","2.18366"
"user_001","Jumping",1446047228318,"0.077239","1.15021","1.53221"
"user_001","Jumping",1446047228383,"0.230521","2.0699","-1.41845"
"user_001","Jumping",1446047228447,"0.652044","-0.497565","1.76214"
"user_001","Jumping",1446047228512,"1.53341","-0.305964","1.41725"
"user_001","Jumping",1446047228578,"-1.07237","-1.95374","0.191003"
"user_001","Jumping",1446047228642,"2.75966","-13.75639","0.191003"
"user_001","Jumping",1446047228707,"7.43474","-19.58108","2.95007"
"user_001","Jumping",1446047228771,"5.63368","-19.58108","5.59417"
"user_001","Jumping",1446047228836,"5.02056","-11.72542","-1.38013"
"user_001","Jumping",1446047228900,"-2.10702","0.575403","1.07237"
"user_001","Jumping",1446047228966,"-1.30229","2.2615","-1.26517"
"user_001","Jumping",1446047229030,"1.68669","-0.957409","1.57053"
"user_001","Jumping",1446047229095,"2.60638","-0.229323","2.14534"
"user_001","Jumping",1446047229159,"1.30349","-0.152682","0.497565"
"user_001","Jumping",1446047229224,"1.64837","-8.12331","1.60885"
"user_001","Jumping",1446047229290,"0.15388","-18.46979","-1.03525"
"user_001","Jumping",1446047229354,"4.98224","-19.58108","0.995729"
"user_001","Jumping",1446047229419,"5.17384","-19.2362","0.612526"
"user_001","Jumping",1446047229483,"2.03158","-9.11964","1.34061"
"user_001","Jumping",1446047229549,"-1.95374","-1.03405","0.574206"
"user_001","Jumping",1446047229612,"0.1922","1.30349","-1.03525"
"user_001","Jumping",1446047229678,"1.64837","-0.727487","-0.307161"
"user_001","Jumping",1446047229743,"2.56806","-1.03405","0.267643"
"user_001","Jumping",1446047229806,"1.41845","-0.305964","0.727487"
"user_001","Jumping",1446047229872,"1.03525","-6.82042","0.957409"
"user_001","Jumping",1446047229936,"3.94759","-19.31284","-1.22685"
"user_001","Jumping",1446047230001,"6.13185","-19.58108","2.72014"
"user_001","Jumping",1446047230066,"7.89458","-16.9753","0.229323"
"user_001","Jumping",1446047230131,"2.6447","-3.75479","0.114362"
"user_001","Jumping",1446047230195,"-2.41358","1.91661","-0.000599"
"user_001","Jumping",1446047230260,"-1.95374","-0.114362","-0.268841"
"user_001","Jumping",1446047230325,"-0.229323","-1.95374","2.75846"
"user_001","Jumping",1446047230390,"2.68302","0.268841","0.535886"
"user_001","Jumping",1446047230455,"1.15021","-1.41725","0.880768"
"user_001","Jumping",1446047230519,"2.98958","-11.53382","-0.920286"
"user_001","Jumping",1446047230584,"8.00954","-19.58108","-0.1922"
"user_001","Jumping",1446047230649,"7.31978","-19.58108","2.52854"
"user_001","Jumping",1446047230713,"5.44208","-13.56479","-0.498763"
"user_001","Jumping",1446047230779,"0.728685","-0.420925","1.68549"
"user_001","Jumping",1446047230842,"1.18853","1.41845","-2.22318"
"user_001","Jumping",1446047230907,"0.15388","-1.80046","2.03038"
"user_001","Jumping",1446047230972,"-0.037722","1.07357","-0.958607"
"user_001","Jumping",1446047231038,"0.230521","0.728685","-0.690364"
"user_001","Jumping",1446047231102,"0.805325","-1.14901","1.53221"
"user_001","Jumping",1446047231167,"5.17384","-9.15796","-2.91294"
"user_001","Jumping",1446047231231,"8.00954","-19.54276","1.49389"
"user_001","Jumping",1446047231296,"11.61165","-19.58108","4.25296"
"user_001","Jumping",1446047231361,"7.89458","-18.35483","3.71647"
"user_001","Jumping",1446047231426,"0.537083","-3.21831","0.420925"
"user_001","Jumping",1446047231490,"-1.91542","4.10087","-2.6447"
"user_001","Jumping",1446047231555,"-0.919089","-0.382604","0.114362"
"user_001","Jumping",1446047231620,"0.613724","-0.842448","1.57053"
"user_001","Jumping",1446047231684,"1.38013","-0.459245","0.305964"
"user_001","Jumping",1446047231749,"2.29982","-1.49389","-1.26517"
"user_001","Jumping",1446047231814,"4.94392","-10.95901","-0.498763"
"user_001","Jumping",1446047231878,"3.75599","-19.27452","-1.76333"
"user_001","Jumping",1446047231944,"7.70298","-19.58108","-2.95126"
"user_001","Jumping",1446047232008,"6.55337","-17.51178","-1.91661"
"user_001","Jumping",1446047232073,"2.10822","-2.03038","-0.268841"
"user_001","Jumping",1446047232138,"-1.68549","3.67935","-0.881966"
"user_001","Jumping",1446047232203,"0.15388","-0.114362","0.076042"
"user_001","Jumping",1446047232267,"2.79798","-1.95374","0.689167"
"user_001","Jumping",1446047232332,"1.45677","-0.957409","0.420925"
"user_001","Jumping",1446047232397,"1.18853","-2.95007","0.650847"
"user_001","Jumping",1446047232462,"3.44943","-13.87135","-3.71767"
"user_001","Jumping",1446047232526,"3.79431","-19.58108","-0.345482"
"user_001","Jumping",1446047232591,"6.09353","-19.58108","2.87342"
"user_001","Jumping",1446047232655,"4.48408","-14.3312","-0.498763"
"user_001","Jumping",1446047232720,"-0.037722","-1.68549","2.6435"
"user_001","Jumping",1446047232785,"0.613724","2.68302","-2.37646"
"user_001","Jumping",1446047232850,"0.996927","-0.497565","0.497565"
"user_001","Jumping",1446047232915,"0.15388","-0.689167","1.34061"
"user_001","Jumping",1446047232979,"1.07357","1.07357","-2.60638"
"user_001","Jumping",1446047233044,"2.37646","-3.29495","-1.45677"
"user_001","Jumping",1446047233109,"2.22318","-16.09393","1.37893"
"user_001","Jumping",1446047233173,"6.82161","-19.58108","0.037722"
"user_001","Jumping",1446047233238,"8.39275","-19.54276","1.22565"
"user_001","Jumping",1446047233303,"1.22685","-9.50284","-0.307161"
"user_001","Jumping",1446047233368,"1.83997","-0.152682","1.41725"
"user_001","Jumping",1446047233432,"-0.765807","0.996927","-1.91661"
"user_001","Jumping",1446047233497,"0.728685","-0.612526","0.152682"
"user_001","Jumping",1446047233562,"0.996927","-0.305964","0.995729"
"user_001","Jumping",1446047233626,"1.22685","-0.305964","0.037722"
"user_001","Jumping",1446047233692,"1.91661","-2.95007","-0.345482"
"user_001","Jumping",1446047233755,"4.75232","-14.63776","-3.67935"
"user_001","Jumping",1446047233821,"4.56072","-19.58108","-2.6447"
"user_001","Jumping",1446047233886,"10.69197","-19.38948","-5.94025"
"user_001","Jumping",1446047233950,"3.10454","-10.42253","-2.22318"
"user_001","Jumping",1446047234015,"-0.152682","1.61005","1.80046"
"user_001","Jumping",1446047234080,"-0.267643","1.83997","-0.958607"
"user_001","Jumping",1446047234145,"1.99325","-0.842448","-0.230521"
"user_001","Jumping",1446047234210,"2.79798","-0.114362","0.919089"
"user_001","Jumping",1446047234273,"1.11189","-0.152682","1.83878"
"user_001","Jumping",1446047234339,"2.75966","-5.05768","-0.537083"
"user_001","Jumping",1446047234403,"4.33079","-16.82202","-3.06622"
"user_001","Jumping",1446047234468,"6.89825","-19.58108","-4.25415"
"user_001","Jumping",1446047234533,"11.49669","-19.50444","-2.52974"
"user_001","Jumping",1446047234598,"6.20849","-9.08132","-1.80165"
"user_001","Jumping",1446047234663,"0.805325","1.15021","-0.15388"
"user_001","Jumping",1446047234726,"-1.76214","2.14654","-0.460442"
"user_001","Jumping",1446047234792,"0.000599","-1.11069","-0.230521"
"user_001","Jumping",1446047234857,"0.652044","0.230521","-0.920286"
"user_001","Jumping",1446047234922,"1.26517","-0.114362","-0.000599"
"user_001","Jumping",1446047234986,"5.36544","-4.09967","-0.537083"
"user_001","Jumping",1446047235051,"6.51505","-17.05194","-3.56439"
"user_001","Jumping",1446047235115,"6.93658","-19.58108","-2.75966"
"user_001","Jumping",1446047235181,"7.89458","-19.58108","-3.18118"
"user_001","Jumping",1446047235245,"1.95493","-9.6178","-1.34181"
"user_001","Jumping",1446047235310,"-2.18366","3.14286","-0.1922"
"user_001","Jumping",1446047235375,"-2.87342","1.99325","-1.76333"
"user_001","Jumping",1446047235439,"1.03525","-1.80046","1.64717"
"user_001","Jumping",1446047235504,"1.95493","-0.689167","1.49389"
"user_001","Jumping",1446047235569,"1.26517","0.383802","-0.307161"
"user_001","Jumping",1446047235633,"1.18853","-2.72014","1.41725"
"user_001","Jumping",1446047235698,"5.71033","-16.51545","-2.03158"
"user_001","Jumping",1446047235763,"6.55337","-19.58108","0.995729"
"user_001","Jumping",1446047235828,"10.577","-19.4278","-1.57173"
"user_001","Jumping",1446047235892,"2.79798","-9.88604","-0.843646"
"user_001","Jumping",1446047235957,"0.652044","-0.037722","1.14901"
"user_001","Jumping",1446047236022,"0.843646","1.95493","-2.14654"
"user_001","Jumping",1446047236086,"0.15388","-0.305964","0.995729"
"user_001","Jumping",1446047236152,"0.767005","-0.114362","0.804128"
"user_001","Jumping",1446047236215,"-0.650847","-0.650847","-0.230521"
"user_001","Jumping",1446047236281,"3.56439","-5.90073","0.727487"
"user_001","Jumping",1446047236346,"7.05154","-17.97163","-1.61005"
"user_001","Jumping",1446047236410,"9.46572","-19.58108","2.10702"
"user_001","Jumping",1446047236475,"7.58802","-18.54643","3.10335"
"user_001","Jumping",1446047236540,"0.767005","-6.28393","0.689167"
"user_001","Jumping",1446047236604,"0.307161","2.29982","-0.498763"
"user_001","Jumping",1446047236669,"0.11556","-0.114362","-1.11189"
"user_001","Jumping",1446047236734,"2.2615","-1.45557","2.14534"
"user_001","Jumping",1446047236798,"1.80165","0.345482","0.765807"
"user_001","Jumping",1446047236863,"-0.650847","-0.305964","-1.15021"
"user_001","Jumping",1446047236928,"3.48775","-7.31858","-0.728685"
"user_001","Jumping",1446047236992,"5.32712","-18.92964","0.114362"
"user_001","Jumping",1446047237057,"10.34708","-19.58108","2.37526"
"user_001","Jumping",1446047237122,"5.97857","-17.78002","0.344284"
"user_001","Jumping",1446047237187,"2.8363","-3.25663","0.152682"
"user_001","Jumping",1446047237252,"-0.995729","3.37279","-1.64837"
"user_001","Jumping",1446047237317,"-1.64717","-0.650847","1.41725"
"user_001","Jumping",1446047237381,"1.30349","-0.152682","1.45557"
"user_001","Jumping",1446047237446,"2.79798","0.000599","-0.460442"
"user_001","Jumping",1446047237510,"1.41845","-0.459245","-1.38013"
"user_001","Jumping",1446047237575,"3.90927","-6.62882","-1.49509"
"user_001","Jumping",1446047237640,"4.98224","-19.27452","1.57053"
"user_001","Jumping",1446047237705,"12.22478","-19.58108","2.0687"
"user_001","Jumping",1446047237769,"4.5224","-16.74538","-0.383802"
"user_001","Jumping",1446047237834,"0.690364","-0.880768","-0.537083"
"user_001","Jumping",1446047237899,"1.26517","2.18486","-1.03525"
"user_001","Jumping",1446047237964,"2.6447","-0.037722","1.18733"
"user_001","Jumping",1446047238028,"2.56806","-0.114362","1.03405"
"user_001","Jumping",1446047238094,"-1.14901","0.11556","-0.307161"
"user_001","Jumping",1446047238157,"-0.574206","-0.114362","-0.920286"
"user_001","Jumping",1446047238223,"2.60638","-3.40991","-0.422122"
"user_001","Jumping",1446047238287,"6.32345","-17.62674","-0.728685"
"user_001","Jumping",1446047238352,"10.53868","-19.58108","0.919089"
"user_001","Jumping",1446047238417,"7.85626","-18.20155","0.995729"
"user_001","Jumping",1446047238481,"-0.152682","-1.41725","-1.22685"
"user_001","Jumping",1446047238546,"1.26517","2.22318","-1.87829"
"user_001","Jumping",1446047238611,"0.537083","-0.497565","1.76214"
"user_001","Jumping",1446047238676,"1.95493","0.230521","1.60885"
"user_001","Jumping",1446047238741,"0.920286","0.537083","-0.422122"
"user_001","Jumping",1446047238806,"0.000599","-0.650847","0.497565"
"user_001","Jumping",1446047238870,"1.64837","-1.99206","-1.72501"
"user_001","Jumping",1446047238935,"5.0972","-14.75272","-1.41845"
"user_001","Jumping",1446047239000,"10.50036","-19.58108","1.91542"
"user_001","Jumping",1446047239064,"8.16283","-19.58108","1.95374"
"user_001","Jumping",1446047239129,"3.56439","-9.4262","-1.22685"
"user_001","Jumping",1446047239194,"0.920286","2.79798","-1.34181"
"user_001","Jumping",1446047239259,"0.1922","0.652044","-0.537083"
"user_001","Jumping",1446047239324,"1.64837","-0.957409","1.41725"
"user_001","Jumping",1446047239388,"1.99325","-0.191003","0.957409"
"user_001","Jumping",1446047239453,"1.26517","-0.305964","-0.000599"
"user_001","Jumping",1446047239518,"2.4531","-4.17632","-1.34181"
"user_001","Jumping",1446047239583,"4.44576","-16.05561","-0.537083"
"user_001","Jumping",1446047239647,"9.58068","-19.58108","1.53221"
"user_001","Jumping",1446047239712,"8.04786","-19.2362","2.0687"
"user_001","Jumping",1446047239777,"1.80165","-6.01569","-0.422122"
"user_001","Jumping",1446047239841,"0.11556","2.10822","-2.6447"
"user_001","Jumping",1446047239906,"0.345482","-0.612526","-1.22685"
"user_001","Jumping",1446047239971,"1.45677","-0.880768","2.2603"
"user_001","Jumping",1446047240036,"0.843646","1.11189","-0.537083"
"user_001","Jumping",1446047240100,"0.996927","0.038919","-0.575403"
"user_001","Jumping",1446047240166,"3.29615","-3.98471","-0.422122"
"user_001","Jumping",1446047240231,"5.21216","-16.7837","-0.613724"
"user_001","Jumping",1446047240295,"7.39642","-19.58108","1.11069"
"user_001","Jumping",1446047240360,"10.23212","-19.58108","0.459245"
"user_001","Jumping",1446047240424,"1.80165","-12.14694","-1.22685"
"user_001","Jumping",1446047240489,"0.575403","1.83997","0.037722"
"user_001","Jumping",1446047240553,"-0.152682","2.22318","-3.0279"
"user_001","Jumping",1446047240618,"0.268841","-1.91542","3.06503"
"user_001","Jumping",1446047240683,"1.72501","-0.535886","1.83878"
"user_001","Jumping",1446047240748,"-1.18733","0.996927","-1.26517"
"user_001","Jumping",1446047240813,"0.920286","-0.535886","-1.91661"
"user_001","Jumping",1446047240878,"4.40743","-11.61046","0.459245"
"user_001","Jumping",1446047240941,"6.78329","-19.58108","4.82776"
"user_001","Jumping",1446047241007,"9.35075","-19.58108","5.74745"
"user_001","Jumping",1446047241072,"5.71033","-15.74905","-1.15021"
"user_001","Jumping",1446047241136,"-1.57053","-1.80046","-0.15388"
"user_001","Jumping",1446047241201,"-0.267643","2.18486","-2.14654"
"user_001","Jumping",1446047241266,"2.22318","-1.80046","1.80046"
"user_001","Jumping",1446047241330,"-1.34061","0.767005","-2.03158"
"user_001","Jumping",1446047241395,"-0.382604","-0.842448","0.382604"
"user_001","Jumping",1446047241459,"-0.765807","-1.41725","0.076042"
"user_001","Jumping",1446047241525,"5.97857","-10.34589","-1.49509"
"user_001","Jumping",1446047241589,"11.34341","-19.58108","6.13065"
"user_001","Jumping",1446047241654,"10.15548","-19.58108","8.58315"
"user_001","Jumping",1446047241718,"4.67568","-11.61046","-0.307161"
"user_001","Jumping",1446047241783,"-1.37893","-0.650847","0.459245"
"user_001","Jumping",1446047241849,"-0.919089","2.6447","-3.14286"
"user_001","Jumping",1446047241913,"1.07357","-1.03405","-0.613724"
"user_001","Jumping",1446047241977,"0.15388","-0.919089","2.18366"
"user_001","Jumping",1446047242042,"-0.995729","0.460442","0.305964"
"user_001","Jumping",1446047242107,"2.03158","-0.382604","0.076042"
"user_001","Jumping",1446047242172,"6.93658","-10.84405","-0.422122"
"user_001","Jumping",1446047242237,"13.79591","-19.58108","1.49389"
"user_001","Jumping",1446047242302,"15.86521","-19.50444","-1.72501"
"user_001","Jumping",1446047242366,"4.79064","-12.03198","1.8771"
"user_001","Jumping",1446047242431,"-0.497565","1.45677","-4.10087"
"user_001","Jumping",1446047242495,"0.11556","0.613724","-1.30349"
"user_001","Jumping",1446047242560,"-0.459245","-0.305964","2.56686"
"user_001","Jumping",1446047242625,"2.29982","1.18853","-0.767005"
"user_001","Jumping",1446047242690,"1.38013","-0.305964","-1.87829"
"user_001","Jumping",1446047242754,"2.52974","-2.75846","0.152682"
"user_001","Jumping",1446047242819,"5.32712","-13.21991","-1.34181"
"user_001","Jumping",1446047242884,"10.50036","-19.58108","1.8771"
"user_001","Jumping",1446047242948,"13.02951","-19.58108","-0.498763"
"user_001","Jumping",1446047243013,"4.33079","-10.34589","-2.4531"
"user_001","Jumping",1446047243078,"0.613724","0.498763","2.49022"
"user_001","Jumping",1446047243143,"0.307161","1.53341","-2.49142"
"user_001","Jumping",1446047243207,"0.077239","-0.344284","1.91542"
"user_001","Jumping",1446047243272,"2.03158","0.1922","-0.000599"
"user_001","Jumping",1446047243337,"1.22685","0.230521","-1.87829"
"user_001","Jumping",1446047243401,"0.537083","-1.26397","0.535886"
"user_001","Jumping",1446047243467,"5.2888","-11.22725","0.689167"
"user_001","Jumping",1446047243531,"11.57333","-19.58108","0.344284"
"user_001","Jumping",1446047243596,"14.524","-19.58108","2.33694"
"user_001","Jumping",1446047243661,"3.18118","-11.8787","0.191003"
"user_001","Jumping",1446047243725,"-1.07237","1.15021","0.152682"
"user_001","Jumping",1446047243790,"0.422122","1.95493","-1.87829"
"user_001","Jumping",1446047243855,"-0.535886","-0.957409","1.18733"
"user_001","Jumping",1446047243920,"3.60271","-0.114362","0.574206"
"user_001","Jumping",1446047243984,"2.87462","-0.535886","-0.268841"
"user_001","Jumping",1446047244049,"2.2615","-1.53221","-0.11556"
"user_001","Jumping",1446047244114,"6.85994","-10.61413","-1.11189"
"user_001","Jumping",1446047244178,"7.97122","-19.58108","-0.077239"
"user_001","Jumping",1446047244243,"14.86888","-19.58108","-1.41845"
"user_001","Jumping",1446047244308,"8.50771","-14.48448","-0.613724"
"user_001","Jumping",1446047244373,"-0.535886","-1.18733","1.64717"
"user_001","Jumping",1446047244437,"-4.5212","3.37279","0.191003"
"user_001","Jumping",1446047244502,"-1.72382","0.690364","0.191003"
"user_003","Jumping",1446047778034,"-2.75846","-7.28026","-1.45677"
"user_003","Jumping",1446047778099,"-3.75479","-7.08866","-2.37646"
"user_003","Jumping",1446047778164,"-2.14534","-6.74378","-2.22318"
"user_003","Jumping",1446047778229,"-2.22198","-6.01569","-2.2615"
"user_003","Jumping",1446047778293,"-3.63983","-9.04299","-2.6447"
"user_003","Jumping",1446047778358,"-4.82776","-17.09026","-4.02423"
"user_003","Jumping",1446047778422,"-5.82409","-19.46612","-6.70665"
"user_003","Jumping",1446047778488,"0.805325","-10.92069","-1.49509"
"user_003","Jumping",1446047778552,"-4.67448","1.22685","1.64717"
"user_003","Jumping",1446047778616,"-1.37893","0.460442","-2.18486"
"user_003","Jumping",1446047778682,"0.1922","-0.650847","-0.077239"
"user_003","Jumping",1446047778746,"-0.842448","0.11556","-1.26517"
"user_003","Jumping",1446047778811,"-1.26397","-11.91702","-3.64103"
"user_003","Jumping",1446047778876,"-1.41725","-19.58108","-0.767005"
"user_003","Jumping",1446047778940,"-1.76214","-15.44249","-2.68302"
"user_003","Jumping",1446047779006,"-0.574206","-9.00468","-1.76333"
"user_003","Jumping",1446047779071,"-3.14167","-4.36792","-1.68669"
"user_003","Jumping",1446047779135,"-3.37159","-4.3296","-2.4531"
"user_003","Jumping",1446047779200,"-4.06135","-4.75112","-3.10454"
"user_003","Jumping",1446047779265,"-4.36792","-11.26557","-0.345482"
"user_003","Jumping",1446047779329,"-5.97737","-19.12124","-4.25415"
"user_003","Jumping",1446047779394,"-3.75479","-19.19788","-4.9056"
"user_003","Jumping",1446047779459,"-0.459245","-7.39522","-0.537083"
"user_003","Jumping",1446047779524,"-2.91174","3.10454","-2.37646"
"user_003","Jumping",1446047779589,"-1.41725","-0.995729","-0.230521"
"user_003","Jumping",1446047779653,"0.422122","-0.114362","0.344284"
"user_003","Jumping",1446047779718,"-0.574206","-0.535886","-1.18853"
"user_003","Jumping",1446047779783,"-1.72382","-13.98632","-4.10087"
"user_003","Jumping",1446047779847,"-3.14167","-19.58108","-3.48775"
"user_003","Jumping",1446047779912,"-2.68182","-13.21991","-3.18118"
"user_003","Jumping",1446047779977,"-0.267643","-8.92803","-2.10822"
"user_003","Jumping",1446047780042,"-3.17999","-3.98471","-0.728685"
"user_003","Jumping",1446047780107,"-2.91174","-4.48288","-2.18486"
"user_003","Jumping",1446047780171,"-3.37159","-5.78577","-3.87095"
"user_003","Jumping",1446047780236,"-4.02303","-9.84772","-0.383802"
"user_003","Jumping",1446047780301,"-6.47553","-17.81835","-4.13919"
"user_003","Jumping",1446047780365,"-3.86975","-19.58108","-5.40376"
"user_003","Jumping",1446047780430,"-1.99206","-14.44616","-3.64103"
"user_003","Jumping",1446047780495,"-2.29862","-1.18733","-0.307161"
"user_003","Jumping",1446047780559,"-1.8771","1.99325","-1.68669"
"user_003","Jumping",1446047780625,"0.000599","-0.344284","0.650847"
"user_003","Jumping",1446047780689,"-0.459245","-0.191003","-0.613724"
"user_003","Jumping",1446047780754,"-2.60518","-6.9737","-4.714"
"user_003","Jumping",1446047780818,"-1.41725","-19.58108","-2.56806"
"user_003","Jumping",1446047780884,"-3.40991","-17.51178","-4.59904"
"user_003","Jumping",1446047780948,"-1.95374","-10.61413","-2.91294"
"user_003","Jumping",1446047781013,"-3.06503","-6.70546","-1.76333"
"user_003","Jumping",1446047781078,"-2.6435","-7.20362","-1.87829"
"user_003","Jumping",1446047781143,"-2.60518","-7.28026","-1.64837"
"user_003","Jumping",1446047781208,"-1.72382","-6.51385","-2.0699"
"user_003","Jumping",1446047781272,"-0.689167","-5.55585","-2.37646"
"user_003","Jumping",1446047781337,"-3.67815","-8.00835","-1.99325"
"user_003","Jumping",1446047781402,"-5.63249","-13.64143","-3.44943"
"user_003","Jumping",1446047781467,"-5.97737","-18.16323","-5.59536"
"user_003","Jumping",1446047781531,"-2.03038","-19.2362","-5.25048"
"user_003","Jumping",1446047781596,"-2.14534","-8.88971","-1.18853"
"user_003","Jumping",1446047781660,"-3.06503","2.52974","-0.767005"
"user_003","Jumping",1446047781725,"0.230521","0.268841","-0.345482"
"user_003","Jumping",1446047781790,"0.15388","-0.535886","-0.690364"
"user_003","Jumping",1446047781855,"-0.420925","-2.18366","-2.68302"
"user_003","Jumping",1446047781919,"-3.63983","-17.47346","-4.44576"
"user_003","Jumping",1446047781985,"-2.91174","-18.35483","-4.714"
"user_003","Jumping",1446047782049,"-0.919089","-10.88237","-2.2615"
"user_003","Jumping",1446047782114,"-3.02671","-6.93538","-2.68302"
"user_003","Jumping",1446047782178,"-2.75846","-7.3569","-2.10822"
"user_003","Jumping",1446047782244,"-1.45557","-7.85507","-1.22685"
"user_003","Jumping",1446047782309,"-1.8771","-6.32225","-1.99325"
"user_003","Jumping",1446047782373,"-1.64717","-5.90073","-2.33814"
"user_003","Jumping",1446047782438,"-2.49022","-8.08499","-1.80165"
"user_003","Jumping",1446047782503,"-4.40624","-11.26557","-2.37646"
"user_003","Jumping",1446047782568,"-5.13432","-17.1669","-4.67568"
"user_003","Jumping",1446047782632,"-3.40991","-19.54276","-5.71033"
"user_003","Jumping",1446047782697,"-1.99206","-13.48815","-4.06255"
"user_003","Jumping",1446047782762,"-2.22198","-1.68549","0.267643"
"user_003","Jumping",1446047782827,"-1.91542","1.80165","-1.68669"
"user_003","Jumping",1446047782891,"0.575403","-0.152682","0.459245"
"user_003","Jumping",1446047782956,"-0.842448","-0.804128","-1.03525"
"user_003","Jumping",1446047783021,"-1.76214","-12.22358","-5.67201"
"user_003","Jumping",1446047783086,"-2.33694","-19.19788","-4.02423"
"user_003","Jumping",1446047783151,"-1.14901","-14.98264","-4.13919"
"user_003","Jumping",1446047783215,"-2.22198","-9.04299","-2.91294"
"user_003","Jumping",1446047783280,"-2.41358","-5.59417","-0.613724"
"user_003","Jumping",1446047783344,"-2.60518","-5.78577","-2.33814"
"user_003","Jumping",1446047783409,"-3.67815","-4.44456","-2.4531"
"user_003","Jumping",1446047783474,"-2.8351","-4.63616","-3.75599"
"user_003","Jumping",1446047783539,"-1.64717","-16.4005","-0.843646"
"user_003","Jumping",1446047783604,"-6.62882","-19.58108","-8.58435"
"user_003","Jumping",1446047783669,"-1.72382","-14.67608","-3.52607"
"user_003","Jumping",1446047783733,"-1.57053","1.34181","0.612526"
"user_003","Jumping",1446047783798,"-1.18733","1.22685","-1.38013"
"user_003","Jumping",1446047783863,"-0.612526","-0.612526","-0.268841"
"user_003","Jumping",1446047783928,"-1.22565","0.345482","-0.805325"
"user_003","Jumping",1446047783992,"-1.11069","-2.2603","-2.56806"
"user_003","Jumping",1446047784057,"-2.8351","-17.62674","-5.02056"
"user_003","Jumping",1446047784122,"-3.90807","-19.19788","-5.59536"
"user_003","Jumping",1446047784187,"-1.72382","-12.6451","-3.64103"
"user_003","Jumping",1446047784252,"-1.49389","-6.7821","-2.0699"
"user_003","Jumping",1446047784316,"-2.14534","-4.67448","-1.15021"
"user_003","Jumping",1446047784381,"-2.91174","-6.93538","-2.6447"
"user_003","Jumping",1446047784446,"-3.71647","-5.86241","-2.56806"
"user_003","Jumping",1446047784510,"-0.344284","-6.51385","-2.8363"
"user_003","Jumping",1446047784575,"-4.02303","-14.98264","-2.2615"
"user_003","Jumping",1446047784639,"-5.36425","-19.58108","-8.89091"
"user_003","Jumping",1446047784704,"-0.191003","-14.906","-3.94759"
"user_003","Jumping",1446047784769,"-2.4519","0.077239","1.11069"
"user_003","Jumping",1446047784834,"-2.4519","1.22685","-1.41845"
"user_003","Jumping",1446047784899,"-0.344284","0.345482","-0.15388"
"user_003","Jumping",1446047784964,"-0.842448","0.422122","-0.996927"
"user_003","Jumping",1446047785029,"-2.29862","-4.3296","-4.63736"
"user_003","Jumping",1446047785093,"-0.765807","-18.92964","-3.98591"
"user_003","Jumping",1446047785158,"-4.3296","-19.38948","-5.17384"
"user_003","Jumping",1446047785222,"-1.11069","-12.6451","-1.22685"
"user_003","Jumping",1446047785287,"-3.44823","-7.24194","-2.18486"
"user_003","Jumping",1446047785352,"-2.49022","-6.51385","-2.95126"
"user_003","Jumping",1446047785417,"-2.10702","-6.85874","-1.22685"
"user_003","Jumping",1446047785482,"-2.79678","-7.05034","-2.14654"
"user_003","Jumping",1446047785546,"-2.56686","-5.01936","-2.52974"
"user_003","Jumping",1446047785611,"-2.87342","-6.62882","-2.56806"
"user_003","Jumping",1446047785676,"-4.63616","-14.44616","-2.52974"
"user_003","Jumping",1446047785741,"-5.67081","-19.58108","-6.59169"
"user_003","Jumping",1446047785805,"-3.67815","-17.66507","-6.63001"
"user_003","Jumping",1446047785870,"-0.919089","-4.82776","-0.268841"
"user_003","Jumping",1446047785934,"-2.91174","2.87462","-2.33814"
"user_003","Jumping",1446047785999,"-0.420925","-0.420925","0.919089"
"user_003","Jumping",1446047786065,"-0.037722","0.230521","0.382604"
"user_003","Jumping",1446047786129,"-0.842448","-0.497565","-3.0279"
"user_003","Jumping",1446047786194,"-0.995729","-15.48081","-4.33079"
"user_003","Jumping",1446047786259,"-3.98471","-19.50444","-6.89825"
"user_003","Jumping",1446047786324,"-0.574206","-13.29655","-3.94759"
"user_003","Jumping",1446047786388,"-1.41725","-8.85139","-1.38013"
"user_003","Jumping",1446047786453,"-3.94639","-5.59417","-3.14286"
"user_003","Jumping",1446047786518,"-2.18366","-8.04667","-3.06622"
"user_003","Jumping",1446047786582,"-1.60885","-8.92803","-1.15021"
"user_003","Jumping",1446047786647,"-2.72014","-5.13432","-2.33814"
"user_003","Jumping",1446047786712,"-2.03038","-4.25296","-3.18118"
"user_003","Jumping",1446047786777,"-3.48655","-9.84772","-1.45677"
"user_003","Jumping",1446047786841,"-6.01569","-18.96796","-6.78329"
"user_003","Jumping",1446047786906,"-4.44456","-19.4278","-8.62267"
"user_003","Jumping",1446047786970,"-0.267643","-8.92803","-1.57173"
"user_003","Jumping",1446047787035,"-2.91174","3.41111","-1.53341"
"user_003","Jumping",1446047787100,"0.268841","-0.765807","0.689167"
"user_003","Jumping",1446047787165,"-0.650847","0.383802","-0.728685"
"user_003","Jumping",1446047787230,"-0.880768","0.422122","-1.03525"
"user_003","Jumping",1446047787294,"-1.60885","-11.11229","-5.4804"
"user_003","Jumping",1446047787358,"-4.59784","-19.35116","-5.94025"
"user_003","Jumping",1446047787424,"-2.41358","-15.71073","-5.74865"
"user_003","Jumping",1446047787489,"-1.26397","-9.34956","-3.06622"
"user_003","Jumping",1446047787553,"-3.44823","-6.47553","-2.75966"
"user_003","Jumping",1446047787618,"-2.4519","-7.97003","-2.2615"
"user_003","Jumping",1446047787683,"-1.95374","-7.81675","-1.83997"
"user_003","Jumping",1446047787747,"-2.75846","-4.67448","-3.06622"
"user_003","Jumping",1446047787812,"-2.22198","-5.24928","-2.0699"
"user_003","Jumping",1446047787877,"-3.29495","-12.41518","-1.49509"
"user_003","Jumping",1446047787942,"-5.44089","-19.46612","-6.20849"
"user_003","Jumping",1446047788007,"-3.79311","-18.69972","-7.70298"
"user_003","Jumping",1446047788071,"-0.382604","-7.1653","-1.83997"
"user_003","Jumping",1446047788136,"-3.14167","2.72134","-0.575403"
"user_003","Jumping",1446047788201,"0.460442","-0.152682","0.114362"
"user_003","Jumping",1446047788265,"-0.842448","0.652044","-0.230521"
"user_003","Jumping",1446047788331,"-2.2603","-0.229323","-1.45677"
"user_003","Jumping",1446047788395,"-1.45557","-13.71807","-6.63001"
"user_003","Jumping",1446047788460,"-3.14167","-19.54276","-6.32345"
"user_003","Jumping",1446047788524,"-1.83878","-13.06663","-4.40743"
"user_003","Jumping",1446047788590,"-1.83878","-9.77108","-3.25783"
"user_003","Jumping",1446047788654,"-3.14167","-6.55217","-2.6447"
"user_003","Jumping",1446047788719,"-2.10702","-7.31858","-2.10822"
"user_003","Jumping",1446047788784,"-2.14534","-5.90073","-2.0699"
"user_003","Jumping",1446047788848,"-1.57053","-4.9044","-2.75966"
"user_003","Jumping",1446047788913,"-2.75846","-7.5485","-1.76333"
"user_003","Jumping",1446047788978,"-4.98104","-15.97897","-4.67568"
"user_003","Jumping",1446047789043,"-3.10335","-19.58108","-8.16283"
"user_003","Jumping",1446047789107,"-1.68549","-16.36218","-5.25048"
"user_003","Jumping",1446047789172,"-2.52854","-2.0687","-0.345482"
"user_003","Jumping",1446047789236,"-1.72382","2.41478","-2.37646"
"user_003","Jumping",1446047789301,"0.843646","-0.382604","1.11069"
"user_003","Jumping",1446047789366,"-0.689167","0.230521","-0.728685"
"user_003","Jumping",1446047789431,"-1.14901","-1.95374","-2.72134"
"user_003","Jumping",1446047789495,"-3.90807","-17.74171","-7.58802"
"user_003","Jumping",1446047789560,"-3.29495","-18.96796","-5.05888"
"user_003","Jumping",1446047789625,"-2.22198","-12.0703","-3.98591"
"user_003","Jumping",1446047789690,"-2.79678","-7.7401","-2.37646"
"user_003","Jumping",1446047789755,"-1.95374","-5.74745","-2.22318"
"user_003","Jumping",1446047789818,"-2.03038","-6.85874","-1.68669"
"user_003","Jumping",1446047789883,"-2.91174","-5.67081","-2.52974"
"user_003","Jumping",1446047789949,"-1.49389","-4.94272","-2.75966"
"user_003","Jumping",1446047790013,"-3.37159","-10.88237","-1.57173"
"user_003","Jumping",1446047790078,"-5.51753","-19.58108","-7.24314"
"user_003","Jumping",1446047790143,"-4.48288","-17.5501","-6.9749"
"user_003","Jumping",1446047790207,"-0.612526","-2.68182","-0.613724"
"user_003","Jumping",1446047790272,"-1.8771","2.68302","-2.03158"
"user_003","Jumping",1446047790337,"1.15021","-0.919089","1.64717"
"user_003","Jumping",1446047790402,"-2.03038","0.307161","-1.22685"
"user_003","Jumping",1446047790467,"-1.34061","-0.191003","-1.61005"
"user_003","Jumping",1446047790531,"-3.02671","-13.64143","-5.17384"
"user_003","Jumping",1446047790596,"-4.75112","-19.58108","-7.62634"
"user_003","Jumping",1446047790660,"-2.60518","-15.05928","-6.01689"
"user_003","Jumping",1446047790726,"-1.26397","-8.12331","-3.10454"
"user_003","Jumping",1446047790790,"-3.25663","-5.97737","-3.0279"
"user_003","Jumping",1446047790855,"-0.420925","-6.24561","-2.2615"
"user_003","Jumping",1446047790919,"-1.83878","-3.40991","-3.60271"
"user_003","Jumping",1446047790984,"-3.94639","-5.36425","-3.75599"
"user_003","Jumping",1446047791049,"-2.2603","-13.98632","-3.67935"
"user_003","Jumping",1446047791114,"-7.70178","-19.58108","-7.5497"
"user_003","Jumping",1446047791179,"-2.52854","-15.36585","-6.66833"
"user_003","Jumping",1446047791243,"-0.574206","-0.497565","-0.230521"
"user_003","Jumping",1446047791308,"-2.0687","0.958607","-0.575403"
"user_003","Jumping",1446047791373,"0.11556","-0.497565","0.344284"
"user_003","Jumping",1446047791438,"-1.30229","1.34181","-1.11189"
"user_003","Jumping",1446047791503,"-0.382604","-0.880768","-2.22318"
"user_003","Jumping",1446047791568,"-2.87342","-15.97897","-6.28513"
"user_003","Jumping",1446047791632,"-2.95007","-19.38948","-7.51138"
"user_003","Jumping",1446047791697,"-4.59784","-13.37319","-5.02056"
"user_003","Jumping",1446047791762,"-2.14534","-9.84772","-3.2195"
"user_003","Jumping",1446047791827,"-2.29862","-6.55217","-2.49142"
"user_003","Jumping",1446047791891,"-1.68549","-7.47186","-2.29982"
"user_003","Jumping",1446047791956,"-1.22565","-8.16163","-2.56806"
"user_003","Jumping",1446047792021,"-2.95007","-5.096","-2.52974"
"user_003","Jumping",1446047792085,"-4.02303","-4.06135","-7.7413"
"user_003","Jumping",1446047792151,"-2.60518","-14.40784","0.727487"
"user_003","Jumping",1446047792215,"-6.24561","-19.58108","-8.89091"
"user_003","Jumping",1446047792280,"-1.60885","-12.56846","-3.2195"
"user_003","Jumping",1446047792344,"-3.10335","3.14286","-1.26517"
"user_003","Jumping",1446047792410,"-0.037722","0.000599","-0.11556"
"user_003","Jumping",1446047792474,"0.268841","-0.650847","0.114362"
"user_003","Jumping",1446047792539,"-2.68182","0.996927","-1.99325"
"user_003","Jumping",1446047792603,"-1.64717","-4.67448","-5.51872"
"user_003","Jumping",1446047792669,"-2.87342","-18.96796","-5.86361"
"user_003","Jumping",1446047792733,"-3.29495","-18.66139","-4.44576"
"user_003","Jumping",1446047792798,"-4.02303","-13.21991","-4.79064"
"user_003","Jumping",1446047792863,"-2.95007","-7.97003","-2.79798"
"user_003","Jumping",1446047792928,"-3.17999","-6.20729","-2.29982"
"user_003","Jumping",1446047792992,"-3.25663","-5.40257","-2.37646"
"user_003","Jumping",1446047793057,"-3.40991","-4.5212","-3.18118"
"user_003","Jumping",1446047793122,"-3.29495","-3.90807","-3.94759"
"user_003","Jumping",1446047793186,"-4.7128","-14.7144","-2.75966"
"user_003","Jumping",1446047793251,"-4.55952","-19.58108","-7.77962"
"user_003","Jumping",1446047793316,"-2.95007","-16.32385","-6.93658"
"user_003","Jumping",1446047793380,"-1.26397","-2.14534","-1.38013"
"user_003","Jumping",1446047793445,"-2.10702","2.33814","-1.64837"
"user_003","Jumping",1446047793510,"-0.420925","-0.267643","1.07237"
"user_003","Jumping",1446047793575,"-0.995729","0.383802","-0.537083"
"user_003","Jumping",1446047793640,"-1.14901","-1.76214","-2.29982"
"user_003","Jumping",1446047793704,"-2.56686","-14.82936","-6.28513"
"user_003","Jumping",1446047793768,"-3.40991","-19.31284","-7.3581"
"user_003","Jumping",1446047793834,"-3.52487","-13.87135","-5.63368"
"user_003","Jumping",1446047793899,"-2.68182","-10.84405","-3.10454"
"user_003","Jumping",1446047793963,"-2.72014","-4.78944","-2.72134"
"user_003","Jumping",1446047794028,"-1.03405","-4.94272","-1.68669"
"user_003","Jumping",1446047794093,"-2.10702","-5.93905","-2.14654"
"user_003","Jumping",1446047794158,"-1.57053","-5.90073","-3.2195"
"user_003","Jumping",1446047794222,"-4.3296","-8.08499","-1.15021"
"user_003","Jumping",1446047794287,"-4.48288","-19.08292","-6.36177"
"user_003","Jumping",1446047794352,"-4.67448","-19.58108","-8.27779"
"user_003","Jumping",1446047794416,"0.000599","-10.76741","-2.33814"
"user_003","Jumping",1446047794481,"-1.95374","3.41111","-1.07357"
"user_003","Jumping",1446047794546,"-0.152682","-0.689167","0.229323"
"user_003","Jumping",1446047794611,"-0.957409","0.422122","-0.460442"
"user_003","Jumping",1446047794676,"-0.957409","0.996927","-0.881966"
"user_003","Jumping",1446047794740,"-1.57053","-3.60151","-4.75232"
"user_003","Jumping",1446047794805,"-1.99206","-18.35483","-5.78697"
"user_003","Jumping",1446047794870,"-3.71647","-19.2362","-6.47673"
"user_003","Jumping",1446047794935,"-0.842448","-14.94432","-4.17751"
"user_002","Standing",1446309439342,"-2.49022","-9.19628","-1.11189"
"user_002","Standing",1446309439349,"-2.41358","-9.15796","-1.22685"
"user_002","Standing",1446309439358,"-2.56686","-9.15796","-1.30349"
"user_002","Standing",1446309439365,"-2.75846","-9.08132","-1.11189"
"user_002","Standing",1446309439373,"-2.75846","-9.08132","-1.15021"
"user_002","Standing",1446309439382,"-2.8351","-9.15796","-0.920286"
"user_002","Standing",1446309439390,"-2.8357","-9.15855","-0.919687"
"user_002","Standing",1446309439398,"-2.95007","-9.2346","-0.881966"
"user_002","Standing",1446309439406,"-2.8351","-9.11964","-0.958607"
"user_002","Standing",1446309439414,"-2.8351","-8.96635","-1.15021"
"user_002","Standing",1446309439422,"-2.95007","-9.08132","-0.920286"
"user_002","Standing",1446309439430,"-2.95007","-8.88971","-0.996927"
"user_002","Standing",1446309439438,"-2.91174","-9.00468","-1.15021"
"user_002","Standing",1446309439447,"-3.02671","-8.96635","-0.996927"
"user_002","Standing",1446309439454,"-2.79678","-8.96635","-1.15021"
"user_002","Standing",1446309439463,"-2.87342","-9.08132","-1.34181"
"user_002","Standing",1446309439471,"-2.98839","-9.19628","-1.22685"
"user_002","Standing",1446309439479,"-2.95007","-9.00468","-1.30349"
"user_002","Standing",1446309439487,"-2.98839","-9.00468","-1.15021"
"user_002","Standing",1446309439495,"-3.06503","-9.00468","-0.958607"
"user_002","Standing",1446309439503,"-3.02671","-9.04299","-0.881966"
"user_002","Standing",1446309439511,"-2.91174","-9.00468","-0.920286"
"user_002","Standing",1446309439519,"-2.95007","-9.08132","-0.767005"
"user_002","Standing",1446309439527,"-2.8351","-9.00468","-0.958607"
"user_002","Standing",1446309439536,"-2.95007","-9.00468","-0.996927"
"user_002","Standing",1446309439543,"-2.75846","-9.04299","-0.690364"
"user_002","Standing",1446309439552,"-2.6435","-9.00468","-1.03525"
"user_002","Standing",1446309439560,"-2.72014","-9.00468","-1.15021"
"user_002","Standing",1446309439568,"-2.68182","-9.04299","-1.11189"
"user_002","Standing",1446309439576,"-2.60518","-9.00468","-1.11189"
"user_002","Standing",1446309439584,"-2.52854","-8.88971","-1.15021"
"user_002","Standing",1446309439593,"-2.6435","-8.92803","-1.03525"
"user_002","Standing",1446309439601,"-2.60518","-8.96635","-0.996927"
"user_002","Standing",1446309439609,"-2.6435","-9.04299","-0.996927"
"user_002","Standing",1446309439616,"-2.75846","-9.04299","-0.920286"
"user_002","Standing",1446309439624,"-2.6435","-9.19628","-0.805325"
"user_002","Standing",1446309439633,"-2.79678","-8.96635","-0.767005"
"user_002","Standing",1446309439641,"-2.87342","-8.92803","-0.958607"
"user_002","Standing",1446309439649,"-2.60518","-9.11964","-0.843646"
"user_002","Standing",1446309439657,"-2.8351","-9.08132","-0.843646"
"user_002","Standing",1446309439665,"-2.8351","-9.2346","-0.920286"
"user_002","Standing",1446309439674,"-2.72014","-9.11964","-0.958607"
"user_002","Standing",1446309439682,"-2.87342","-9.15796","-1.18853"
"user_002","Standing",1446309439690,"-3.06503","-9.2346","-1.18853"
"user_002","Standing",1446309439698,"-2.8351","-9.08132","-0.996927"
"user_002","Standing",1446309439705,"-2.98839","-9.04299","-1.03525"
"user_002","Standing",1446309439713,"-3.17999","-8.96635","-1.18853"
"user_002","Standing",1446309439722,"-3.21831","-8.88971","-1.18853"
"user_002","Standing",1446309439729,"-3.17999","-8.92803","-1.15021"
"user_002","Standing",1446309439738,"-3.17999","-8.77475","-1.22685"
"user_002","Standing",1446309439746,"-3.33327","-8.65979","-1.07357"
"user_002","Standing",1446309439754,"-3.37159","-8.58315","-1.34181"
"user_002","Standing",1446309439762,"-3.33327","-8.77475","-1.18853"
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"user_002","Standing",1446309439883,"-2.91174","-8.96635","-0.881966"
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"user_002","S...
Distributed Vertex Programming using GraphX
This is an augmentation of http://go.databricks.com/hubfs/notebooks/3-GraphFrames-User-Guide-scala.html that was last retrieved in 2019.
See:
- https://amplab.cs.berkeley.edu/wp-content/uploads/2014/09/graphx.pdf
- https://amplab.github.io/graphx/
- https://spark.apache.org/docs/latest/graphx-programming-guide.html
- https://databricks.com/blog/2016/03/03/introducing-graphframes.html
- https://databricks.com/blog/2016/03/16/on-time-flight-performance-with-spark-graphframes.html
- http://ampcamp.berkeley.edu/big-data-mini-course/graph-analytics-with-graphx.html
And of course the databricks guide: * https://docs.databricks.com/spark/latest/graph-analysis/index.html
Use the source, Luke/Lea!
Community Packages in Spark - more generally
Let us recall the following quoate in Chapter 10 of High Performance Spark book (needs access to Orielly publishers via your library/subscription): - https://learning.oreilly.com/library/view/high-performance-spark/9781491943199/ch10.html#components
Beyond the integrated components, the community packages can add important functionality to Spark, sometimes even superseding built-in functionality—like with GraphFrames.
Here we introduce you to GraphFrames quickly so you don't need to drop down to the GraphX library that requires more understanding of caching and checkpointing to keep the vertex program's DAG from exploding or becoming inefficient.
GraphFrames User Guide (Scala)
GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs. It provides high-level APIs in Scala, Java, and Python. It aims to provide both the functionality of GraphX and extended functionality taking advantage of Spark DataFrames. This extended functionality includes motif finding, DataFrame-based serialization, and highly expressive graph queries.
The GraphFrames package is available from Spark Packages.
This notebook demonstrates examples from the GraphFrames User Guide: https://graphframes.github.io/graphframes/docs/_site/user-guide.html.
sc.version // link the right library depending on Spark version of the cluster that's running
// spark version 2.3.0 works with graphframes:graphframes:0.7.0-spark2.3-s_2.11
// spark version 3.0.1 works with graphframes:graphframes:0.8.1-spark3.0-s_2.12
res0: String = 3.0.1
Since databricks.com stopped allowing IFrame embeds we have to open it in a separate window now. The blog is insightful and worth a perusal:
- https://databricks.com/blog/2016/03/03/introducing-graphframes.html
// we first need to install the library - graphframes as a Spark package - and attach it to our cluster - see note two cells above!
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.graphframes._
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.graphframes._
Creating GraphFrames
Let us try to create an example social network from the blog: * https://databricks.com/blog/2016/03/03/introducing-graphframes.html.

Users can create GraphFrames from vertex and edge DataFrames.
- Vertex DataFrame: A vertex DataFrame should contain a special column named
idwhich specifies unique IDs for each vertex in the graph. - Edge DataFrame: An edge DataFrame should contain two special columns:
src(source vertex ID of edge) anddst(destination vertex ID of edge).
Both DataFrames can have arbitrary other columns. Those columns can represent vertex and edge attributes.
In our example, we can use a GraphFrame can store data or properties associated with each vertex and edge.
In our social network, each user might have an age and name, and each connection might have a relationship type.
Create the vertices and edges
// Vertex DataFrame
val v = sqlContext.createDataFrame(List(
("a", "Alice", 34),
("b", "Bob", 36),
("c", "Charlie", 30),
("d", "David", 29),
("e", "Esther", 32),
("f", "Fanny", 36),
("g", "Gabby", 60)
)).toDF("id", "name", "age")
// Edge DataFrame
val e = sqlContext.createDataFrame(List(
("a", "b", "friend"),
("b", "c", "follow"),
("c", "b", "follow"),
("f", "c", "follow"),
("e", "f", "follow"),
("e", "d", "friend"),
("d", "a", "friend"),
("a", "e", "friend")
)).toDF("src", "dst", "relationship")
v: org.apache.spark.sql.DataFrame = [id: string, name: string ... 1 more field]
e: org.apache.spark.sql.DataFrame = [src: string, dst: string ... 1 more field]
Let's create a graph from these vertices and these edges:
val g = GraphFrame(v, e)
g: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
Let's use the d3.graphs to visualise graphs (recall the D3 graphs in wiki-click example). You need the Run Cell below using that cell's Play button's drop-down menu.
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
d3.graphs.help()
Produces a force-directed graph given a collection of edges of the following form: case class Edge(src: String, dest: String, count: Long)
Usage:
import d3._
graphs.force(
height = 500,
width = 500,
clicks: Dataset[Edge])
import org.apache.spark.sql.functions.lit // import the lit function in sql
val gE= g.edges.select($"src", $"dst".as("dest"), lit(1L).as("count")) // for us the column count is just an edge incidence
import org.apache.spark.sql.functions.lit
gE: org.apache.spark.sql.DataFrame = [src: string, dest: string ... 1 more field]
display(gE)
| src | dest | count |
|---|---|---|
| a | b | 1.0 |
| b | c | 1.0 |
| c | b | 1.0 |
| f | c | 1.0 |
| e | f | 1.0 |
| e | d | 1.0 |
| d | a | 1.0 |
| a | e | 1.0 |
d3.graphs.force(
height = 500,
width = 500,
clicks = gE.as[d3.Edge])
// This example graph also comes with the GraphFrames package.
val g0 = examples.Graphs.friends
g0: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
d3.graphs.force( // let us see g0 now in one cell
height = 500,
width = 500,
clicks = g0.edges.select($"src", $"dst".as("dest"), lit(1L).as("count")).as[d3.Edge])
Basic graph and DataFrame queries
GraphFrames provide several simple graph queries, such as node degree.
Also, since GraphFrames represent graphs as pairs of vertex and edge DataFrames, it is easy to make powerful queries directly on the vertex and edge DataFrames. Those DataFrames are made available as vertices and edges fields in the GraphFrame.
Simple queries are simple
GraphFrames make it easy to express queries over graphs. Since GraphFrame vertices and edges are stored as DataFrames, many queries are just DataFrame (or SQL) queries.
display(g.vertices)
| id | name | age |
|---|---|---|
| a | Alice | 34.0 |
| b | Bob | 36.0 |
| c | Charlie | 30.0 |
| d | David | 29.0 |
| e | Esther | 32.0 |
| f | Fanny | 36.0 |
| g | Gabby | 60.0 |
display(g0.vertices) // this is the same query on the graph loaded as an example from GraphFrame package
| id | name | age |
|---|---|---|
| a | Alice | 34.0 |
| b | Bob | 36.0 |
| c | Charlie | 30.0 |
| d | David | 29.0 |
| e | Esther | 32.0 |
| f | Fanny | 36.0 |
| g | Gabby | 60.0 |
display(g.edges)
| src | dst | relationship |
|---|---|---|
| a | b | friend |
| b | c | follow |
| c | b | follow |
| f | c | follow |
| e | f | follow |
| e | d | friend |
| d | a | friend |
| a | e | friend |
The incoming degree of the vertices:
display(g.inDegrees)
| id | inDegree |
|---|---|
| c | 2.0 |
| d | 1.0 |
| e | 1.0 |
| b | 2.0 |
| f | 1.0 |
| a | 1.0 |
The outgoing degree of the vertices:
display(g.outDegrees)
| id | outDegree |
|---|---|
| a | 2.0 |
| c | 1.0 |
| e | 2.0 |
| d | 1.0 |
| b | 1.0 |
| f | 1.0 |
The degree of the vertices:
display(g.degrees)
| id | degree |
|---|---|
| b | 3.0 |
| a | 3.0 |
| c | 3.0 |
| f | 2.0 |
| e | 3.0 |
| d | 2.0 |
You can run queries directly on the vertices DataFrame. For example, we can find the age of the youngest person in the graph:
val youngest = g.vertices.groupBy().min("age")
display(youngest)
| min(age) |
|---|
| 29.0 |
Likewise, you can run queries on the edges DataFrame.
For example, let us count the number of 'follow' relationships in the graph:
val numFollows = g.edges.filter("relationship = 'follow'").count()
numFollows: Long = 4
Motif finding
More complex relationships involving edges and vertices can be built using motifs.
The following cell finds the pairs of vertices with edges in both directions between them.
The result is a dataframe, in which the column names are given by the motif keys.
Check out the GraphFrame User Guide at https://graphframes.github.io/graphframes/docs/_site/user-guide.html for more details on the API.
// Search for pairs of vertices with edges in both directions between them, i.e., find undirected or bidirected edges.
val motifs = g.find("(a)-[e1]->(b); (b)-[e2]->(a)")
display(motifs)
Since the result is a DataFrame, more complex queries can be built on top of the motif.
Let us find all the reciprocal relationships in which one person is older than 30:
val filtered = motifs.filter("b.age > 30")
display(filtered)
You Try!
//Search for all "directed triangles" or triplets of vertices: a,b,c with edges: a->b, b->c and c->a
//uncomment the next 2 lines and replace the "XXX" below
//val motifs3 = g.find("(a)-[e1]->(b); (b)-[e2]->(c); (c)-[e3]->(XXX)")
//display(motifs3)
Stateful queries
Many motif queries are stateless and simple to express, as in the examples above. The next examples demonstrate more complex queries which carry state along a path in the motif. These queries can be expressed by combining GraphFrame motif finding with filters on the result, where the filters use sequence operations to construct a series of DataFrame Columns.
For example, suppose one wishes to identify a chain of 4 vertices with some property defined by a sequence of functions. That is, among chains of 4 vertices a->b->c->d, identify the subset of chains matching this complex filter:
- Initialize state on path.
- Update state based on vertex a.
- Update state based on vertex b.
- Etc. for c and d.
- If final state matches some condition, then the chain is accepted by the filter.
The below code snippets demonstrate this process, where we identify chains of 4 vertices such that at least 2 of the 3 edges are friend relationships. In this example, the state is the current count of friend edges; in general, it could be any DataFrame Column.
// Find chains of 4 vertices.
val chain4 = g.find("(a)-[ab]->(b); (b)-[bc]->(c); (c)-[cd]->(d)")
// Query on sequence, with state (cnt)
// (a) Define method for updating state given the next element of the motif.
def sumFriends(cnt: Column, relationship: Column): Column = {
when(relationship === "friend", cnt + 1).otherwise(cnt)
}
// (b) Use sequence operation to apply method to sequence of elements in motif.
// In this case, the elements are the 3 edges.
val condition = Seq("ab", "bc", "cd").
foldLeft(lit(0))((cnt, e) => sumFriends(cnt, col(e)("relationship")))
// (c) Apply filter to DataFrame.
val chainWith2Friends2 = chain4.where(condition >= 2)
display(chainWith2Friends2)
chain4
res22: org.apache.spark.sql.DataFrame = [a: struct<id: string, name: string ... 1 more field>, ab: struct<src: string, dst: string ... 1 more field> ... 5 more fields]
chain4.printSchema
root
|-- a: struct (nullable = false)
| |-- id: string (nullable = true)
| |-- name: string (nullable = true)
| |-- age: integer (nullable = false)
|-- ab: struct (nullable = false)
| |-- src: string (nullable = true)
| |-- dst: string (nullable = true)
| |-- relationship: string (nullable = true)
|-- b: struct (nullable = false)
| |-- id: string (nullable = true)
| |-- name: string (nullable = true)
| |-- age: integer (nullable = false)
|-- bc: struct (nullable = false)
| |-- src: string (nullable = true)
| |-- dst: string (nullable = true)
| |-- relationship: string (nullable = true)
|-- c: struct (nullable = false)
| |-- id: string (nullable = true)
| |-- name: string (nullable = true)
| |-- age: integer (nullable = false)
|-- cd: struct (nullable = false)
| |-- src: string (nullable = true)
| |-- dst: string (nullable = true)
| |-- relationship: string (nullable = true)
|-- d: struct (nullable = false)
| |-- id: string (nullable = true)
| |-- name: string (nullable = true)
| |-- age: integer (nullable = false)
An idea -- a diatribe into an AI security product.
Can you think of a way to use stateful queries in social media networks to find perpetrators of hate-speech online who are possibly worthy of an investigation by domain experts, say in the intelligence or security domain, for potential prosecution on charges of having incited another person to cause physical violence... This is a real problem today as Swedish law effectively prohibits certain forms of online hate-speech.
An idea for a product that can be used by Swedish security agencies?
See https://näthatsgranskaren.se/ for details of a non-profit in Sweden doing such operaitons mostly manually as of early 2020.
Subgraphs
Subgraphs are built by filtering a subset of edges and vertices. For example, the following subgraph only contains people who are friends and who are more than 30 years old.
// Select subgraph of users older than 30, and edges of type "friend"
val v2 = g.vertices.filter("age > 30")
val e2 = g.edges.filter("relationship = 'friend'")
val g2 = GraphFrame(v2, e2)
v2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: string, name: string ... 1 more field]
e2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [src: string, dst: string ... 1 more field]
g2: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
display(g2.vertices)
| id | name | age |
|---|---|---|
| a | Alice | 34.0 |
| b | Bob | 36.0 |
| e | Esther | 32.0 |
| f | Fanny | 36.0 |
| g | Gabby | 60.0 |
display(g2.edges)
| src | dst | relationship |
|---|---|---|
| a | b | friend |
| e | d | friend |
| d | a | friend |
| a | e | friend |
d3.graphs.force( // let us see g2 now in one cell
height = 500,
width = 500,
clicks = g2.edges.select($"src", $"dst".as("dest"), lit(1L).as("count")).as[d3.Edge])
Complex triplet filters
The following example shows how to select a subgraph based upon triplet filters which operate on:
- an edge and
- its src and
- dst vertices.
This example could be extended to go beyond triplets by using more complex motifs.
// Select subgraph based on edges "e" of type "follow"
// pointing from a younger user "a" to an older user "b".
val paths = g.find("(a)-[e]->(b)")
.filter("e.relationship = 'follow'")
.filter("a.age < b.age")
// "paths" contains vertex info. Extract the edges.
val e2 = paths.select("e.src", "e.dst", "e.relationship")
// In Spark 1.5+, the user may simplify this call:
// val e2 = paths.select("e.*")
// Construct the subgraph
val g2 = GraphFrame(g.vertices, e2)
paths: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [a: struct<id: string, name: string ... 1 more field>, e: struct<src: string, dst: string ... 1 more field> ... 1 more field]
e2: org.apache.spark.sql.DataFrame = [src: string, dst: string ... 1 more field]
g2: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
display(g2.vertices)
| id | name | age |
|---|---|---|
| a | Alice | 34.0 |
| b | Bob | 36.0 |
| c | Charlie | 30.0 |
| d | David | 29.0 |
| e | Esther | 32.0 |
| f | Fanny | 36.0 |
| g | Gabby | 60.0 |
display(g2.edges)
| src | dst | relationship |
|---|---|---|
| c | b | follow |
| e | f | follow |
Standard graph algorithms in GraphX conveniently via GraphFrames
GraphFrames comes with a number of standard graph algorithms built in:
- Breadth-first search (BFS)
- Connected components
- Strongly connected components
- Label Propagation Algorithm (LPA)
- PageRank
- Shortest paths
- Triangle count
Read
https://graphframes.github.io/graphframes/docs/_site/user-guide.html
Search from "Esther" for users of age < 32.
// Search from "Esther" for users of age <= 32.
val paths: DataFrame = g.bfs.fromExpr("name = 'Esther'").toExpr("age < 32").run()
display(paths)
val paths: DataFrame = g.bfs.fromExpr("name = 'Esther' OR name = 'Bob'").toExpr("age < 32").run()
display(paths)
The search may also be limited by edge filters and maximum path lengths.
val filteredPaths = g.bfs.fromExpr("name = 'Esther'").toExpr("age < 32")
.edgeFilter("relationship != 'friend'")
.maxPathLength(3)
.run()
display(filteredPaths)
Connected components
Compute the connected component membership of each vertex and return a graph with each vertex assigned a component ID.
READ https://graphframes.github.io/graphframes/docs/_site/user-guide.html#connected-components.
From https://graphframes.github.io/graphframes/docs/_site/user-guide.html#connected-components:-
NOTE: With GraphFrames 0.3.0 and later releases, the default Connected Components algorithm requires setting a Spark checkpoint directory. Users can revert to the old algorithm using .setAlgorithm("graphx").
Recall the following quote from Chapter 5 on Effective Transformations of the High Performance Spark Book why one needs to check-point to keep the RDD lineage DAGs from growing too large.
Types of Reuse: Cache, Persist, Checkpoint, Shuffle Files If you decide that you need to reuse your RDD, Spark provides a multitude of options for how to store the RDD. Thus it is important to understand when to use the various types of persistence.There are three primary operations that you can use to store your RDD: cache, persist, and checkpoint. In general, caching (equivalent to persisting with the in-memory storage) and persisting are most useful to avoid recomputation during one Spark job or to break RDDs with long lineages, since they keep an RDD on the executors during a Spark job. Checkpointing is most useful to prevent failures and a high cost of recomputation by saving intermediate results. Like persisting, checkpointing helps avoid computation, thus minimizing the cost of failure, and avoids recomputation by breaking the lineage graph.
sc.setCheckpointDir("/_checkpoint") // just a directory in distributed file system
val result = g.connectedComponents.run()
display(result)
| id | name | age | component |
|---|---|---|---|
| a | Alice | 34.0 | 4.12316860416e11 |
| b | Bob | 36.0 | 4.12316860416e11 |
| c | Charlie | 30.0 | 4.12316860416e11 |
| d | David | 29.0 | 4.12316860416e11 |
| e | Esther | 32.0 | 4.12316860416e11 |
| f | Fanny | 36.0 | 4.12316860416e11 |
| g | Gabby | 60.0 | 1.46028888064e11 |
Fun Exercise: Try to modify the d3.graph function to allow a visualisation of a given Sequence of component ids in the above result.
Strongly connected components
Compute the strongly connected component (SCC) of each vertex and return a graph with each vertex assigned to the SCC containing that vertex.
READ https://graphframes.github.io/graphframes/docs/_site/user-guide.html#strongly-connected-components.
val result = g.stronglyConnectedComponents.maxIter(10).run()
display(result.orderBy("component"))
| id | name | age | component |
|---|---|---|---|
| g | Gabby | 60.0 | 1.46028888064e11 |
| f | Fanny | 36.0 | 4.12316860416e11 |
| a | Alice | 34.0 | 6.70014898176e11 |
| e | Esther | 32.0 | 6.70014898176e11 |
| d | David | 29.0 | 6.70014898176e11 |
| b | Bob | 36.0 | 1.047972020224e12 |
| c | Charlie | 30.0 | 1.047972020224e12 |
Label propagation
Run static Label Propagation Algorithm for detecting communities in networks.
Each node in the network is initially assigned to its own community. At every superstep, nodes send their community affiliation to all neighbors and update their state to the mode community affiliation of incoming messages.
LPA is a standard community detection algorithm for graphs. It is very inexpensive computationally, although
- (1) convergence is not guaranteed and
- (2) one can end up with trivial solutions (all nodes are identified into a single community).
READ: https://graphframes.github.io/graphframes/docs/_site/user-guide.html#label-propagation-algorithm-lpa.
val result = g.labelPropagation.maxIter(5).run()
display(result.orderBy("label"))
| id | name | age | label |
|---|---|---|---|
| g | Gabby | 60.0 | 1.46028888064e11 |
| b | Bob | 36.0 | 1.047972020224e12 |
| e | Esther | 32.0 | 1.382979469312e12 |
| a | Alice | 34.0 | 1.382979469312e12 |
| c | Charlie | 30.0 | 1.382979469312e12 |
| f | Fanny | 36.0 | 1.46028888064e12 |
| d | David | 29.0 | 1.46028888064e12 |
PageRank
Identify important vertices in a graph based on connections.
READ: https://graphframes.github.io/graphframes/docs/_site/user-guide.html#pagerank.
// Run PageRank until convergence to tolerance "tol".
val results = g.pageRank.resetProbability(0.15).tol(0.01).run()
display(results.vertices)
| id | name | age | pagerank |
|---|---|---|---|
| b | Bob | 36.0 | 2.655507832863289 |
| e | Esther | 32.0 | 0.37085233187676075 |
| a | Alice | 34.0 | 0.44910633706538744 |
| f | Fanny | 36.0 | 0.3283606792049851 |
| g | Gabby | 60.0 | 0.1799821386239711 |
| d | David | 29.0 | 0.3283606792049851 |
| c | Charlie | 30.0 | 2.6878300011606218 |
display(results.edges)
| src | dst | relationship | weight |
|---|---|---|---|
| f | c | follow | 1.0 |
| e | f | follow | 0.5 |
| e | d | friend | 0.5 |
| d | a | friend | 1.0 |
| c | b | follow | 1.0 |
| b | c | follow | 1.0 |
| a | e | friend | 0.5 |
| a | b | friend | 0.5 |
// Run PageRank for a fixed number of iterations.
val results2 = g.pageRank.resetProbability(0.15).maxIter(10).run()
display(results2.vertices)
| id | name | age | pagerank |
|---|---|---|---|
| b | Bob | 36.0 | 2.7025217677349773 |
| e | Esther | 32.0 | 0.3613490987992571 |
| a | Alice | 34.0 | 0.4485115093698443 |
| f | Fanny | 36.0 | 0.32504910549694244 |
| g | Gabby | 60.0 | 0.17073170731707318 |
| d | David | 29.0 | 0.32504910549694244 |
| c | Charlie | 30.0 | 2.6667877057849627 |
// Run PageRank personalized for vertex "a"
val results3 = g.pageRank.resetProbability(0.15).maxIter(10).sourceId("a").run()
display(results3.vertices)
| id | name | age | pagerank |
|---|---|---|---|
| b | Bob | 36.0 | 0.3366143039702568 |
| e | Esther | 32.0 | 7.657840357273027e-2 |
| a | Alice | 34.0 | 0.17710831642683564 |
| f | Fanny | 36.0 | 3.189213697274781e-2 |
| g | Gabby | 60.0 | 0.0 |
| d | David | 29.0 | 3.189213697274781e-2 |
| c | Charlie | 30.0 | 0.3459147020846817 |
Shortest paths
Computes shortest paths to the given set of landmark vertices, where landmarks are specified by vertex ID.
READ https://graphframes.github.io/graphframes/docs/_site/user-guide.html#shortest-paths.
val paths = g.shortestPaths.landmarks(Seq("a", "d")).run()
display(paths)
g.edges.show()
+---+---+------------+
|src|dst|relationship|
+---+---+------------+
| a| b| friend|
| b| c| follow|
| c| b| follow|
| f| c| follow|
| e| f| follow|
| e| d| friend|
| d| a| friend|
| a| e| friend|
+---+---+------------+
Triangle count
Computes the number of triangles passing through each vertex.
val results = g.triangleCount.run()
display(results)
| count | id | name | age |
|---|---|---|---|
| 1.0 | a | Alice | 34.0 |
| 0.0 | b | Bob | 36.0 |
| 0.0 | c | Charlie | 30.0 |
| 1.0 | d | David | 29.0 |
| 1.0 | e | Esther | 32.0 |
| 0.0 | f | Fanny | 36.0 |
| 0.0 | g | Gabby | 60.0 |
YouTry
and undestand how the below code snippet shows how to use aggregateMessages to compute the sum of the ages of adjacent users.
import org.graphframes.{examples,GraphFrame}
import org.graphframes.lib.AggregateMessages
val g: GraphFrame = examples.Graphs.friends // get example graph
// We will use AggregateMessages utilities later, so name it "AM" for short.
val AM = AggregateMessages
// For each user, sum the ages of the adjacent users.
val msgToSrc = AM.dst("age")
val msgToDst = AM.src("age")
val agg = { g.aggregateMessages
.sendToSrc(msgToSrc) // send destination user's age to source
.sendToDst(msgToDst) // send source user's age to destination
.agg(sum(AM.msg).as("summedAges")) } // sum up ages, stored in AM.msg column
agg.show()
+---+----------+
| id|summedAges|
+---+----------+
| a| 97|
| c| 108|
| e| 99|
| d| 66|
| b| 94|
| f| 62|
+---+----------+
import org.graphframes.{examples, GraphFrame}
import org.graphframes.lib.AggregateMessages
g: org.graphframes.GraphFrame = GraphFrame(v:[id: string, name: string ... 1 more field], e:[src: string, dst: string ... 1 more field])
AM: org.graphframes.lib.AggregateMessages.type = org.graphframes.lib.AggregateMessages$@706833c8
msgToSrc: org.apache.spark.sql.Column = dst[age]
msgToDst: org.apache.spark.sql.Column = src[age]
agg: org.apache.spark.sql.DataFrame = [id: string, summedAges: bigint]
There is a lot more that can be done with aggregate messaging - let's get into belief propogation algorithm for a more complex example!
Belief propogation is a powerful computational framework for Graphical Models.
- let's dive here:
as
This provides a template for building customized BP algorithms for different types of graphical models.
Project Idea
Understand parallel belief propagation using colored fields in the Scala code linked above and also pasted below in one cell (for you to modify if you want to do it in a databricks or jupyter or zeppelin notebook) unless you want to fork and extend the github repo directly with your own example.
Then use it with necessary adaptations to be able to model your favorite interacting particle system. Don't just redo the Ising model done there!
This can be used to gain intuition for various real-world scenarios, including the mathematics in your head:
- Make a graph for contact network of a set of hosts
- A simple model of COVID spreading in an SI or SIS or SIR or other epidemic models
- this can be abstract and simply show your skills in programming, say create a random network
- or be more explicit with some assumptions about the contact process (population sampled, in one or two cities, with some assumptions on contacts during transportation, school, work, etc)
- show that you have a fully scalable simulation model that can theoretically scale to billions of hosts
The project does not have to be a recommendation to Swedish authorities! Just a start in the right direction, for instance.
Some readings that can help here include the following and references therein:
- The Transmission Process: A Combinatorial Stochastic Process for the Evolution of Transmission Trees over Networks, Raazesh Sainudiin and David Welch, Journal of Theoretical Biology, Volume 410, Pages 137–170, 10.1016/j.jtbi.2016.07.038, 2016.
Other Project Ideas
- try to do a scalable inference algorithm for one of the graphical models that you already know...
- make a large simulaiton of your favourite Finite Markov Information Exchange (FMIE) process defined by Aldous (see reference in the above linked paper)
- anything else that fancies you or your research orientation/interests and can benefit from adapting the template for the parallel belief propagation algorithm here.
If you want to do this project in databricks (or other) notebook then start by modifying the following code from the example and making it run... Then adapt... start in small steps... make a team with fellow students with complementary skills...
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.graphframes.examples
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.graphx.{Graph, VertexRDD, Edge => GXEdge}
import org.apache.spark.sql.{Column, Row, SparkSession, SQLContext}
import org.apache.spark.sql.functions.{col, lit, sum, udf, when}
import org.graphframes.GraphFrame
import org.graphframes.examples.Graphs.gridIsingModel
import org.graphframes.lib.AggregateMessages
/**
* Example code for Belief Propagation (BP)
*
* This provides a template for building customized BP algorithms for different types of
* graphical models.
*
* This example:
* - Ising model on a grid
* - Parallel Belief Propagation using colored fields
*
* Ising models are probabilistic graphical models over binary variables x,,i,,.
* Each binary variable x,,i,, corresponds to one vertex, and it may take values -1 or +1.
* The probability distribution P(X) (over all x,,i,,) is parameterized by vertex factors a,,i,,
* and edge factors b,,ij,,:
* {{{
* P(X) = (1/Z) * exp[ \sum_i a_i x_i + \sum_{ij} b_{ij} x_i x_j ]
* }}}
* where Z is the normalization constant (partition function).
* See [[https://en.wikipedia.org/wiki/Ising_model Wikipedia]] for more information on Ising models.
*
* Belief Propagation (BP) provides marginal probabilities of the values of the variables x,,i,,,
* i.e., P(x,,i,,) for each i. This allows a user to understand likely values of variables.
* See [[https://en.wikipedia.org/wiki/Belief_propagation Wikipedia]] for more information on BP.
*
* We use a batch synchronous BP algorithm, where batches of vertices are updated synchronously.
* We follow the mean field update algorithm in Slide 13 of the
* [[http://www.eecs.berkeley.edu/~wainwrig/Talks/A_GraphModel_Tutorial talk slides]] from:
* Wainwright. "Graphical models, message-passing algorithms, and convex optimization."
*
* The batches are chosen according to a coloring. For background on graph colorings for inference,
* see for example:
* Gonzalez et al. "Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees."
* AISTATS, 2011.
*
* The BP algorithm works by:
* - Coloring the graph by assigning a color to each vertex such that no neighboring vertices
* share the same color.
* - In each step of BP, update all vertices of a single color. Alternate colors.
*/
object BeliefPropagation {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder()
.appName("BeliefPropagation example")
.getOrCreate()
val sql = spark.sqlContext
// Create graphical model g of size 3 x 3.
val g = gridIsingModel(sql, 3)
println("Original Ising model:")
g.vertices.show()
g.edges.show()
// Run BP for 5 iterations.
val numIter = 5
val results = runBPwithGraphX(g, numIter)
// Display beliefs.
val beliefs = results.vertices.select("id", "belief")
println(s"Done with BP. Final beliefs after $numIter iterations:")
beliefs.show()
spark.stop()
}
/**
* Given a GraphFrame, choose colors for each vertex. No neighboring vertices will share the
* same color. The number of colors is minimized.
*
* This is written specifically for grid graphs. For non-grid graphs, it should be generalized,
* such as by using a greedy coloring scheme.
*
* @param g Grid graph generated by [[org.graphframes.examples.Graphs.gridIsingModel()]]
* @return Same graph, but with a new vertex column "color" of type Int (0 or 1)
*/
private def colorGraph(g: GraphFrame): GraphFrame = {
val colorUDF = udf { (i: Int, j: Int) => (i + j) % 2 }
val v = g.vertices.withColumn("color", colorUDF(col("i"), col("j")))
GraphFrame(v, g.edges)
}
/**
* Run Belief Propagation.
*
* This implementation of BP shows how to use GraphX's aggregateMessages method.
* It is simple to convert to and from GraphX format. This method does the following:
* - Color GraphFrame vertices for BP scheduling.
* - Convert GraphFrame to GraphX format.
* - Run BP using GraphX's aggregateMessages API.
* - Augment the original GraphFrame with the BP results (vertex beliefs).
*
* @param g Graphical model created by `org.graphframes.examples.Graphs.gridIsingModel()`
* @param numIter Number of iterations of BP to run. One iteration includes updating each
* vertex's belief once.
* @return Same graphical model, but with [[GraphFrame.vertices]] augmented with a new column
* "belief" containing P(x,,i,, = +1), the marginal probability of vertex i taking
* value +1 instead of -1.
*/
def runBPwithGraphX(g: GraphFrame, numIter: Int): GraphFrame = {
// Choose colors for vertices for BP scheduling.
val colorG = colorGraph(g)
val numColors: Int = colorG.vertices.select("color").distinct.count().toInt
// Convert GraphFrame to GraphX, and initialize beliefs.
val gx0 = colorG.toGraphX
// Schema maps for extracting attributes
val vColsMap = colorG.vertexColumnMap
val eColsMap = colorG.edgeColumnMap
// Convert vertex attributes to nice case classes.
val gx1: Graph[VertexAttr, Row] = gx0.mapVertices { case (_, attr) =>
// Initialize belief at 0.0
VertexAttr(attr.getDouble(vColsMap("a")), 0.0, attr.getInt(vColsMap("color")))
}
// Convert edge attributes to nice case classes.
val extractEdgeAttr: (GXEdge[Row] => EdgeAttr) = { e =>
EdgeAttr(e.attr.getDouble(eColsMap("b")))
}
var gx: Graph[VertexAttr, EdgeAttr] = gx1.mapEdges(extractEdgeAttr)
// Run BP for numIter iterations.
for (iter <- Range(0, numIter)) {
// For each color, have that color receive messages from neighbors.
for (color <- Range(0, numColors)) {
// Send messages to vertices of the current color.
val msgs: VertexRDD[Double] = gx.aggregateMessages(
ctx =>
// Can send to source or destination since edges are treated as undirected.
if (ctx.dstAttr.color == color) {
val msg = ctx.attr.b * ctx.srcAttr.belief
// Only send message if non-zero.
if (msg != 0) ctx.sendToDst(msg)
} else if (ctx.srcAttr.color == color) {
val msg = ctx.attr.b * ctx.dstAttr.belief
// Only send message if non-zero.
if (msg != 0) ctx.sendToSrc(msg)
},
_ + _)
// Receive messages, and update beliefs for vertices of the current color.
gx = gx.outerJoinVertices(msgs) {
case (vID, vAttr, optMsg) =>
if (vAttr.color == color) {
val x = vAttr.a + optMsg.getOrElse(0.0)
val newBelief = math.exp(-log1pExp(-x))
VertexAttr(vAttr.a, newBelief, color)
} else {
vAttr
}
}
}
}
// Convert back to GraphFrame with a new column "belief" for vertices DataFrame.
val gxFinal: Graph[Double, Unit] = gx.mapVertices((_, attr) => attr.belief).mapEdges(_ => ())
GraphFrame.fromGraphX(colorG, gxFinal, vertexNames = Seq("belief"))
}
case class VertexAttr(a: Double, belief: Double, color: Int)
case class EdgeAttr(b: Double)
/**
* Run Belief Propagation.
*
* This implementation of BP shows how to use GraphFrame's aggregateMessages method.
* - Color GraphFrame vertices for BP scheduling.
* - Run BP using GraphFrame's aggregateMessages API.
* - Augment the original GraphFrame with the BP results (vertex beliefs).
*
* @param g Graphical model created by `org.graphframes.examples.Graphs.gridIsingModel()`
* @param numIter Number of iterations of BP to run. One iteration includes updating each
* vertex's belief once.
* @return Same graphical model, but with [[GraphFrame.vertices]] augmented with a new column
* "belief" containing P(x,,i,, = +1), the marginal probability of vertex i taking
* value +1 instead of -1.
*/
def runBPwithGraphFrames(g: GraphFrame, numIter: Int): GraphFrame = {
// Choose colors for vertices for BP scheduling.
val colorG = colorGraph(g)
val numColors: Int = colorG.vertices.select("color").distinct.count().toInt
// TODO: Handle vertices without any edges.
// Initialize vertex beliefs at 0.0.
var gx = GraphFrame(colorG.vertices.withColumn("belief", lit(0.0)), colorG.edges)
// Run BP for numIter iterations.
for (iter <- Range(0, numIter)) {
// For each color, have that color receive messages from neighbors.
for (color <- Range(0, numColors)) {
// Define "AM" for shorthand for referring to the src, dst, edge, and msg fields.
// (See usage below.)
val AM = AggregateMessages
// Send messages to vertices of the current color.
// We may send to source or destination since edges are treated as undirected.
val msgForSrc: Column = when(AM.src("color") === color, AM.edge("b") * AM.dst("belief"))
val msgForDst: Column = when(AM.dst("color") === color, AM.edge("b") * AM.src("belief"))
val logistic = udf { (x: Double) => math.exp(-log1pExp(-x)) }
val aggregates = gx.aggregateMessages
.sendToSrc(msgForSrc)
.sendToDst(msgForDst)
.agg(sum(AM.msg).as("aggMess"))
val v = gx.vertices
// Receive messages, and update beliefs for vertices of the current color.
val newBeliefCol = when(v("color") === color && aggregates("aggMess").isNotNull,
logistic(aggregates("aggMess") + v("a")))
.otherwise(v("belief")) // keep old beliefs for other colors
val newVertices = v
.join(aggregates, v("id") === aggregates("id"), "left_outer") // join messages, vertices
.drop(aggregates("id")) // drop duplicate ID column (from outer join)
.withColumn("newBelief", newBeliefCol) // compute new beliefs
.drop("aggMess") // drop messages
.drop("belief") // drop old beliefs
.withColumnRenamed("newBelief", "belief")
// Cache new vertices using workaround for SPARK-13346
val cachedNewVertices = AM.getCachedDataFrame(newVertices)
gx = GraphFrame(cachedNewVertices, gx.edges)
}
}
// Drop the "color" column from vertices
GraphFrame(gx.vertices.drop("color"), gx.edges)
}
/** More numerically stable `log(1 + exp(x))` */
private def log1pExp(x: Double): Double = {
if (x > 0) {
x + math.log1p(math.exp(-x))
} else {
math.log1p(math.exp(x))
}
}
}
This is a scala version of the python notebook in the following talk:
Homework:
See https://www.brighttalk.com/webcast/12891/199003 (you need to subscribe freely to Bright Talk first). Then go through this scala version of the notebook from the talk.
On-Time Flight Performance with GraphFrames for Apache Spark
This notebook provides an analysis of On-Time Flight Performance and Departure Delays data using GraphFrames for Apache Spark.
Source Data:
- OpenFlights: Airport, airline and route data
- United States Department of Transportation: Bureau of Transportation Statistics (TranStats)
- Note, the data used here was extracted from the US DOT:BTS between 1/1/2014 and 3/31/2014*
References:
Preparation
Extract the Airports and Departure Delays information from S3 / DBFS
// Set File Paths
val tripdelaysFilePath = "/databricks-datasets/flights/departuredelays.csv"
val airportsnaFilePath = "/databricks-datasets/flights/airport-codes-na.txt"
tripdelaysFilePath: String = /databricks-datasets/flights/departuredelays.csv
airportsnaFilePath: String = /databricks-datasets/flights/airport-codes-na.txt
// Obtain airports dataset
// Note that "spark-csv" package is built-in datasource in Spark 2.0
val airportsna = sqlContext.read.format("com.databricks.spark.csv").
option("header", "true").
option("inferschema", "true").
option("delimiter", "\t").
load(airportsnaFilePath)
airportsna.createOrReplaceTempView("airports_na")
// Obtain departure Delays data
val departureDelays = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").load(tripdelaysFilePath)
departureDelays.createOrReplaceTempView("departureDelays")
departureDelays.cache()
// Available IATA (International Air Transport Association) codes from the departuredelays sample dataset
val tripIATA = sqlContext.sql("select distinct iata from (select distinct origin as iata from departureDelays union all select distinct destination as iata from departureDelays) a")
tripIATA.createOrReplaceTempView("tripIATA")
// Only include airports with atleast one trip from the departureDelays dataset
val airports = sqlContext.sql("select f.IATA, f.City, f.State, f.Country from airports_na f join tripIATA t on t.IATA = f.IATA")
airports.createOrReplaceTempView("airports")
airports.cache()
airportsna: org.apache.spark.sql.DataFrame = [City: string, State: string ... 2 more fields]
departureDelays: org.apache.spark.sql.DataFrame = [date: string, delay: string ... 3 more fields]
tripIATA: org.apache.spark.sql.DataFrame = [iata: string]
airports: org.apache.spark.sql.DataFrame = [IATA: string, City: string ... 2 more fields]
res0: airports.type = [IATA: string, City: string ... 2 more fields]
// Build `departureDelays_geo` DataFrame
// Obtain key attributes such as Date of flight, delays, distance, and airport information (Origin, Destination)
val departureDelays_geo = sqlContext.sql("select cast(f.date as int) as tripid, cast(concat(concat(concat(concat(concat(concat('2014-', concat(concat(substr(cast(f.date as string), 1, 2), '-')), substr(cast(f.date as string), 3, 2)), ' '), substr(cast(f.date as string), 5, 2)), ':'), substr(cast(f.date as string), 7, 2)), ':00') as timestamp) as `localdate`, cast(f.delay as int), cast(f.distance as int), f.origin as src, f.destination as dst, o.city as city_src, d.city as city_dst, o.state as state_src, d.state as state_dst from departuredelays f join airports o on o.iata = f.origin join airports d on d.iata = f.destination")
// RegisterTempTable
departureDelays_geo.createOrReplaceTempView("departureDelays_geo")
// Cache and Count
departureDelays_geo.cache()
departureDelays_geo.count()
departureDelays_geo: org.apache.spark.sql.DataFrame = [tripid: int, localdate: timestamp ... 8 more fields]
res2: Long = 1361141
display(departureDelays_geo)
| tripid | localdate | delay | distance | src | dst | city_src | city_dst | state_src | state_dst |
|---|---|---|---|---|---|---|---|---|---|
| 1011111.0 | 2014-01-01T11:11:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1021111.0 | 2014-01-02T11:11:00.000+0000 | 7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1031111.0 | 2014-01-03T11:11:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1041925.0 | 2014-01-04T19:25:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1061115.0 | 2014-01-06T11:15:00.000+0000 | 33.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1071115.0 | 2014-01-07T11:15:00.000+0000 | 23.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1081115.0 | 2014-01-08T11:15:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1091115.0 | 2014-01-09T11:15:00.000+0000 | 11.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1101115.0 | 2014-01-10T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1112015.0 | 2014-01-11T20:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1121925.0 | 2014-01-12T19:25:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1131115.0 | 2014-01-13T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1141115.0 | 2014-01-14T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1151115.0 | 2014-01-15T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1161115.0 | 2014-01-16T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1171115.0 | 2014-01-17T11:15:00.000+0000 | 4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1182015.0 | 2014-01-18T20:15:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1191925.0 | 2014-01-19T19:25:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1201115.0 | 2014-01-20T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1211115.0 | 2014-01-21T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1221115.0 | 2014-01-22T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1231115.0 | 2014-01-23T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1241115.0 | 2014-01-24T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1252015.0 | 2014-01-25T20:15:00.000+0000 | -12.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1261925.0 | 2014-01-26T19:25:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1271115.0 | 2014-01-27T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1281115.0 | 2014-01-28T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1291115.0 | 2014-01-29T11:15:00.000+0000 | -2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1301115.0 | 2014-01-30T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 1311115.0 | 2014-01-31T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2012015.0 | 2014-02-01T20:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2022015.0 | 2014-02-02T20:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2031115.0 | 2014-02-03T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2041115.0 | 2014-02-04T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2051115.0 | 2014-02-05T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2061115.0 | 2014-02-06T11:15:00.000+0000 | -2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2071115.0 | 2014-02-07T11:15:00.000+0000 | -15.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2082015.0 | 2014-02-08T20:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2091925.0 | 2014-02-09T19:25:00.000+0000 | 1.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2101115.0 | 2014-02-10T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2111115.0 | 2014-02-11T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2121115.0 | 2014-02-12T11:15:00.000+0000 | -2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2131115.0 | 2014-02-13T11:15:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2141115.0 | 2014-02-14T11:15:00.000+0000 | -11.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2152015.0 | 2014-02-15T20:15:00.000+0000 | 16.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2161925.0 | 2014-02-16T19:25:00.000+0000 | 169.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2171115.0 | 2014-02-17T11:15:00.000+0000 | 27.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2181115.0 | 2014-02-18T11:15:00.000+0000 | 96.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2191115.0 | 2014-02-19T11:15:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2201115.0 | 2014-02-20T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2211115.0 | 2014-02-21T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2222015.0 | 2014-02-22T20:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2231925.0 | 2014-02-23T19:25:00.000+0000 | -3.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2241115.0 | 2014-02-24T11:15:00.000+0000 | -2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2251115.0 | 2014-02-25T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2261115.0 | 2014-02-26T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2271115.0 | 2014-02-27T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2281115.0 | 2014-02-28T11:15:00.000+0000 | 5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3012015.0 | 2014-03-01T20:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3022000.0 | 2014-03-02T20:00:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3031115.0 | 2014-03-03T11:15:00.000+0000 | 17.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3041115.0 | 2014-03-04T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3051115.0 | 2014-03-05T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3061115.0 | 2014-03-06T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3071115.0 | 2014-03-07T11:15:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3082000.0 | 2014-03-08T20:00:00.000+0000 | -11.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3092000.0 | 2014-03-09T20:00:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3101115.0 | 2014-03-10T11:15:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3111115.0 | 2014-03-11T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3121115.0 | 2014-03-12T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3131115.0 | 2014-03-13T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3141115.0 | 2014-03-14T11:15:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3152000.0 | 2014-03-15T20:00:00.000+0000 | -11.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3162000.0 | 2014-03-16T20:00:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3171115.0 | 2014-03-17T11:15:00.000+0000 | 25.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3181115.0 | 2014-03-18T11:15:00.000+0000 | 2.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3191115.0 | 2014-03-19T11:15:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3201115.0 | 2014-03-20T11:15:00.000+0000 | -6.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3211115.0 | 2014-03-21T11:15:00.000+0000 | 0.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3222000.0 | 2014-03-22T20:00:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3232000.0 | 2014-03-23T20:00:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3241115.0 | 2014-03-24T11:15:00.000+0000 | -9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3251115.0 | 2014-03-25T11:15:00.000+0000 | -4.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3261115.0 | 2014-03-26T11:15:00.000+0000 | -5.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3271115.0 | 2014-03-27T11:15:00.000+0000 | 9.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3281115.0 | 2014-03-28T11:15:00.000+0000 | -7.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3292000.0 | 2014-03-29T20:00:00.000+0000 | -19.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3302000.0 | 2014-03-30T20:00:00.000+0000 | -10.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 3311115.0 | 2014-03-31T11:15:00.000+0000 | -8.0 | 221.0 | MSP | INL | Minneapolis | International Falls | MN | MN |
| 2011230.0 | 2014-02-01T12:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2010719.0 | 2014-02-01T07:19:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2021230.0 | 2014-02-02T12:30:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2020709.0 | 2014-02-02T07:09:00.000+0000 | 59.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2021654.0 | 2014-02-02T16:54:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2030719.0 | 2014-02-03T07:19:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2031659.0 | 2014-02-03T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2031230.0 | 2014-02-03T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2032043.0 | 2014-02-03T20:43:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2041230.0 | 2014-02-04T12:30:00.000+0000 | 24.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2040719.0 | 2014-02-04T07:19:00.000+0000 | 168.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2041730.0 | 2014-02-04T17:30:00.000+0000 | 88.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2042043.0 | 2014-02-04T20:43:00.000+0000 | 106.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2051659.0 | 2014-02-05T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2050719.0 | 2014-02-05T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2050929.0 | 2014-02-05T09:29:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2052043.0 | 2014-02-05T20:43:00.000+0000 | 46.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2060719.0 | 2014-02-06T07:19:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2061659.0 | 2014-02-06T16:59:00.000+0000 | 82.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2061230.0 | 2014-02-06T12:30:00.000+0000 | 61.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2062043.0 | 2014-02-06T20:43:00.000+0000 | 7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2070719.0 | 2014-02-07T07:19:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2071659.0 | 2014-02-07T16:59:00.000+0000 | 19.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2071230.0 | 2014-02-07T12:30:00.000+0000 | 27.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2072048.0 | 2014-02-07T20:48:00.000+0000 | 47.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2081230.0 | 2014-02-08T12:30:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2080719.0 | 2014-02-08T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2091229.0 | 2014-02-09T12:29:00.000+0000 | 95.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2091654.0 | 2014-02-09T16:54:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2090709.0 | 2014-02-09T07:09:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2092043.0 | 2014-02-09T20:43:00.000+0000 | 32.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2100719.0 | 2014-02-10T07:19:00.000+0000 | 14.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2101659.0 | 2014-02-10T16:59:00.000+0000 | 16.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2101230.0 | 2014-02-10T12:30:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2102043.0 | 2014-02-10T20:43:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2111230.0 | 2014-02-11T12:30:00.000+0000 | 10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2110719.0 | 2014-02-11T07:19:00.000+0000 | 46.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2111730.0 | 2014-02-11T17:30:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2112043.0 | 2014-02-11T20:43:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2120719.0 | 2014-02-12T07:19:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2121230.0 | 2014-02-12T12:30:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2120929.0 | 2014-02-12T09:29:00.000+0000 | 89.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2122043.0 | 2014-02-12T20:43:00.000+0000 | 36.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2130738.0 | 2014-02-13T07:38:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2132041.0 | 2014-02-13T20:41:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2140729.0 | 2014-02-14T07:29:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2142041.0 | 2014-02-14T20:41:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2151206.0 | 2014-02-15T12:06:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2150659.0 | 2014-02-15T06:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2160705.0 | 2014-02-16T07:05:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2170729.0 | 2014-02-17T07:29:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2172041.0 | 2014-02-17T20:41:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2180738.0 | 2014-02-18T07:38:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2182041.0 | 2014-02-18T20:41:00.000+0000 | 36.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2190727.0 | 2014-02-19T07:27:00.000+0000 | 15.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2200738.0 | 2014-02-20T07:38:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2202041.0 | 2014-02-20T20:41:00.000+0000 | 51.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2210729.0 | 2014-02-21T07:29:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2212041.0 | 2014-02-21T20:41:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2221206.0 | 2014-02-22T12:06:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2220659.0 | 2014-02-22T06:59:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2232041.0 | 2014-02-23T20:41:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2230705.0 | 2014-02-23T07:05:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2240729.0 | 2014-02-24T07:29:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2242041.0 | 2014-02-24T20:41:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2250738.0 | 2014-02-25T07:38:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2260727.0 | 2014-02-26T07:27:00.000+0000 | 23.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2262041.0 | 2014-02-26T20:41:00.000+0000 | 174.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2270738.0 | 2014-02-27T07:38:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2272041.0 | 2014-02-27T20:41:00.000+0000 | 32.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2280729.0 | 2014-02-28T07:29:00.000+0000 | 12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2282041.0 | 2014-02-28T20:41:00.000+0000 | 49.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2281000.0 | 2014-02-28T10:00:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2051230.0 | 2014-02-05T12:30:00.000+0000 | 216.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2131536.0 | 2014-02-13T15:36:00.000+0000 | 273.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2141536.0 | 2014-02-14T15:36:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2151902.0 | 2014-02-15T19:02:00.000+0000 | 31.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2151536.0 | 2014-02-15T15:36:00.000+0000 | 66.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2162041.0 | 2014-02-16T20:41:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2161536.0 | 2014-02-16T15:36:00.000+0000 | 7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2171536.0 | 2014-02-17T15:36:00.000+0000 | 3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2181536.0 | 2014-02-18T15:36:00.000+0000 | 26.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2191536.0 | 2014-02-19T15:36:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2201536.0 | 2014-02-20T15:36:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2211536.0 | 2014-02-21T15:36:00.000+0000 | 34.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2221900.0 | 2014-02-22T19:00:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2221536.0 | 2014-02-22T15:36:00.000+0000 | 65.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2231536.0 | 2014-02-23T15:36:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2241536.0 | 2014-02-24T15:36:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2251536.0 | 2014-02-25T15:36:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2261536.0 | 2014-02-26T15:36:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2271536.0 | 2014-02-27T15:36:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2281536.0 | 2014-02-28T15:36:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2010730.0 | 2014-02-01T07:30:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2021815.0 | 2014-02-02T18:15:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2031815.0 | 2014-02-03T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2041815.0 | 2014-02-04T18:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2051815.0 | 2014-02-05T18:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2061815.0 | 2014-02-06T18:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2071815.0 | 2014-02-07T18:15:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2080730.0 | 2014-02-08T07:30:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2091815.0 | 2014-02-09T18:15:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2101815.0 | 2014-02-10T18:15:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2111815.0 | 2014-02-11T18:15:00.000+0000 | 4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2121815.0 | 2014-02-12T18:15:00.000+0000 | 64.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2131805.0 | 2014-02-13T18:05:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2141635.0 | 2014-02-14T16:35:00.000+0000 | 52.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2150730.0 | 2014-02-15T07:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2161635.0 | 2014-02-16T16:35:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2171635.0 | 2014-02-17T16:35:00.000+0000 | 23.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2181635.0 | 2014-02-18T16:35:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2191635.0 | 2014-02-19T16:35:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2201635.0 | 2014-02-20T16:35:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2211635.0 | 2014-02-21T16:35:00.000+0000 | 292.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2220730.0 | 2014-02-22T07:30:00.000+0000 | 28.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2231635.0 | 2014-02-23T16:35:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2241635.0 | 2014-02-24T16:35:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2251635.0 | 2014-02-25T16:35:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2261635.0 | 2014-02-26T16:35:00.000+0000 | 10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2271635.0 | 2014-02-27T16:35:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2281635.0 | 2014-02-28T16:35:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3011206.0 | 2014-03-01T12:06:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3010659.0 | 2014-03-01T06:59:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3022041.0 | 2014-03-02T20:41:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3020705.0 | 2014-03-02T07:05:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3030729.0 | 2014-03-03T07:29:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3032041.0 | 2014-03-03T20:41:00.000+0000 | 113.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3040738.0 | 2014-03-04T07:38:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3050727.0 | 2014-03-05T07:27:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3052041.0 | 2014-03-05T20:41:00.000+0000 | 4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3060705.0 | 2014-03-06T07:05:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3061252.0 | 2014-03-06T12:52:00.000+0000 | 67.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3062100.0 | 2014-03-06T21:00:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3070705.0 | 2014-03-07T07:05:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3071252.0 | 2014-03-07T12:52:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3072100.0 | 2014-03-07T21:00:00.000+0000 | 13.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3080705.0 | 2014-03-08T07:05:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3081250.0 | 2014-03-08T12:50:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3091255.0 | 2014-03-09T12:55:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3092059.0 | 2014-03-09T20:59:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3100705.0 | 2014-03-10T07:05:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3101252.0 | 2014-03-10T12:52:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3102059.0 | 2014-03-10T20:59:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3112059.0 | 2014-03-11T20:59:00.000+0000 | 181.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3111252.0 | 2014-03-11T12:52:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3122059.0 | 2014-03-12T20:59:00.000+0000 | 22.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3121252.0 | 2014-03-12T12:52:00.000+0000 | 161.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3130705.0 | 2014-03-13T07:05:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3131252.0 | 2014-03-13T12:52:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3132059.0 | 2014-03-13T20:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3140705.0 | 2014-03-14T07:05:00.000+0000 | 66.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3141252.0 | 2014-03-14T12:52:00.000+0000 | 39.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3142059.0 | 2014-03-14T20:59:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3150700.0 | 2014-03-15T07:00:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3151250.0 | 2014-03-15T12:50:00.000+0000 | 34.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3161255.0 | 2014-03-16T12:55:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3162059.0 | 2014-03-16T20:59:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3170705.0 | 2014-03-17T07:05:00.000+0000 | -11.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3171252.0 | 2014-03-17T12:52:00.000+0000 | 22.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3172059.0 | 2014-03-17T20:59:00.000+0000 | 132.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3182059.0 | 2014-03-18T20:59:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3181252.0 | 2014-03-18T12:52:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3192059.0 | 2014-03-19T20:59:00.000+0000 | 16.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3191252.0 | 2014-03-19T12:52:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3200705.0 | 2014-03-20T07:05:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3201252.0 | 2014-03-20T12:52:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3202059.0 | 2014-03-20T20:59:00.000+0000 | 77.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3210705.0 | 2014-03-21T07:05:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3211252.0 | 2014-03-21T12:52:00.000+0000 | 9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3212059.0 | 2014-03-21T20:59:00.000+0000 | 11.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3220705.0 | 2014-03-22T07:05:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3221250.0 | 2014-03-22T12:50:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3221600.0 | 2014-03-22T16:00:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3231255.0 | 2014-03-23T12:55:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3240705.0 | 2014-03-24T07:05:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3241252.0 | 2014-03-24T12:52:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3242059.0 | 2014-03-24T20:59:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3252059.0 | 2014-03-25T20:59:00.000+0000 | 121.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3251252.0 | 2014-03-25T12:52:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3262059.0 | 2014-03-26T20:59:00.000+0000 | 49.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3261252.0 | 2014-03-26T12:52:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3270705.0 | 2014-03-27T07:05:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3271252.0 | 2014-03-27T12:52:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3272059.0 | 2014-03-27T20:59:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3280705.0 | 2014-03-28T07:05:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3281252.0 | 2014-03-28T12:52:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3282059.0 | 2014-03-28T20:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3291250.0 | 2014-03-29T12:50:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3290705.0 | 2014-03-29T07:05:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3291600.0 | 2014-03-29T16:00:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3301255.0 | 2014-03-30T12:55:00.000+0000 | 64.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3302059.0 | 2014-03-30T20:59:00.000+0000 | 166.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3310705.0 | 2014-03-31T07:05:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3311252.0 | 2014-03-31T12:52:00.000+0000 | 12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3312059.0 | 2014-03-31T20:59:00.000+0000 | 7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3011902.0 | 2014-03-01T19:02:00.000+0000 | 64.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3011536.0 | 2014-03-01T15:36:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3021536.0 | 2014-03-02T15:36:00.000+0000 | 9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3031536.0 | 2014-03-03T15:36:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3041536.0 | 2014-03-04T15:36:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3051536.0 | 2014-03-05T15:36:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3090715.0 | 2014-03-09T07:15:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3110705.0 | 2014-03-11T07:05:00.000+0000 | -11.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3120659.0 | 2014-03-12T06:59:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3160715.0 | 2014-03-16T07:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3180705.0 | 2014-03-18T07:05:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3190659.0 | 2014-03-19T06:59:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3230715.0 | 2014-03-23T07:15:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3250705.0 | 2014-03-25T07:05:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3260659.0 | 2014-03-26T06:59:00.000+0000 | 63.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3300715.0 | 2014-03-30T07:15:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3010730.0 | 2014-03-01T07:30:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3021635.0 | 2014-03-02T16:35:00.000+0000 | 16.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3031635.0 | 2014-03-03T16:35:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3041635.0 | 2014-03-04T16:35:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3051635.0 | 2014-03-05T16:35:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3061635.0 | 2014-03-06T16:35:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3071635.0 | 2014-03-07T16:35:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3080730.0 | 2014-03-08T07:30:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3091825.0 | 2014-03-09T18:25:00.000+0000 | 45.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3101825.0 | 2014-03-10T18:25:00.000+0000 | 10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3111825.0 | 2014-03-11T18:25:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3121825.0 | 2014-03-12T18:25:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3131825.0 | 2014-03-13T18:25:00.000+0000 | 123.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3141825.0 | 2014-03-14T18:25:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3150730.0 | 2014-03-15T07:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3161825.0 | 2014-03-16T18:25:00.000+0000 | 24.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3171825.0 | 2014-03-17T18:25:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3181825.0 | 2014-03-18T18:25:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3191825.0 | 2014-03-19T18:25:00.000+0000 | 223.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3201825.0 | 2014-03-20T18:25:00.000+0000 | 178.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3211825.0 | 2014-03-21T18:25:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3220730.0 | 2014-03-22T07:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3231825.0 | 2014-03-23T18:25:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3241825.0 | 2014-03-24T18:25:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3251825.0 | 2014-03-25T18:25:00.000+0000 | 222.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3261825.0 | 2014-03-26T18:25:00.000+0000 | 51.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3271825.0 | 2014-03-27T18:25:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3281825.0 | 2014-03-28T18:25:00.000+0000 | 26.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3290730.0 | 2014-03-29T07:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3301825.0 | 2014-03-30T18:25:00.000+0000 | 139.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 3311825.0 | 2014-03-31T18:25:00.000+0000 | 25.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1020705.0 | 2014-01-02T07:05:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1030705.0 | 2014-01-03T07:05:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1040655.0 | 2014-01-04T06:55:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1050703.0 | 2014-01-05T07:03:00.000+0000 | 4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1060705.0 | 2014-01-06T07:05:00.000+0000 | 36.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1071230.0 | 2014-01-07T12:30:00.000+0000 | 24.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1070719.0 | 2014-01-07T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1071730.0 | 2014-01-07T17:30:00.000+0000 | 161.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1072043.0 | 2014-01-07T20:43:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1080719.0 | 2014-01-08T07:19:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1081659.0 | 2014-01-08T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1081230.0 | 2014-01-08T12:30:00.000+0000 | 66.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1082043.0 | 2014-01-08T20:43:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1090719.0 | 2014-01-09T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1091659.0 | 2014-01-09T16:59:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1091230.0 | 2014-01-09T12:30:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1092043.0 | 2014-01-09T20:43:00.000+0000 | 63.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1100719.0 | 2014-01-10T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1101659.0 | 2014-01-10T16:59:00.000+0000 | 244.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1101230.0 | 2014-01-10T12:30:00.000+0000 | 110.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1102043.0 | 2014-01-10T20:43:00.000+0000 | 43.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1111230.0 | 2014-01-11T12:30:00.000+0000 | 87.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1110719.0 | 2014-01-11T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1121654.0 | 2014-01-12T16:54:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1121230.0 | 2014-01-12T12:30:00.000+0000 | 21.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1120709.0 | 2014-01-12T07:09:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1122043.0 | 2014-01-12T20:43:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1130719.0 | 2014-01-13T07:19:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1131659.0 | 2014-01-13T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1131230.0 | 2014-01-13T12:30:00.000+0000 | 14.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1132043.0 | 2014-01-13T20:43:00.000+0000 | 51.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1141230.0 | 2014-01-14T12:30:00.000+0000 | 30.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1140719.0 | 2014-01-14T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1141730.0 | 2014-01-14T17:30:00.000+0000 | 69.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1142043.0 | 2014-01-14T20:43:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1150719.0 | 2014-01-15T07:19:00.000+0000 | 42.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1151659.0 | 2014-01-15T16:59:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1151230.0 | 2014-01-15T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1152043.0 | 2014-01-15T20:43:00.000+0000 | 22.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1160719.0 | 2014-01-16T07:19:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1161659.0 | 2014-01-16T16:59:00.000+0000 | -9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1161230.0 | 2014-01-16T12:30:00.000+0000 | -10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1162043.0 | 2014-01-16T20:43:00.000+0000 | 3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1171659.0 | 2014-01-17T16:59:00.000+0000 | 46.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1170719.0 | 2014-01-17T07:19:00.000+0000 | -4.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1172043.0 | 2014-01-17T20:43:00.000+0000 | 54.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1181230.0 | 2014-01-18T12:30:00.000+0000 | 20.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1180719.0 | 2014-01-18T07:19:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1191654.0 | 2014-01-19T16:54:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1191230.0 | 2014-01-19T12:30:00.000+0000 | 29.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1190709.0 | 2014-01-19T07:09:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1192043.0 | 2014-01-19T20:43:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1201659.0 | 2014-01-20T16:59:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1200719.0 | 2014-01-20T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1202043.0 | 2014-01-20T20:43:00.000+0000 | 12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1211230.0 | 2014-01-21T12:30:00.000+0000 | 102.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1210719.0 | 2014-01-21T07:19:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1211730.0 | 2014-01-21T17:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1212043.0 | 2014-01-21T20:43:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1220719.0 | 2014-01-22T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1221659.0 | 2014-01-22T16:59:00.000+0000 | 6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1221230.0 | 2014-01-22T12:30:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1222043.0 | 2014-01-22T20:43:00.000+0000 | 70.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1230719.0 | 2014-01-23T07:19:00.000+0000 | 12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1231659.0 | 2014-01-23T16:59:00.000+0000 | 94.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1231230.0 | 2014-01-23T12:30:00.000+0000 | 111.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1232043.0 | 2014-01-23T20:43:00.000+0000 | 13.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1240719.0 | 2014-01-24T07:19:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1241659.0 | 2014-01-24T16:59:00.000+0000 | 84.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1241230.0 | 2014-01-24T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1242043.0 | 2014-01-24T20:43:00.000+0000 | 56.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1251230.0 | 2014-01-25T12:30:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1250719.0 | 2014-01-25T07:19:00.000+0000 | 23.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1261654.0 | 2014-01-26T16:54:00.000+0000 | 113.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1261230.0 | 2014-01-26T12:30:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1260709.0 | 2014-01-26T07:09:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1262043.0 | 2014-01-26T20:43:00.000+0000 | 31.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1271659.0 | 2014-01-27T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1270719.0 | 2014-01-27T07:19:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1281230.0 | 2014-01-28T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1280719.0 | 2014-01-28T07:19:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1281730.0 | 2014-01-28T17:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1282043.0 | 2014-01-28T20:43:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1291659.0 | 2014-01-29T16:59:00.000+0000 | -6.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1290719.0 | 2014-01-29T07:19:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1292043.0 | 2014-01-29T20:43:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1300719.0 | 2014-01-30T07:19:00.000+0000 | 2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1301659.0 | 2014-01-30T16:59:00.000+0000 | 9.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1301230.0 | 2014-01-30T12:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1302043.0 | 2014-01-30T20:43:00.000+0000 | 93.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1310719.0 | 2014-01-31T07:19:00.000+0000 | 34.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1311659.0 | 2014-01-31T16:59:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1311230.0 | 2014-01-31T12:30:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1312043.0 | 2014-01-31T20:43:00.000+0000 | 28.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1171230.0 | 2014-01-17T12:30:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1201250.0 | 2014-01-20T12:50:00.000+0000 | -12.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1291230.0 | 2014-01-29T12:30:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1011815.0 | 2014-01-01T18:15:00.000+0000 | 125.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1021815.0 | 2014-01-02T18:15:00.000+0000 | 33.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1031815.0 | 2014-01-03T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1040755.0 | 2014-01-04T07:55:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1051815.0 | 2014-01-05T18:15:00.000+0000 | 172.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1061815.0 | 2014-01-06T18:15:00.000+0000 | 151.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1071815.0 | 2014-01-07T18:15:00.000+0000 | 43.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1081815.0 | 2014-01-08T18:15:00.000+0000 | 14.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1091815.0 | 2014-01-09T18:15:00.000+0000 | 3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1101815.0 | 2014-01-10T18:15:00.000+0000 | 10.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1110730.0 | 2014-01-11T07:30:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1121815.0 | 2014-01-12T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1131815.0 | 2014-01-13T18:15:00.000+0000 | -2.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1141815.0 | 2014-01-14T18:15:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1151815.0 | 2014-01-15T18:15:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1161815.0 | 2014-01-16T18:15:00.000+0000 | 8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1171815.0 | 2014-01-17T18:15:00.000+0000 | 22.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1180730.0 | 2014-01-18T07:30:00.000+0000 | 1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1191815.0 | 2014-01-19T18:15:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1201815.0 | 2014-01-20T18:15:00.000+0000 | 5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1211815.0 | 2014-01-21T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1221815.0 | 2014-01-22T18:15:00.000+0000 | 3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1231815.0 | 2014-01-23T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1241815.0 | 2014-01-24T18:15:00.000+0000 | 84.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1250730.0 | 2014-01-25T07:30:00.000+0000 | -7.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1261815.0 | 2014-01-26T18:15:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1271815.0 | 2014-01-27T18:15:00.000+0000 | -1.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1281815.0 | 2014-01-28T18:15:00.000+0000 | 0.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1291815.0 | 2014-01-29T18:15:00.000+0000 | -3.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1301815.0 | 2014-01-30T18:15:00.000+0000 | -5.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 1311815.0 | 2014-01-31T18:15:00.000+0000 | -8.0 | 1014.0 | EWR | MSY | Newark | New Orleans | NJ | LA |
| 2011055.0 | 2014-02-01T10:55:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2021025.0 | 2014-02-02T10:25:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2031025.0 | 2014-02-03T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2031750.0 | 2014-02-03T17:50:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2041025.0 | 2014-02-04T10:25:00.000+0000 | 6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2041750.0 | 2014-02-04T17:50:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2051025.0 | 2014-02-05T10:25:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2051750.0 | 2014-02-05T17:50:00.000+0000 | 29.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2061025.0 | 2014-02-06T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2061750.0 | 2014-02-06T17:50:00.000+0000 | 48.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2071025.0 | 2014-02-07T10:25:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2071750.0 | 2014-02-07T17:50:00.000+0000 | 20.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2081055.0 | 2014-02-08T10:55:00.000+0000 | 2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2091025.0 | 2014-02-09T10:25:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2091750.0 | 2014-02-09T17:50:00.000+0000 | 33.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2101025.0 | 2014-02-10T10:25:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2101750.0 | 2014-02-10T17:50:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2111025.0 | 2014-02-11T10:25:00.000+0000 | 15.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2111750.0 | 2014-02-11T17:50:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2121025.0 | 2014-02-12T10:25:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2121750.0 | 2014-02-12T17:50:00.000+0000 | 158.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2131855.0 | 2014-02-13T18:55:00.000+0000 | 11.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2131215.0 | 2014-02-13T12:15:00.000+0000 | 23.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2141855.0 | 2014-02-14T18:55:00.000+0000 | 22.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2141215.0 | 2014-02-14T12:15:00.000+0000 | 18.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2150935.0 | 2014-02-15T09:35:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2151635.0 | 2014-02-15T16:35:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2161855.0 | 2014-02-16T18:55:00.000+0000 | 58.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2161215.0 | 2014-02-16T12:15:00.000+0000 | 17.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2171855.0 | 2014-02-17T18:55:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2171215.0 | 2014-02-17T12:15:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2181855.0 | 2014-02-18T18:55:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2181215.0 | 2014-02-18T12:15:00.000+0000 | 58.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2191855.0 | 2014-02-19T18:55:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2191215.0 | 2014-02-19T12:15:00.000+0000 | 32.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2201855.0 | 2014-02-20T18:55:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2201215.0 | 2014-02-20T12:15:00.000+0000 | 28.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2211855.0 | 2014-02-21T18:55:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2211215.0 | 2014-02-21T12:15:00.000+0000 | 11.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2220935.0 | 2014-02-22T09:35:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2221635.0 | 2014-02-22T16:35:00.000+0000 | 133.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2231855.0 | 2014-02-23T18:55:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2231215.0 | 2014-02-23T12:15:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2241855.0 | 2014-02-24T18:55:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2241215.0 | 2014-02-24T12:15:00.000+0000 | 16.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2251855.0 | 2014-02-25T18:55:00.000+0000 | 7.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2251215.0 | 2014-02-25T12:15:00.000+0000 | 15.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2261855.0 | 2014-02-26T18:55:00.000+0000 | 2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2261215.0 | 2014-02-26T12:15:00.000+0000 | 16.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2271855.0 | 2014-02-27T18:55:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2271215.0 | 2014-02-27T12:15:00.000+0000 | 32.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2281855.0 | 2014-02-28T18:55:00.000+0000 | 61.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2281215.0 | 2014-02-28T12:15:00.000+0000 | 94.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3010935.0 | 2014-03-01T09:35:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3011635.0 | 2014-03-01T16:35:00.000+0000 | 39.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3021855.0 | 2014-03-02T18:55:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3021215.0 | 2014-03-02T12:15:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3031855.0 | 2014-03-03T18:55:00.000+0000 | 25.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3031215.0 | 2014-03-03T12:15:00.000+0000 | 8.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3041855.0 | 2014-03-04T18:55:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3041215.0 | 2014-03-04T12:15:00.000+0000 | 28.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3051855.0 | 2014-03-05T18:55:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3051215.0 | 2014-03-05T12:15:00.000+0000 | 27.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3061855.0 | 2014-03-06T18:55:00.000+0000 | 20.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3061215.0 | 2014-03-06T12:15:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3071855.0 | 2014-03-07T18:55:00.000+0000 | 36.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3071215.0 | 2014-03-07T12:15:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3081940.0 | 2014-03-08T19:40:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3080830.0 | 2014-03-08T08:30:00.000+0000 | 6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3091905.0 | 2014-03-09T19:05:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3090840.0 | 2014-03-09T08:40:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3101905.0 | 2014-03-10T19:05:00.000+0000 | 49.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3100840.0 | 2014-03-10T08:40:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3111905.0 | 2014-03-11T19:05:00.000+0000 | 9.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3110840.0 | 2014-03-11T08:40:00.000+0000 | 84.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3121905.0 | 2014-03-12T19:05:00.000+0000 | 26.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3120840.0 | 2014-03-12T08:40:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3131905.0 | 2014-03-13T19:05:00.000+0000 | 37.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3130840.0 | 2014-03-13T08:40:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3141905.0 | 2014-03-14T19:05:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3140840.0 | 2014-03-14T08:40:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3150830.0 | 2014-03-15T08:30:00.000+0000 | 16.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3151940.0 | 2014-03-15T19:40:00.000+0000 | 95.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3161905.0 | 2014-03-16T19:05:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3160840.0 | 2014-03-16T08:40:00.000+0000 | -6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3171905.0 | 2014-03-17T19:05:00.000+0000 | 13.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3170840.0 | 2014-03-17T08:40:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3181905.0 | 2014-03-18T19:05:00.000+0000 | 52.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3180840.0 | 2014-03-18T08:40:00.000+0000 | 2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3191905.0 | 2014-03-19T19:05:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3190840.0 | 2014-03-19T08:40:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3201905.0 | 2014-03-20T19:05:00.000+0000 | 36.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3200840.0 | 2014-03-20T08:40:00.000+0000 | 68.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3211905.0 | 2014-03-21T19:05:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3210840.0 | 2014-03-21T08:40:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3220830.0 | 2014-03-22T08:30:00.000+0000 | 12.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3221940.0 | 2014-03-22T19:40:00.000+0000 | 11.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3231905.0 | 2014-03-23T19:05:00.000+0000 | 9.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3230840.0 | 2014-03-23T08:40:00.000+0000 | 16.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3241905.0 | 2014-03-24T19:05:00.000+0000 | 9.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3240840.0 | 2014-03-24T08:40:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3251905.0 | 2014-03-25T19:05:00.000+0000 | 10.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3250840.0 | 2014-03-25T08:40:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3261905.0 | 2014-03-26T19:05:00.000+0000 | 14.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3260840.0 | 2014-03-26T08:40:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3271905.0 | 2014-03-27T19:05:00.000+0000 | 13.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3270840.0 | 2014-03-27T08:40:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3281905.0 | 2014-03-28T19:05:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3280840.0 | 2014-03-28T08:40:00.000+0000 | 33.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3290830.0 | 2014-03-29T08:30:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3291940.0 | 2014-03-29T19:40:00.000+0000 | 231.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3301905.0 | 2014-03-30T19:05:00.000+0000 | 7.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3300840.0 | 2014-03-30T08:40:00.000+0000 | 15.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3311905.0 | 2014-03-31T19:05:00.000+0000 | 19.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 3310840.0 | 2014-03-31T08:40:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1010850.0 | 2014-01-01T08:50:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1011755.0 | 2014-01-01T17:55:00.000+0000 | 160.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1021805.0 | 2014-01-02T18:05:00.000+0000 | 138.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1020905.0 | 2014-01-02T09:05:00.000+0000 | 5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1031805.0 | 2014-01-03T18:05:00.000+0000 | 154.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1030905.0 | 2014-01-03T09:05:00.000+0000 | 179.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1041655.0 | 2014-01-04T16:55:00.000+0000 | 113.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1040900.0 | 2014-01-04T09:00:00.000+0000 | 56.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1050905.0 | 2014-01-05T09:05:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1051805.0 | 2014-01-05T18:05:00.000+0000 | 53.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1061755.0 | 2014-01-06T17:55:00.000+0000 | 61.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1060905.0 | 2014-01-06T09:05:00.000+0000 | 23.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1071025.0 | 2014-01-07T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1071750.0 | 2014-01-07T17:50:00.000+0000 | 302.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1081025.0 | 2014-01-08T10:25:00.000+0000 | 7.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1081750.0 | 2014-01-08T17:50:00.000+0000 | 52.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1091025.0 | 2014-01-09T10:25:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1091750.0 | 2014-01-09T17:50:00.000+0000 | 8.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1101025.0 | 2014-01-10T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1101750.0 | 2014-01-10T17:50:00.000+0000 | 92.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1111055.0 | 2014-01-11T10:55:00.000+0000 | 31.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1121025.0 | 2014-01-12T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1121750.0 | 2014-01-12T17:50:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1131025.0 | 2014-01-13T10:25:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1131750.0 | 2014-01-13T17:50:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1141025.0 | 2014-01-14T10:25:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1141750.0 | 2014-01-14T17:50:00.000+0000 | 127.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1151025.0 | 2014-01-15T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1151750.0 | 2014-01-15T17:50:00.000+0000 | -3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1161025.0 | 2014-01-16T10:25:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1161750.0 | 2014-01-16T17:50:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1171025.0 | 2014-01-17T10:25:00.000+0000 | 12.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1171750.0 | 2014-01-17T17:50:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1181055.0 | 2014-01-18T10:55:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1191025.0 | 2014-01-19T10:25:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1191750.0 | 2014-01-19T17:50:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1201025.0 | 2014-01-20T10:25:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1201750.0 | 2014-01-20T17:50:00.000+0000 | 6.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1211025.0 | 2014-01-21T10:25:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1211750.0 | 2014-01-21T17:50:00.000+0000 | 4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1221025.0 | 2014-01-22T10:25:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1221750.0 | 2014-01-22T17:50:00.000+0000 | 3.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1231025.0 | 2014-01-23T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1231750.0 | 2014-01-23T17:50:00.000+0000 | 30.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1241025.0 | 2014-01-24T10:25:00.000+0000 | 43.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1241750.0 | 2014-01-24T17:50:00.000+0000 | -4.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1251055.0 | 2014-01-25T10:55:00.000+0000 | 5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1261025.0 | 2014-01-26T10:25:00.000+0000 | -1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1261750.0 | 2014-01-26T17:50:00.000+0000 | 27.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1271025.0 | 2014-01-27T10:25:00.000+0000 | -2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1271750.0 | 2014-01-27T17:50:00.000+0000 | 2.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1281025.0 | 2014-01-28T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1281750.0 | 2014-01-28T17:50:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1291025.0 | 2014-01-29T10:25:00.000+0000 | 1.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1291750.0 | 2014-01-29T17:50:00.000+0000 | -5.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1301025.0 | 2014-01-30T10:25:00.000+0000 | 0.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1301750.0 | 2014-01-30T17:50:00.000+0000 | 35.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1311025.0 | 2014-01-31T10:25:00.000+0000 | 11.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 1311750.0 | 2014-01-31T17:50:00.000+0000 | 25.0 | 1303.0 | LAS | MSY | Las Vegas | New Orleans | NV | LA |
| 2011530.0 | 2014-02-01T15:30:00.000+0000 | -3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2021105.0 | 2014-02-02T11:05:00.000+0000 | -4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2031105.0 | 2014-02-03T11:05:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2041105.0 | 2014-02-04T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2051105.0 | 2014-02-05T11:05:00.000+0000 | 6.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2061105.0 | 2014-02-06T11:05:00.000+0000 | 40.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2071105.0 | 2014-02-07T11:05:00.000+0000 | 7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2081530.0 | 2014-02-08T15:30:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2091105.0 | 2014-02-09T11:05:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2101105.0 | 2014-02-10T11:05:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2111105.0 | 2014-02-11T11:05:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2121105.0 | 2014-02-12T11:05:00.000+0000 | 7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2130800.0 | 2014-02-13T08:00:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2140830.0 | 2014-02-14T08:30:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2150750.0 | 2014-02-15T07:50:00.000+0000 | 7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2160930.0 | 2014-02-16T09:30:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2170830.0 | 2014-02-17T08:30:00.000+0000 | 10.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2180830.0 | 2014-02-18T08:30:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2190830.0 | 2014-02-19T08:30:00.000+0000 | -4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2200830.0 | 2014-02-20T08:30:00.000+0000 | 9.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2210830.0 | 2014-02-21T08:30:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2220750.0 | 2014-02-22T07:50:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2230930.0 | 2014-02-23T09:30:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2240830.0 | 2014-02-24T08:30:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2250830.0 | 2014-02-25T08:30:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2260830.0 | 2014-02-26T08:30:00.000+0000 | 251.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2270830.0 | 2014-02-27T08:30:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 2280830.0 | 2014-02-28T08:30:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3010750.0 | 2014-03-01T07:50:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3020930.0 | 2014-03-02T09:30:00.000+0000 | 35.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3030830.0 | 2014-03-03T08:30:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3040830.0 | 2014-03-04T08:30:00.000+0000 | 11.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3050830.0 | 2014-03-05T08:30:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3060830.0 | 2014-03-06T08:30:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3070830.0 | 2014-03-07T08:30:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3081610.0 | 2014-03-08T16:10:00.000+0000 | 93.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3090950.0 | 2014-03-09T09:50:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3100950.0 | 2014-03-10T09:50:00.000+0000 | 8.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3110950.0 | 2014-03-11T09:50:00.000+0000 | 36.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3120950.0 | 2014-03-12T09:50:00.000+0000 | 68.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3130950.0 | 2014-03-13T09:50:00.000+0000 | 13.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3140950.0 | 2014-03-14T09:50:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3151610.0 | 2014-03-15T16:10:00.000+0000 | 16.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3160950.0 | 2014-03-16T09:50:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3170950.0 | 2014-03-17T09:50:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3180950.0 | 2014-03-18T09:50:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3190950.0 | 2014-03-19T09:50:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3200950.0 | 2014-03-20T09:50:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3210950.0 | 2014-03-21T09:50:00.000+0000 | 7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3221610.0 | 2014-03-22T16:10:00.000+0000 | 38.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3230950.0 | 2014-03-23T09:50:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3240950.0 | 2014-03-24T09:50:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3250950.0 | 2014-03-25T09:50:00.000+0000 | 4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3260950.0 | 2014-03-26T09:50:00.000+0000 | 5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3270950.0 | 2014-03-27T09:50:00.000+0000 | 12.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3280950.0 | 2014-03-28T09:50:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3291610.0 | 2014-03-29T16:10:00.000+0000 | 12.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3300950.0 | 2014-03-30T09:50:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3310950.0 | 2014-03-31T09:50:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1011635.0 | 2014-01-01T16:35:00.000+0000 | -7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1021635.0 | 2014-01-02T16:35:00.000+0000 | 96.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1031635.0 | 2014-01-03T16:35:00.000+0000 | 3.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1041205.0 | 2014-01-04T12:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1051635.0 | 2014-01-05T16:35:00.000+0000 | 60.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1061635.0 | 2014-01-06T16:35:00.000+0000 | -7.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1071105.0 | 2014-01-07T11:05:00.000+0000 | 14.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1081105.0 | 2014-01-08T11:05:00.000+0000 | 4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1091105.0 | 2014-01-09T11:05:00.000+0000 | 11.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1101105.0 | 2014-01-10T11:05:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1111530.0 | 2014-01-11T15:30:00.000+0000 | 11.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1121105.0 | 2014-01-12T11:05:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1131105.0 | 2014-01-13T11:05:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1141105.0 | 2014-01-14T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1151105.0 | 2014-01-15T11:05:00.000+0000 | 9.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1161105.0 | 2014-01-16T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1171105.0 | 2014-01-17T11:05:00.000+0000 | -1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1181530.0 | 2014-01-18T15:30:00.000+0000 | 48.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1191105.0 | 2014-01-19T11:05:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1201105.0 | 2014-01-20T11:05:00.000+0000 | -4.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1211105.0 | 2014-01-21T11:05:00.000+0000 | 2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1221105.0 | 2014-01-22T11:05:00.000+0000 | 19.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1231105.0 | 2014-01-23T11:05:00.000+0000 | -2.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1241105.0 | 2014-01-24T11:05:00.000+0000 | 72.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1251530.0 | 2014-01-25T15:30:00.000+0000 | 5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1261105.0 | 2014-01-26T11:05:00.000+0000 | -5.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1271105.0 | 2014-01-27T11:05:00.000+0000 | -6.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1281105.0 | 2014-01-28T11:05:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1291105.0 | 2014-01-29T11:05:00.000+0000 | 0.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1301105.0 | 2014-01-30T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 1311105.0 | 2014-01-31T11:05:00.000+0000 | 1.0 | 599.0 | MCI | MSY | Kansas City | New Orleans | MO | LA |
| 3011530.0 | 2014-03-01T15:30:00.000+0000 | 72.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3010850.0 | 2014-03-01T08:50:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3011245.0 | 2014-03-01T12:45:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3021610.0 | 2014-03-02T16:10:00.000+0000 | 14.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3021350.0 | 2014-03-02T13:50:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3021800.0 | 2014-03-02T18:00:00.000+0000 | 56.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3031610.0 | 2014-03-03T16:10:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3030810.0 | 2014-03-03T08:10:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3031350.0 | 2014-03-03T13:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3031800.0 | 2014-03-03T18:00:00.000+0000 | -5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3041610.0 | 2014-03-04T16:10:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3040810.0 | 2014-03-04T08:10:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3041350.0 | 2014-03-04T13:50:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3041800.0 | 2014-03-04T18:00:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3051610.0 | 2014-03-05T16:10:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3050810.0 | 2014-03-05T08:10:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3051350.0 | 2014-03-05T13:50:00.000+0000 | 48.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3051800.0 | 2014-03-05T18:00:00.000+0000 | 21.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3061610.0 | 2014-03-06T16:10:00.000+0000 | 26.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3060810.0 | 2014-03-06T08:10:00.000+0000 | -5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3061350.0 | 2014-03-06T13:50:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3061800.0 | 2014-03-06T18:00:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3071610.0 | 2014-03-07T16:10:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3070810.0 | 2014-03-07T08:10:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3071350.0 | 2014-03-07T13:50:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3071800.0 | 2014-03-07T18:00:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3081540.0 | 2014-03-08T15:40:00.000+0000 | 27.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3081335.0 | 2014-03-08T13:35:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3080945.0 | 2014-03-08T09:45:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3091620.0 | 2014-03-09T16:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3091820.0 | 2014-03-09T18:20:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3091420.0 | 2014-03-09T14:20:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3100820.0 | 2014-03-10T08:20:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3101420.0 | 2014-03-10T14:20:00.000+0000 | 41.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3101820.0 | 2014-03-10T18:20:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3101620.0 | 2014-03-10T16:20:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3110820.0 | 2014-03-11T08:20:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3111420.0 | 2014-03-11T14:20:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3111820.0 | 2014-03-11T18:20:00.000+0000 | 29.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3111620.0 | 2014-03-11T16:20:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3120820.0 | 2014-03-12T08:20:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3121420.0 | 2014-03-12T14:20:00.000+0000 | 40.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3121820.0 | 2014-03-12T18:20:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3121620.0 | 2014-03-12T16:20:00.000+0000 | 24.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3130820.0 | 2014-03-13T08:20:00.000+0000 | 126.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3131420.0 | 2014-03-13T14:20:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3131820.0 | 2014-03-13T18:20:00.000+0000 | 55.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3131620.0 | 2014-03-13T16:20:00.000+0000 | 40.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3140820.0 | 2014-03-14T08:20:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3141420.0 | 2014-03-14T14:20:00.000+0000 | 35.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3141820.0 | 2014-03-14T18:20:00.000+0000 | 21.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3141620.0 | 2014-03-14T16:20:00.000+0000 | 36.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3151335.0 | 2014-03-15T13:35:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3150945.0 | 2014-03-15T09:45:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3151540.0 | 2014-03-15T15:40:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3161620.0 | 2014-03-16T16:20:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3161820.0 | 2014-03-16T18:20:00.000+0000 | 39.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3161420.0 | 2014-03-16T14:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3170820.0 | 2014-03-17T08:20:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3171420.0 | 2014-03-17T14:20:00.000+0000 | 112.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3171820.0 | 2014-03-17T18:20:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3171620.0 | 2014-03-17T16:20:00.000+0000 | 56.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3180820.0 | 2014-03-18T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3181420.0 | 2014-03-18T14:20:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3181820.0 | 2014-03-18T18:20:00.000+0000 | 36.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3181620.0 | 2014-03-18T16:20:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3190820.0 | 2014-03-19T08:20:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3191420.0 | 2014-03-19T14:20:00.000+0000 | 54.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3191820.0 | 2014-03-19T18:20:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3191620.0 | 2014-03-19T16:20:00.000+0000 | 29.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3200820.0 | 2014-03-20T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3201420.0 | 2014-03-20T14:20:00.000+0000 | 18.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3201820.0 | 2014-03-20T18:20:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3201620.0 | 2014-03-20T16:20:00.000+0000 | 79.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3210820.0 | 2014-03-21T08:20:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3211420.0 | 2014-03-21T14:20:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3211820.0 | 2014-03-21T18:20:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3211620.0 | 2014-03-21T16:20:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3221335.0 | 2014-03-22T13:35:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3220945.0 | 2014-03-22T09:45:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3221540.0 | 2014-03-22T15:40:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3231620.0 | 2014-03-23T16:20:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3231820.0 | 2014-03-23T18:20:00.000+0000 | 6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3231420.0 | 2014-03-23T14:20:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3240820.0 | 2014-03-24T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3241420.0 | 2014-03-24T14:20:00.000+0000 | 27.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3241820.0 | 2014-03-24T18:20:00.000+0000 | 44.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3241620.0 | 2014-03-24T16:20:00.000+0000 | 41.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3250820.0 | 2014-03-25T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3251420.0 | 2014-03-25T14:20:00.000+0000 | 70.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3251820.0 | 2014-03-25T18:20:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3251620.0 | 2014-03-25T16:20:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3260820.0 | 2014-03-26T08:20:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3261420.0 | 2014-03-26T14:20:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3261820.0 | 2014-03-26T18:20:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3261620.0 | 2014-03-26T16:20:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3270820.0 | 2014-03-27T08:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3271420.0 | 2014-03-27T14:20:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3271820.0 | 2014-03-27T18:20:00.000+0000 | 41.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3271620.0 | 2014-03-27T16:20:00.000+0000 | 23.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3280820.0 | 2014-03-28T08:20:00.000+0000 | -5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3281420.0 | 2014-03-28T14:20:00.000+0000 | 52.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3281820.0 | 2014-03-28T18:20:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3281620.0 | 2014-03-28T16:20:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3291335.0 | 2014-03-29T13:35:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3290945.0 | 2014-03-29T09:45:00.000+0000 | 20.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3291540.0 | 2014-03-29T15:40:00.000+0000 | 35.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3301620.0 | 2014-03-30T16:20:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3301820.0 | 2014-03-30T18:20:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3301420.0 | 2014-03-30T14:20:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3310820.0 | 2014-03-31T08:20:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3311420.0 | 2014-03-31T14:20:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3311820.0 | 2014-03-31T18:20:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 3311620.0 | 2014-03-31T16:20:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1010800.0 | 2014-01-01T08:00:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1011210.0 | 2014-01-01T12:10:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1011835.0 | 2014-01-01T18:35:00.000+0000 | 53.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1021215.0 | 2014-01-02T12:15:00.000+0000 | 43.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1021835.0 | 2014-01-02T18:35:00.000+0000 | 200.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1020800.0 | 2014-01-02T08:00:00.000+0000 | 29.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1031215.0 | 2014-01-03T12:15:00.000+0000 | 185.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1031835.0 | 2014-01-03T18:35:00.000+0000 | 226.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1030800.0 | 2014-01-03T08:00:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1041420.0 | 2014-01-04T14:20:00.000+0000 | 174.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1040835.0 | 2014-01-04T08:35:00.000+0000 | 18.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1051215.0 | 2014-01-05T12:15:00.000+0000 | 85.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1051835.0 | 2014-01-05T18:35:00.000+0000 | 283.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1050800.0 | 2014-01-05T08:00:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1061215.0 | 2014-01-06T12:15:00.000+0000 | 150.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1061835.0 | 2014-01-06T18:35:00.000+0000 | 133.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1060800.0 | 2014-01-06T08:00:00.000+0000 | 102.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1071240.0 | 2014-01-07T12:40:00.000+0000 | 57.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1071455.0 | 2014-01-07T14:55:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1070905.0 | 2014-01-07T09:05:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1071735.0 | 2014-01-07T17:35:00.000+0000 | 131.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1081240.0 | 2014-01-08T12:40:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1081455.0 | 2014-01-08T14:55:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1080905.0 | 2014-01-08T09:05:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1081735.0 | 2014-01-08T17:35:00.000+0000 | 34.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1091240.0 | 2014-01-09T12:40:00.000+0000 | 14.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1091455.0 | 2014-01-09T14:55:00.000+0000 | 24.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1090905.0 | 2014-01-09T09:05:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1091735.0 | 2014-01-09T17:35:00.000+0000 | 28.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1101240.0 | 2014-01-10T12:40:00.000+0000 | 50.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1101455.0 | 2014-01-10T14:55:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1100905.0 | 2014-01-10T09:05:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1101735.0 | 2014-01-10T17:35:00.000+0000 | 33.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1111305.0 | 2014-01-11T13:05:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1111805.0 | 2014-01-11T18:05:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1110950.0 | 2014-01-11T09:50:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1121235.0 | 2014-01-12T12:35:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1121455.0 | 2014-01-12T14:55:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1121735.0 | 2014-01-12T17:35:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1131240.0 | 2014-01-13T12:40:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1131455.0 | 2014-01-13T14:55:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1130850.0 | 2014-01-13T08:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1131735.0 | 2014-01-13T17:35:00.000+0000 | 15.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1141240.0 | 2014-01-14T12:40:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1141455.0 | 2014-01-14T14:55:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1140850.0 | 2014-01-14T08:50:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1141735.0 | 2014-01-14T17:35:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1151240.0 | 2014-01-15T12:40:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1151455.0 | 2014-01-15T14:55:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1150850.0 | 2014-01-15T08:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1151735.0 | 2014-01-15T17:35:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1161240.0 | 2014-01-16T12:40:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1161455.0 | 2014-01-16T14:55:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1160850.0 | 2014-01-16T08:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1161735.0 | 2014-01-16T17:35:00.000+0000 | 52.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1171240.0 | 2014-01-17T12:40:00.000+0000 | 21.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1171455.0 | 2014-01-17T14:55:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1170850.0 | 2014-01-17T08:50:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1171735.0 | 2014-01-17T17:35:00.000+0000 | 40.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1181305.0 | 2014-01-18T13:05:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1181805.0 | 2014-01-18T18:05:00.000+0000 | 42.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1180950.0 | 2014-01-18T09:50:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1191235.0 | 2014-01-19T12:35:00.000+0000 | 23.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1191455.0 | 2014-01-19T14:55:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1191735.0 | 2014-01-19T17:35:00.000+0000 | 64.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1201240.0 | 2014-01-20T12:40:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1201455.0 | 2014-01-20T14:55:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1200850.0 | 2014-01-20T08:50:00.000+0000 | 7.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1201735.0 | 2014-01-20T17:35:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1211240.0 | 2014-01-21T12:40:00.000+0000 | 41.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1211455.0 | 2014-01-21T14:55:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1210850.0 | 2014-01-21T08:50:00.000+0000 | 18.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1211735.0 | 2014-01-21T17:35:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1221240.0 | 2014-01-22T12:40:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1221455.0 | 2014-01-22T14:55:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1220850.0 | 2014-01-22T08:50:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1221735.0 | 2014-01-22T17:35:00.000+0000 | 118.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1231240.0 | 2014-01-23T12:40:00.000+0000 | 20.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1231455.0 | 2014-01-23T14:55:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1230850.0 | 2014-01-23T08:50:00.000+0000 | 18.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1231735.0 | 2014-01-23T17:35:00.000+0000 | 33.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1241240.0 | 2014-01-24T12:40:00.000+0000 | 20.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1241455.0 | 2014-01-24T14:55:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1240850.0 | 2014-01-24T08:50:00.000+0000 | 12.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1241735.0 | 2014-01-24T17:35:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1251305.0 | 2014-01-25T13:05:00.000+0000 | 27.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1251805.0 | 2014-01-25T18:05:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1250950.0 | 2014-01-25T09:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1261235.0 | 2014-01-26T12:35:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1261455.0 | 2014-01-26T14:55:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1261735.0 | 2014-01-26T17:35:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1271240.0 | 2014-01-27T12:40:00.000+0000 | 17.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1271455.0 | 2014-01-27T14:55:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1270850.0 | 2014-01-27T08:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1271735.0 | 2014-01-27T17:35:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1281240.0 | 2014-01-28T12:40:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1281455.0 | 2014-01-28T14:55:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1280850.0 | 2014-01-28T08:50:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1281735.0 | 2014-01-28T17:35:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1291240.0 | 2014-01-29T12:40:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1291455.0 | 2014-01-29T14:55:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1290850.0 | 2014-01-29T08:50:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1291735.0 | 2014-01-29T17:35:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1301240.0 | 2014-01-30T12:40:00.000+0000 | 38.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1301455.0 | 2014-01-30T14:55:00.000+0000 | 6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1300850.0 | 2014-01-30T08:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1301735.0 | 2014-01-30T17:35:00.000+0000 | 57.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1311240.0 | 2014-01-31T12:40:00.000+0000 | 31.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1311455.0 | 2014-01-31T14:55:00.000+0000 | 22.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1310850.0 | 2014-01-31T08:50:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 1311735.0 | 2014-01-31T17:35:00.000+0000 | 6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2011305.0 | 2014-02-01T13:05:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2011805.0 | 2014-02-01T18:05:00.000+0000 | -6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2010950.0 | 2014-02-01T09:50:00.000+0000 | 4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2021455.0 | 2014-02-02T14:55:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2021235.0 | 2014-02-02T12:35:00.000+0000 | 26.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2021650.0 | 2014-02-02T16:50:00.000+0000 | 77.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2031240.0 | 2014-02-03T12:40:00.000+0000 | 30.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2031455.0 | 2014-02-03T14:55:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2030850.0 | 2014-02-03T08:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2031735.0 | 2014-02-03T17:35:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2041240.0 | 2014-02-04T12:40:00.000+0000 | 56.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2041455.0 | 2014-02-04T14:55:00.000+0000 | 23.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2040850.0 | 2014-02-04T08:50:00.000+0000 | -1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2041735.0 | 2014-02-04T17:35:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2051240.0 | 2014-02-05T12:40:00.000+0000 | 33.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2051455.0 | 2014-02-05T14:55:00.000+0000 | 14.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2050850.0 | 2014-02-05T08:50:00.000+0000 | 19.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2051735.0 | 2014-02-05T17:35:00.000+0000 | 26.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2061240.0 | 2014-02-06T12:40:00.000+0000 | 43.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2061455.0 | 2014-02-06T14:55:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2060850.0 | 2014-02-06T08:50:00.000+0000 | 10.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2061735.0 | 2014-02-06T17:35:00.000+0000 | 34.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2071240.0 | 2014-02-07T12:40:00.000+0000 | 88.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2071455.0 | 2014-02-07T14:55:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2070850.0 | 2014-02-07T08:50:00.000+0000 | 1.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2071735.0 | 2014-02-07T17:35:00.000+0000 | 20.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2081305.0 | 2014-02-08T13:05:00.000+0000 | -6.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2081805.0 | 2014-02-08T18:05:00.000+0000 | 0.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2080950.0 | 2014-02-08T09:50:00.000+0000 | -3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2091235.0 | 2014-02-09T12:35:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2091455.0 | 2014-02-09T14:55:00.000+0000 | 2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2091735.0 | 2014-02-09T17:35:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2101240.0 | 2014-02-10T12:40:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2101455.0 | 2014-02-10T14:55:00.000+0000 | 14.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2100850.0 | 2014-02-10T08:50:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2101735.0 | 2014-02-10T17:35:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2111240.0 | 2014-02-11T12:40:00.000+0000 | 145.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2111455.0 | 2014-02-11T14:55:00.000+0000 | -2.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2110850.0 | 2014-02-11T08:50:00.000+0000 | 96.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2111735.0 | 2014-02-11T17:35:00.000+0000 | 25.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2121240.0 | 2014-02-12T12:40:00.000+0000 | 8.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2121455.0 | 2014-02-12T14:55:00.000+0000 | 29.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2120850.0 | 2014-02-12T08:50:00.000+0000 | 3.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2121735.0 | 2014-02-12T17:35:00.000+0000 | 39.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2131350.0 | 2014-02-13T13:50:00.000+0000 | 5.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2131610.0 | 2014-02-13T16:10:00.000+0000 | 11.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2130810.0 | 2014-02-13T08:10:00.000+0000 | -4.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2131800.0 | 2014-02-13T18:00:00.000+0000 | 9.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2141610.0 | 2014-02-14T16:10:00.000+0000 | 37.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2140810.0 | 2014-02-14T08:10:00.000+0000 | 13.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2141350.0 | 2014-02-14T13:50:00.000+0000 | 46.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
| 2141800.0 | 2014-02-14T18:00:00.000+0000 | 21.0 | 409.0 | BNA | MSY | Nashville | New Orleans | TN | LA |
Building the Graph
Now that we've imported our data, we're going to need to build our graph. To do so we're going to do two things. We are going to build the structure of the vertices (or nodes) and we're going to build the structure of the edges. What's awesome about GraphFrames is that this process is incredibly simple.
- Rename IATA airport code to id in the Vertices Table
- Start and End airports to src and dst for the Edges Table (flights)
These are required naming conventions for vertices and edges in GraphFrames.
WARNING: If the graphframes package, required in the cell below, is not installed, follow the instructions here.
// Note, ensure you have already installed the GraphFrames spack-package
import org.apache.spark.sql.functions._
import org.graphframes._
// Create Vertices (airports) and Edges (flights)
val tripVertices = airports.withColumnRenamed("IATA", "id").distinct()
val tripEdges = departureDelays_geo.select("tripid", "delay", "src", "dst", "city_dst", "state_dst")
// Cache Vertices and Edges
tripEdges.cache()
tripVertices.cache()
import org.apache.spark.sql.functions._
import org.graphframes._
tripVertices: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: string, City: string ... 2 more fields]
tripEdges: org.apache.spark.sql.DataFrame = [tripid: int, delay: int ... 4 more fields]
res5: tripVertices.type = [id: string, City: string ... 2 more fields]
// Vertices
// The vertices of our graph are the airports
display(tripVertices)
| id | City | State | Country |
|---|---|---|---|
| FAT | Fresno | CA | USA |
| CMH | Columbus | OH | USA |
| PHX | Phoenix | AZ | USA |
| PAH | Paducah | KY | USA |
| COS | Colorado Springs | CO | USA |
| RNO | Reno | NV | USA |
| MYR | Myrtle Beach | SC | USA |
| VLD | Valdosta | GA | USA |
| BPT | Beaumont | TX | USA |
| CAE | Columbia | SC | USA |
| PSC | Pasco | WA | USA |
| SRQ | Sarasota | FL | USA |
| LAX | Los Angeles | CA | USA |
| DAY | Dayton | OH | USA |
| AVP | Wilkes-Barre | PA | USA |
| MFR | Medford | OR | USA |
| JFK | New York | NY | USA |
| BNA | Nashville | TN | USA |
| CLT | Charlotte | NC | USA |
| LAS | Las Vegas | NV | USA |
| BDL | Hartford | CT | USA |
| ILG | Wilmington | DE | USA |
| ACT | Waco | TX | USA |
| ATW | Appleton | WI | USA |
| RHI | Rhinelander | WI | USA |
| PWM | Portland | ME | USA |
| SJT | San Angelo | TX | USA |
| APN | Alpena | MI | USA |
| GRB | Green Bay | WI | USA |
| CAK | Akron | OH | USA |
| LAN | Lansing | MI | USA |
| FLL | Fort Lauderdale | FL | USA |
| MSY | New Orleans | LA | USA |
| SAT | San Antonio | TX | USA |
| TPA | Tampa | FL | USA |
| COD | Cody | WY | USA |
| MOD | Modesto | CA | USA |
| GTR | Columbus | MS | USA |
| BTV | Burlington | VT | USA |
| RDD | Redding | CA | USA |
| HLN | Helena | MT | USA |
| CPR | Casper | WY | USA |
| FWA | Fort Wayne | IN | USA |
| IMT | Iron Mountain | MI | USA |
| DTW | Detroit | MI | USA |
| BZN | Bozeman | MT | USA |
| SBN | South Bend | IN | USA |
| SPS | Wichita Falls | TX | USA |
| BFL | Bakersfield | CA | USA |
| HOB | Hobbs | NM | USA |
| TVC | Traverse City | MI | USA |
| CLE | Cleveland | OH | USA |
| ABR | Aberdeen | SD | USA |
| CHS | Charleston | SC | USA |
| GUC | Gunnison | CO | USA |
| IND | Indianapolis | IN | USA |
| SDF | Louisville | KY | USA |
| SAN | San Diego | CA | USA |
| RSW | Fort Myers | FL | USA |
| BOS | Boston | MA | USA |
| TUL | Tulsa | OK | USA |
| AGS | Augusta | GA | USA |
| MOB | Mobile | AL | USA |
| TUS | Tucson | AZ | USA |
| KTN | Ketchikan | AK | USA |
| BTR | Baton Rouge | LA | USA |
| PNS | Pensacola | FL | USA |
| ABQ | Albuquerque | NM | USA |
| LGA | New York | NY | USA |
| DAL | Dallas | TX | USA |
| JNU | Juneau | AK | USA |
| MAF | Midland | TX | USA |
| RIC | Richmond | VA | USA |
| FAR | Fargo | ND | USA |
| MTJ | Montrose | CO | USA |
| AMA | Amarillo | TX | USA |
| ROC | Rochester | NY | USA |
| SHV | Shreveport | LA | USA |
| YAK | Yakutat | AK | USA |
| CSG | Columbus | GA | USA |
| ALO | Waterloo | IA | USA |
| DRO | Durango | CO | USA |
| CRP | Corpus Christi | TX | USA |
| FNT | Flint | MI | USA |
| GSO | Greensboro | NC | USA |
| LWS | Lewiston | ID | USA |
| TOL | Toledo | OH | USA |
| GTF | Great Falls | MT | USA |
| MKE | Milwaukee | WI | USA |
| RKS | Rock Springs | WY | USA |
| STL | St. Louis | MO | USA |
| MHT | Manchester | NH | USA |
| CRW | Charleston | WV | USA |
| SLC | Salt Lake City | UT | USA |
| ACV | Eureka | CA | USA |
| DFW | Dallas | TX | USA |
| OME | Nome | AK | USA |
| ORF | Norfolk | VA | USA |
| RDU | Raleigh | NC | USA |
| SYR | Syracuse | NY | USA |
| BQK | Brunswick | GA | USA |
| ROA | Roanoke | VA | USA |
| OKC | Oklahoma City | OK | USA |
| EYW | Key West | FL | USA |
| BOI | Boise | ID | USA |
| ABY | Albany | GA | USA |
| PIA | Peoria | IL | USA |
| GRI | Grand Island | NE | USA |
| PBI | West Palm Beach | FL | USA |
| FSM | Fort Smith | AR | USA |
| TYS | Knoxville | TN | USA |
| DAB | Daytona Beach | FL | USA |
| TTN | Trenton | NJ | USA |
| MDT | Harrisburg | PA | USA |
| AUS | Austin | TX | USA |
| DCA | Washington DC | null | USA |
| PIH | Pocatello | ID | USA |
| GCC | Gillette | WY | USA |
| BUR | Burbank | CA | USA |
| GRK | Killeen | TX | USA |
| LBB | Lubbock | TX | USA |
| JAN | Jackson | MS | USA |
| MSP | Minneapolis | MN | USA |
| SAF | Santa Fe | NM | USA |
| HPN | White Plains | NY | USA |
| DHN | Dothan | AL | USA |
| PSP | Palm Springs | CA | USA |
| INL | International Falls | MN | USA |
| CIC | Chico | CA | USA |
| EGE | Vail | CO | USA |
| CLD | Carlsbad | CA | USA |
| RST | Rochester | MN | USA |
| DEN | Denver | CO | USA |
| EUG | Eugene | OR | USA |
| LFT | Lafayette | LA | USA |
| PVD | Providence | RI | USA |
| DBQ | Dubuque | IA | USA |
| SAV | Savannah | GA | USA |
| SFO | San Francisco | CA | USA |
| JAX | Jacksonville | FL | USA |
| LIT | Little Rock | AR | USA |
| IDA | Idaho Falls | ID | USA |
| TYR | Tyler | TX | USA |
| ANC | Anchorage | AK | USA |
| HRL | Harlingen | TX | USA |
| LMT | Klamath Falls | OR | USA |
| PHF | Newport News | VA | USA |
| HOU | Houston | TX | USA |
| SPI | Springfield | IL | USA |
| MOT | Minot | ND | USA |
| BTM | Butte | MT | USA |
| SMF | Sacramento | CA | USA |
| HIB | Hibbing | MN | USA |
| ROW | Roswell | NM | USA |
| MBS | Saginaw | MI | USA |
| ILM | Wilmington | NC | USA |
| LGB | Long Beach | CA | USA |
| IAD | Washington DC | null | USA |
| ICT | Wichita | KS | USA |
| BGR | Bangor | ME | USA |
| ELP | El Paso | TX | USA |
| SUX | Sioux City | IA | USA |
| HSV | Huntsville | AL | USA |
| SIT | Sitka | AK | USA |
| CWA | Wausau | WI | USA |
| LCH | Lake Charles | LA | USA |
| CMI | Champaign | IL | USA |
| ELM | Elmira | NY | USA |
| CLL | College Station | TX | USA |
| VPS | Fort Walton Beach | FL | USA |
| MSN | Madison | WI | USA |
| MHK | Manhattan | KS | USA |
| OAJ | Jacksonville | NC | USA |
| EKO | Elko | NV | USA |
| FSD | Sioux Falls | SD | USA |
| EWR | Newark | NJ | USA |
| MEM | Memphis | TN | USA |
| ADQ | Kodiak | AK | USA |
| GGG | Longview | TX | USA |
| BLI | Bellingham | WA | USA |
| OMA | Omaha | NE | USA |
| ATL | Atlanta | GA | USA |
| SJC | San Jose | CA | USA |
| SBA | Santa Barbara | CA | USA |
| ONT | Ontario | CA | USA |
| EAU | Eau Claire | WI | USA |
| GPT | Gulfport | MS | USA |
| SUN | Sun Valley | ID | USA |
| CHO | Charlottesville | VA | USA |
| MSO | Missoula | MT | USA |
| CMX | Hancock | MI | USA |
| ORD | Chicago | IL | USA |
| EVV | Evansville | IN | USA |
| MLU | Monroe | LA | USA |
| LAW | Lawton | OK | USA |
| BRW | Barrow | AK | USA |
| SBP | San Luis Obispo | CA | USA |
| LIH | Lihue, Kauai | HI | USA |
| GJT | Grand Junction | CO | USA |
| FAI | Fairbanks | AK | USA |
| BIS | Bismarck | ND | USA |
| SNA | Orange County | CA | USA |
| LAR | Laramie | WY | USA |
| JLN | Joplin | MO | USA |
| LRD | Laredo | TX | USA |
| LEX | Lexington | KY | USA |
| SGU | St. George | UT | USA |
| MCI | Kansas City | MO | USA |
| AEX | Alexandria | LA | USA |
| ISN | Williston | ND | USA |
| TXK | Texarkana | AR | USA |
| BIL | Billings | MT | USA |
| CID | Cedar Rapids | IA | USA |
| PDX | Portland | OR | USA |
| ABE | Allentown | PA | USA |
| DIK | Dickinson | ND | USA |
| ART | Watertown | NY | USA |
| GCK | Garden City | KS | USA |
| BET | Bethel | AK | USA |
| AVL | Asheville | NC | USA |
| MCO | Orlando | FL | USA |
| GSP | Greenville | SC | USA |
| TWF | Twin Falls | ID | USA |
| MKG | Muskegon | MI | USA |
| FAY | Fayetteville | NC | USA |
| SCE | State College | PA | USA |
| EWN | New Bern | NC | USA |
| XNA | Fayetteville | AR | USA |
| MRY | Monterey | CA | USA |
| MLB | Melbourne | FL | USA |
| HNL | Honolulu, Oahu | HI | USA |
| CVG | Cincinnati | OH | USA |
| RAP | Rapid City | SD | USA |
| AZO | Kalamazoo | MI | USA |
| WRG | Wrangell | AK | USA |
| ISP | Islip | NY | USA |
| FLG | Flagstaff | AZ | USA |
| BHM | Birmingham | AL | USA |
| ALB | Albany | NY | USA |
| SEA | Seattle | WA | USA |
| GRR | Grand Rapids | MI | USA |
| CHA | Chattanooga | TN | USA |
| IAH | Houston | TX | USA |
| SMX | Santa Maria | CA | USA |
| MDW | Chicago | IL | USA |
| MQT | Marquette | MI | USA |
| LNK | Lincoln | NE | USA |
| RDM | Redmond | OR | USA |
| DLH | Duluth | MN | USA |
| DSM | Des Moines | IA | USA |
| OAK | Oakland | CA | USA |
| PHL | Philadelphia | PA | USA |
| FCA | Kalispell | MT | USA |
| MFE | McAllen | TX | USA |
| OGG | Kahului, Maui | HI | USA |
| YUM | Yuma | AZ | USA |
| BMI | Bloomington | IL | USA |
| GEG | Spokane | WA | USA |
| TLH | Tallahassee | FL | USA |
| LSE | La Crosse | WI | USA |
| MIA | Miami | FL | USA |
| BRO | Brownsville | TX | USA |
| JAC | Jackson Hole | WY | USA |
| CDV | Cordova | AK | USA |
| TRI | Tri-City Airport | TN | USA |
| SWF | Newburgh | NY | USA |
| MGM | Montgomery | AL | USA |
| BWI | Baltimore | MD | USA |
| SGF | Springfield | MO | USA |
| GFK | Grand Forks | ND | USA |
| GNV | Gainesville | FL | USA |
| OTH | North Bend | OR | USA |
| PIT | Pittsburgh | PA | USA |
| BJI | Bemidji | MN | USA |
| ASE | Aspen | CO | USA |
| BUF | Buffalo | NY | USA |
| COU | Columbia | MO | USA |
| ABI | Abilene | TX | USA |
| MLI | Moline | IL | USA |
// Edges
// The edges of our graph are the flights between airports
display(tripEdges)
| tripid | delay | src | dst | city_dst | state_dst |
|---|---|---|---|---|---|
| 1011111.0 | -5.0 | MSP | INL | International Falls | MN |
| 1021111.0 | 7.0 | MSP | INL | International Falls | MN |
| 1031111.0 | 0.0 | MSP | INL | International Falls | MN |
| 1041925.0 | 0.0 | MSP | INL | International Falls | MN |
| 1061115.0 | 33.0 | MSP | INL | International Falls | MN |
| 1071115.0 | 23.0 | MSP | INL | International Falls | MN |
| 1081115.0 | -9.0 | MSP | INL | International Falls | MN |
| 1091115.0 | 11.0 | MSP | INL | International Falls | MN |
| 1101115.0 | -3.0 | MSP | INL | International Falls | MN |
| 1112015.0 | -7.0 | MSP | INL | International Falls | MN |
| 1121925.0 | -5.0 | MSP | INL | International Falls | MN |
| 1131115.0 | -3.0 | MSP | INL | International Falls | MN |
| 1141115.0 | -6.0 | MSP | INL | International Falls | MN |
| 1151115.0 | -7.0 | MSP | INL | International Falls | MN |
| 1161115.0 | -3.0 | MSP | INL | International Falls | MN |
| 1171115.0 | 4.0 | MSP | INL | International Falls | MN |
| 1182015.0 | -5.0 | MSP | INL | International Falls | MN |
| 1191925.0 | -7.0 | MSP | INL | International Falls | MN |
| 1201115.0 | -6.0 | MSP | INL | International Falls | MN |
| 1211115.0 | 0.0 | MSP | INL | International Falls | MN |
| 1221115.0 | -4.0 | MSP | INL | International Falls | MN |
| 1231115.0 | -4.0 | MSP | INL | International Falls | MN |
| 1241115.0 | -3.0 | MSP | INL | International Falls | MN |
| 1252015.0 | -12.0 | MSP | INL | International Falls | MN |
| 1261925.0 | -5.0 | MSP | INL | International Falls | MN |
| 1271115.0 | 0.0 | MSP | INL | International Falls | MN |
| 1281115.0 | -8.0 | MSP | INL | International Falls | MN |
| 1291115.0 | -2.0 | MSP | INL | International Falls | MN |
| 1301115.0 | 0.0 | MSP | INL | International Falls | MN |
| 1311115.0 | -3.0 | MSP | INL | International Falls | MN |
| 2012015.0 | -4.0 | MSP | INL | International Falls | MN |
| 2022015.0 | 0.0 | MSP | INL | International Falls | MN |
| 2031115.0 | -7.0 | MSP | INL | International Falls | MN |
| 2041115.0 | -6.0 | MSP | INL | International Falls | MN |
| 2051115.0 | -4.0 | MSP | INL | International Falls | MN |
| 2061115.0 | -2.0 | MSP | INL | International Falls | MN |
| 2071115.0 | -15.0 | MSP | INL | International Falls | MN |
| 2082015.0 | -4.0 | MSP | INL | International Falls | MN |
| 2091925.0 | 1.0 | MSP | INL | International Falls | MN |
| 2101115.0 | -3.0 | MSP | INL | International Falls | MN |
| 2111115.0 | -7.0 | MSP | INL | International Falls | MN |
| 2121115.0 | -2.0 | MSP | INL | International Falls | MN |
| 2131115.0 | -3.0 | MSP | INL | International Falls | MN |
| 2141115.0 | -11.0 | MSP | INL | International Falls | MN |
| 2152015.0 | 16.0 | MSP | INL | International Falls | MN |
| 2161925.0 | 169.0 | MSP | INL | International Falls | MN |
| 2171115.0 | 27.0 | MSP | INL | International Falls | MN |
| 2181115.0 | 96.0 | MSP | INL | International Falls | MN |
| 2191115.0 | -9.0 | MSP | INL | International Falls | MN |
| 2201115.0 | -6.0 | MSP | INL | International Falls | MN |
| 2211115.0 | -4.0 | MSP | INL | International Falls | MN |
| 2222015.0 | -4.0 | MSP | INL | International Falls | MN |
| 2231925.0 | -3.0 | MSP | INL | International Falls | MN |
| 2241115.0 | -2.0 | MSP | INL | International Falls | MN |
| 2251115.0 | -6.0 | MSP | INL | International Falls | MN |
| 2261115.0 | -8.0 | MSP | INL | International Falls | MN |
| 2271115.0 | -8.0 | MSP | INL | International Falls | MN |
| 2281115.0 | 5.0 | MSP | INL | International Falls | MN |
| 3012015.0 | -4.0 | MSP | INL | International Falls | MN |
| 3022000.0 | 0.0 | MSP | INL | International Falls | MN |
| 3031115.0 | 17.0 | MSP | INL | International Falls | MN |
| 3041115.0 | 0.0 | MSP | INL | International Falls | MN |
| 3051115.0 | -7.0 | MSP | INL | International Falls | MN |
| 3061115.0 | -8.0 | MSP | INL | International Falls | MN |
| 3071115.0 | -10.0 | MSP | INL | International Falls | MN |
| 3082000.0 | -11.0 | MSP | INL | International Falls | MN |
| 3092000.0 | -9.0 | MSP | INL | International Falls | MN |
| 3101115.0 | -10.0 | MSP | INL | International Falls | MN |
| 3111115.0 | -8.0 | MSP | INL | International Falls | MN |
| 3121115.0 | -6.0 | MSP | INL | International Falls | MN |
| 3131115.0 | -8.0 | MSP | INL | International Falls | MN |
| 3141115.0 | -5.0 | MSP | INL | International Falls | MN |
| 3152000.0 | -11.0 | MSP | INL | International Falls | MN |
| 3162000.0 | -10.0 | MSP | INL | International Falls | MN |
| 3171115.0 | 25.0 | MSP | INL | International Falls | MN |
| 3181115.0 | 2.0 | MSP | INL | International Falls | MN |
| 3191115.0 | -5.0 | MSP | INL | International Falls | MN |
| 3201115.0 | -6.0 | MSP | INL | International Falls | MN |
| 3211115.0 | 0.0 | MSP | INL | International Falls | MN |
| 3222000.0 | -10.0 | MSP | INL | International Falls | MN |
| 3232000.0 | -9.0 | MSP | INL | International Falls | MN |
| 3241115.0 | -9.0 | MSP | INL | International Falls | MN |
| 3251115.0 | -4.0 | MSP | INL | International Falls | MN |
| 3261115.0 | -5.0 | MSP | INL | International Falls | MN |
| 3271115.0 | 9.0 | MSP | INL | International Falls | MN |
| 3281115.0 | -7.0 | MSP | INL | International Falls | MN |
| 3292000.0 | -19.0 | MSP | INL | International Falls | MN |
| 3302000.0 | -10.0 | MSP | INL | International Falls | MN |
| 3311115.0 | -8.0 | MSP | INL | International Falls | MN |
| 2011230.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 2010719.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 2021230.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 2020709.0 | 59.0 | EWR | MSY | New Orleans | LA |
| 2021654.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2030719.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 2031659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2031230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2032043.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2041230.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 2040719.0 | 168.0 | EWR | MSY | New Orleans | LA |
| 2041730.0 | 88.0 | EWR | MSY | New Orleans | LA |
| 2042043.0 | 106.0 | EWR | MSY | New Orleans | LA |
| 2051659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2050719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2050929.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2052043.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 2060719.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 2061659.0 | 82.0 | EWR | MSY | New Orleans | LA |
| 2061230.0 | 61.0 | EWR | MSY | New Orleans | LA |
| 2062043.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 2070719.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2071659.0 | 19.0 | EWR | MSY | New Orleans | LA |
| 2071230.0 | 27.0 | EWR | MSY | New Orleans | LA |
| 2072048.0 | 47.0 | EWR | MSY | New Orleans | LA |
| 2081230.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 2080719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2091229.0 | 95.0 | EWR | MSY | New Orleans | LA |
| 2091654.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2090709.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 2092043.0 | 32.0 | EWR | MSY | New Orleans | LA |
| 2100719.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 2101659.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 2101230.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2102043.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2111230.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 2110719.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 2111730.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 2112043.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 2120719.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 2121230.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2120929.0 | 89.0 | EWR | MSY | New Orleans | LA |
| 2122043.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 2130738.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2132041.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2140729.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2142041.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2151206.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2150659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2160705.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2170729.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2172041.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 2180738.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2182041.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 2190727.0 | 15.0 | EWR | MSY | New Orleans | LA |
| 2200738.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 2202041.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 2210729.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 2212041.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2221206.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 2220659.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2232041.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2230705.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 2240729.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2242041.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 2250738.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2260727.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 2262041.0 | 174.0 | EWR | MSY | New Orleans | LA |
| 2270738.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2272041.0 | 32.0 | EWR | MSY | New Orleans | LA |
| 2280729.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 2282041.0 | 49.0 | EWR | MSY | New Orleans | LA |
| 2281000.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 2051230.0 | 216.0 | EWR | MSY | New Orleans | LA |
| 2131536.0 | 273.0 | EWR | MSY | New Orleans | LA |
| 2141536.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2151902.0 | 31.0 | EWR | MSY | New Orleans | LA |
| 2151536.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 2162041.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2161536.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 2171536.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 2181536.0 | 26.0 | EWR | MSY | New Orleans | LA |
| 2191536.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 2201536.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 2211536.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 2221900.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 2221536.0 | 65.0 | EWR | MSY | New Orleans | LA |
| 2231536.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 2241536.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2251536.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2261536.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2271536.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2281536.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2010730.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2021815.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2031815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2041815.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2051815.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2061815.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2071815.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2080730.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 2091815.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2101815.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2111815.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 2121815.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 2131805.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2141635.0 | 52.0 | EWR | MSY | New Orleans | LA |
| 2150730.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2161635.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2171635.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 2181635.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 2191635.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2201635.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2211635.0 | 292.0 | EWR | MSY | New Orleans | LA |
| 2220730.0 | 28.0 | EWR | MSY | New Orleans | LA |
| 2231635.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 2241635.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 2251635.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2261635.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 2271635.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 2281635.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3011206.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3010659.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3022041.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3020705.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3030729.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3032041.0 | 113.0 | EWR | MSY | New Orleans | LA |
| 3040738.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3050727.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3052041.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 3060705.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3061252.0 | 67.0 | EWR | MSY | New Orleans | LA |
| 3062100.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 3070705.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 3071252.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3072100.0 | 13.0 | EWR | MSY | New Orleans | LA |
| 3080705.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3081250.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3091255.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 3092059.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3100705.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3101252.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 3102059.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3112059.0 | 181.0 | EWR | MSY | New Orleans | LA |
| 3111252.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 3122059.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 3121252.0 | 161.0 | EWR | MSY | New Orleans | LA |
| 3130705.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3131252.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3132059.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3140705.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 3141252.0 | 39.0 | EWR | MSY | New Orleans | LA |
| 3142059.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3150700.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3151250.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 3161255.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3162059.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3170705.0 | -11.0 | EWR | MSY | New Orleans | LA |
| 3171252.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 3172059.0 | 132.0 | EWR | MSY | New Orleans | LA |
| 3182059.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3181252.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3192059.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 3191252.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3200705.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3201252.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3202059.0 | 77.0 | EWR | MSY | New Orleans | LA |
| 3210705.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3211252.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 3212059.0 | 11.0 | EWR | MSY | New Orleans | LA |
| 3220705.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3221250.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 3221600.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3231255.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3240705.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3241252.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3242059.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 3252059.0 | 121.0 | EWR | MSY | New Orleans | LA |
| 3251252.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 3262059.0 | 49.0 | EWR | MSY | New Orleans | LA |
| 3261252.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3270705.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3271252.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3272059.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3280705.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3281252.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3282059.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3291250.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3290705.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3291600.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 3301255.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 3302059.0 | 166.0 | EWR | MSY | New Orleans | LA |
| 3310705.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3311252.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 3312059.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 3011902.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 3011536.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3021536.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 3031536.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 3041536.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 3051536.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3090715.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 3110705.0 | -11.0 | EWR | MSY | New Orleans | LA |
| 3120659.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3160715.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3180705.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 3190659.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3230715.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3250705.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 3260659.0 | 63.0 | EWR | MSY | New Orleans | LA |
| 3300715.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3010730.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3021635.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 3031635.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 3041635.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 3051635.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3061635.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3071635.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3080730.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3091825.0 | 45.0 | EWR | MSY | New Orleans | LA |
| 3101825.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 3111825.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3121825.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 3131825.0 | 123.0 | EWR | MSY | New Orleans | LA |
| 3141825.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 3150730.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3161825.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 3171825.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 3181825.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 3191825.0 | 223.0 | EWR | MSY | New Orleans | LA |
| 3201825.0 | 178.0 | EWR | MSY | New Orleans | LA |
| 3211825.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3220730.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3231825.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 3241825.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 3251825.0 | 222.0 | EWR | MSY | New Orleans | LA |
| 3261825.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 3271825.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 3281825.0 | 26.0 | EWR | MSY | New Orleans | LA |
| 3290730.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 3301825.0 | 139.0 | EWR | MSY | New Orleans | LA |
| 3311825.0 | 25.0 | EWR | MSY | New Orleans | LA |
| 1020705.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1030705.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1040655.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1050703.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 1060705.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 1071230.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 1070719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1071730.0 | 161.0 | EWR | MSY | New Orleans | LA |
| 1072043.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1080719.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1081659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1081230.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 1082043.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1090719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1091659.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1091230.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1092043.0 | 63.0 | EWR | MSY | New Orleans | LA |
| 1100719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1101659.0 | 244.0 | EWR | MSY | New Orleans | LA |
| 1101230.0 | 110.0 | EWR | MSY | New Orleans | LA |
| 1102043.0 | 43.0 | EWR | MSY | New Orleans | LA |
| 1111230.0 | 87.0 | EWR | MSY | New Orleans | LA |
| 1110719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1121654.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1121230.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 1120709.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1122043.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1130719.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 1131659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1131230.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 1132043.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 1141230.0 | 30.0 | EWR | MSY | New Orleans | LA |
| 1140719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1141730.0 | 69.0 | EWR | MSY | New Orleans | LA |
| 1142043.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1150719.0 | 42.0 | EWR | MSY | New Orleans | LA |
| 1151659.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 1151230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1152043.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 1160719.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 1161659.0 | -9.0 | EWR | MSY | New Orleans | LA |
| 1161230.0 | -10.0 | EWR | MSY | New Orleans | LA |
| 1162043.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1171659.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 1170719.0 | -4.0 | EWR | MSY | New Orleans | LA |
| 1172043.0 | 54.0 | EWR | MSY | New Orleans | LA |
| 1181230.0 | 20.0 | EWR | MSY | New Orleans | LA |
| 1180719.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1191654.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1191230.0 | 29.0 | EWR | MSY | New Orleans | LA |
| 1190709.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1192043.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1201659.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1200719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1202043.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 1211230.0 | 102.0 | EWR | MSY | New Orleans | LA |
| 1210719.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 1211730.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1212043.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1220719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1221659.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 1221230.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1222043.0 | 70.0 | EWR | MSY | New Orleans | LA |
| 1230719.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 1231659.0 | 94.0 | EWR | MSY | New Orleans | LA |
| 1231230.0 | 111.0 | EWR | MSY | New Orleans | LA |
| 1232043.0 | 13.0 | EWR | MSY | New Orleans | LA |
| 1240719.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1241659.0 | 84.0 | EWR | MSY | New Orleans | LA |
| 1241230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1242043.0 | 56.0 | EWR | MSY | New Orleans | LA |
| 1251230.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1250719.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 1261654.0 | 113.0 | EWR | MSY | New Orleans | LA |
| 1261230.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1260709.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 1262043.0 | 31.0 | EWR | MSY | New Orleans | LA |
| 1271659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1270719.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1281230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1280719.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1281730.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1282043.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1291659.0 | -6.0 | EWR | MSY | New Orleans | LA |
| 1290719.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1292043.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 1300719.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 1301659.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 1301230.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1302043.0 | 93.0 | EWR | MSY | New Orleans | LA |
| 1310719.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 1311659.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1311230.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1312043.0 | 28.0 | EWR | MSY | New Orleans | LA |
| 1171230.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1201250.0 | -12.0 | EWR | MSY | New Orleans | LA |
| 1291230.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1011815.0 | 125.0 | EWR | MSY | New Orleans | LA |
| 1021815.0 | 33.0 | EWR | MSY | New Orleans | LA |
| 1031815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1040755.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1051815.0 | 172.0 | EWR | MSY | New Orleans | LA |
| 1061815.0 | 151.0 | EWR | MSY | New Orleans | LA |
| 1071815.0 | 43.0 | EWR | MSY | New Orleans | LA |
| 1081815.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 1091815.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1101815.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 1110730.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1121815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1131815.0 | -2.0 | EWR | MSY | New Orleans | LA |
| 1141815.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1151815.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1161815.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1171815.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 1180730.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1191815.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1201815.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1211815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1221815.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1231815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1241815.0 | 84.0 | EWR | MSY | New Orleans | LA |
| 1250730.0 | -7.0 | EWR | MSY | New Orleans | LA |
| 1261815.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1271815.0 | -1.0 | EWR | MSY | New Orleans | LA |
| 1281815.0 | 0.0 | EWR | MSY | New Orleans | LA |
| 1291815.0 | -3.0 | EWR | MSY | New Orleans | LA |
| 1301815.0 | -5.0 | EWR | MSY | New Orleans | LA |
| 1311815.0 | -8.0 | EWR | MSY | New Orleans | LA |
| 2011055.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 2021025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 2031025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 2031750.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 2041025.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 2041750.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 2051025.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 2051750.0 | 29.0 | LAS | MSY | New Orleans | LA |
| 2061025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 2061750.0 | 48.0 | LAS | MSY | New Orleans | LA |
| 2071025.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 2071750.0 | 20.0 | LAS | MSY | New Orleans | LA |
| 2081055.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 2091025.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 2091750.0 | 33.0 | LAS | MSY | New Orleans | LA |
| 2101025.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 2101750.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 2111025.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 2111750.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 2121025.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 2121750.0 | 158.0 | LAS | MSY | New Orleans | LA |
| 2131855.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 2131215.0 | 23.0 | LAS | MSY | New Orleans | LA |
| 2141855.0 | 22.0 | LAS | MSY | New Orleans | LA |
| 2141215.0 | 18.0 | LAS | MSY | New Orleans | LA |
| 2150935.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 2151635.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 2161855.0 | 58.0 | LAS | MSY | New Orleans | LA |
| 2161215.0 | 17.0 | LAS | MSY | New Orleans | LA |
| 2171855.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 2171215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2181855.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 2181215.0 | 58.0 | LAS | MSY | New Orleans | LA |
| 2191855.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 2191215.0 | 32.0 | LAS | MSY | New Orleans | LA |
| 2201855.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 2201215.0 | 28.0 | LAS | MSY | New Orleans | LA |
| 2211855.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 2211215.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 2220935.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 2221635.0 | 133.0 | LAS | MSY | New Orleans | LA |
| 2231855.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 2231215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2241855.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 2241215.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 2251855.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 2251215.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 2261855.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 2261215.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 2271855.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2271215.0 | 32.0 | LAS | MSY | New Orleans | LA |
| 2281855.0 | 61.0 | LAS | MSY | New Orleans | LA |
| 2281215.0 | 94.0 | LAS | MSY | New Orleans | LA |
| 3010935.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3011635.0 | 39.0 | LAS | MSY | New Orleans | LA |
| 3021855.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 3021215.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 3031855.0 | 25.0 | LAS | MSY | New Orleans | LA |
| 3031215.0 | 8.0 | LAS | MSY | New Orleans | LA |
| 3041855.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 3041215.0 | 28.0 | LAS | MSY | New Orleans | LA |
| 3051855.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 3051215.0 | 27.0 | LAS | MSY | New Orleans | LA |
| 3061855.0 | 20.0 | LAS | MSY | New Orleans | LA |
| 3061215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3071855.0 | 36.0 | LAS | MSY | New Orleans | LA |
| 3071215.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3081940.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 3080830.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 3091905.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 3090840.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 3101905.0 | 49.0 | LAS | MSY | New Orleans | LA |
| 3100840.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3111905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3110840.0 | 84.0 | LAS | MSY | New Orleans | LA |
| 3121905.0 | 26.0 | LAS | MSY | New Orleans | LA |
| 3120840.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3131905.0 | 37.0 | LAS | MSY | New Orleans | LA |
| 3130840.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 3141905.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3140840.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 3150830.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 3151940.0 | 95.0 | LAS | MSY | New Orleans | LA |
| 3161905.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 3160840.0 | -6.0 | LAS | MSY | New Orleans | LA |
| 3171905.0 | 13.0 | LAS | MSY | New Orleans | LA |
| 3170840.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 3181905.0 | 52.0 | LAS | MSY | New Orleans | LA |
| 3180840.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 3191905.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 3190840.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 3201905.0 | 36.0 | LAS | MSY | New Orleans | LA |
| 3200840.0 | 68.0 | LAS | MSY | New Orleans | LA |
| 3211905.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 3210840.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3220830.0 | 12.0 | LAS | MSY | New Orleans | LA |
| 3221940.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 3231905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3230840.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 3241905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3240840.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 3251905.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3250840.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 3261905.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3260840.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 3271905.0 | 13.0 | LAS | MSY | New Orleans | LA |
| 3270840.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 3281905.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3280840.0 | 33.0 | LAS | MSY | New Orleans | LA |
| 3290830.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 3291940.0 | 231.0 | LAS | MSY | New Orleans | LA |
| 3301905.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 3300840.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 3311905.0 | 19.0 | LAS | MSY | New Orleans | LA |
| 3310840.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1010850.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 1011755.0 | 160.0 | LAS | MSY | New Orleans | LA |
| 1021805.0 | 138.0 | LAS | MSY | New Orleans | LA |
| 1020905.0 | 5.0 | LAS | MSY | New Orleans | LA |
| 1031805.0 | 154.0 | LAS | MSY | New Orleans | LA |
| 1030905.0 | 179.0 | LAS | MSY | New Orleans | LA |
| 1041655.0 | 113.0 | LAS | MSY | New Orleans | LA |
| 1040900.0 | 56.0 | LAS | MSY | New Orleans | LA |
| 1050905.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1051805.0 | 53.0 | LAS | MSY | New Orleans | LA |
| 1061755.0 | 61.0 | LAS | MSY | New Orleans | LA |
| 1060905.0 | 23.0 | LAS | MSY | New Orleans | LA |
| 1071025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1071750.0 | 302.0 | LAS | MSY | New Orleans | LA |
| 1081025.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 1081750.0 | 52.0 | LAS | MSY | New Orleans | LA |
| 1091025.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 1091750.0 | 8.0 | LAS | MSY | New Orleans | LA |
| 1101025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1101750.0 | 92.0 | LAS | MSY | New Orleans | LA |
| 1111055.0 | 31.0 | LAS | MSY | New Orleans | LA |
| 1121025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1121750.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1131025.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 1131750.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 1141025.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 1141750.0 | 127.0 | LAS | MSY | New Orleans | LA |
| 1151025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1151750.0 | -3.0 | LAS | MSY | New Orleans | LA |
| 1161025.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1161750.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1171025.0 | 12.0 | LAS | MSY | New Orleans | LA |
| 1171750.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1181055.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 1191025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1191750.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 1201025.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 1201750.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 1211025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1211750.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 1221025.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 1221750.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1231025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1231750.0 | 30.0 | LAS | MSY | New Orleans | LA |
| 1241025.0 | 43.0 | LAS | MSY | New Orleans | LA |
| 1241750.0 | -4.0 | LAS | MSY | New Orleans | LA |
| 1251055.0 | 5.0 | LAS | MSY | New Orleans | LA |
| 1261025.0 | -1.0 | LAS | MSY | New Orleans | LA |
| 1261750.0 | 27.0 | LAS | MSY | New Orleans | LA |
| 1271025.0 | -2.0 | LAS | MSY | New Orleans | LA |
| 1271750.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 1281025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1281750.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1291025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1291750.0 | -5.0 | LAS | MSY | New Orleans | LA |
| 1301025.0 | 0.0 | LAS | MSY | New Orleans | LA |
| 1301750.0 | 35.0 | LAS | MSY | New Orleans | LA |
| 1311025.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 1311750.0 | 25.0 | LAS | MSY | New Orleans | LA |
| 2011530.0 | -3.0 | MCI | MSY | New Orleans | LA |
| 2021105.0 | -4.0 | MCI | MSY | New Orleans | LA |
| 2031105.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 2041105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 2051105.0 | 6.0 | MCI | MSY | New Orleans | LA |
| 2061105.0 | 40.0 | MCI | MSY | New Orleans | LA |
| 2071105.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2081530.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2091105.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 2101105.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 2111105.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 2121105.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2130800.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 2140830.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 2150750.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2160930.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 2170830.0 | 10.0 | MCI | MSY | New Orleans | LA |
| 2180830.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2190830.0 | -4.0 | MCI | MSY | New Orleans | LA |
| 2200830.0 | 9.0 | MCI | MSY | New Orleans | LA |
| 2210830.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 2220750.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 2230930.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2240830.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 2250830.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 2260830.0 | 251.0 | MCI | MSY | New Orleans | LA |
| 2270830.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 2280830.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 3010750.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 3020930.0 | 35.0 | MCI | MSY | New Orleans | LA |
| 3030830.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3040830.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 3050830.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 3060830.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3070830.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 3081610.0 | 93.0 | MCI | MSY | New Orleans | LA |
| 3090950.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3100950.0 | 8.0 | MCI | MSY | New Orleans | LA |
| 3110950.0 | 36.0 | MCI | MSY | New Orleans | LA |
| 3120950.0 | 68.0 | MCI | MSY | New Orleans | LA |
| 3130950.0 | 13.0 | MCI | MSY | New Orleans | LA |
| 3140950.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3151610.0 | 16.0 | MCI | MSY | New Orleans | LA |
| 3160950.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 3170950.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3180950.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3190950.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3200950.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 3210950.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 3221610.0 | 38.0 | MCI | MSY | New Orleans | LA |
| 3230950.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 3240950.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 3250950.0 | 4.0 | MCI | MSY | New Orleans | LA |
| 3260950.0 | 5.0 | MCI | MSY | New Orleans | LA |
| 3270950.0 | 12.0 | MCI | MSY | New Orleans | LA |
| 3280950.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 3291610.0 | 12.0 | MCI | MSY | New Orleans | LA |
| 3300950.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 3310950.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 1011635.0 | -7.0 | MCI | MSY | New Orleans | LA |
| 1021635.0 | 96.0 | MCI | MSY | New Orleans | LA |
| 1031635.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 1041205.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1051635.0 | 60.0 | MCI | MSY | New Orleans | LA |
| 1061635.0 | -7.0 | MCI | MSY | New Orleans | LA |
| 1071105.0 | 14.0 | MCI | MSY | New Orleans | LA |
| 1081105.0 | 4.0 | MCI | MSY | New Orleans | LA |
| 1091105.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 1101105.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 1111530.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 1121105.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 1131105.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 1141105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1151105.0 | 9.0 | MCI | MSY | New Orleans | LA |
| 1161105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1171105.0 | -1.0 | MCI | MSY | New Orleans | LA |
| 1181530.0 | 48.0 | MCI | MSY | New Orleans | LA |
| 1191105.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 1201105.0 | -4.0 | MCI | MSY | New Orleans | LA |
| 1211105.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 1221105.0 | 19.0 | MCI | MSY | New Orleans | LA |
| 1231105.0 | -2.0 | MCI | MSY | New Orleans | LA |
| 1241105.0 | 72.0 | MCI | MSY | New Orleans | LA |
| 1251530.0 | 5.0 | MCI | MSY | New Orleans | LA |
| 1261105.0 | -5.0 | MCI | MSY | New Orleans | LA |
| 1271105.0 | -6.0 | MCI | MSY | New Orleans | LA |
| 1281105.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 1291105.0 | 0.0 | MCI | MSY | New Orleans | LA |
| 1301105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1311105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3011530.0 | 72.0 | BNA | MSY | New Orleans | LA |
| 3010850.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 3011245.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 3021610.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 3021350.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3021800.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 3031610.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3030810.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3031350.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3031800.0 | -5.0 | BNA | MSY | New Orleans | LA |
| 3041610.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3040810.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 3041350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3041800.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3051610.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3050810.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3051350.0 | 48.0 | BNA | MSY | New Orleans | LA |
| 3051800.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 3061610.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 3060810.0 | -5.0 | BNA | MSY | New Orleans | LA |
| 3061350.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3061800.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 3071610.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3070810.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3071350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3071800.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3081540.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 3081335.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 3080945.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3091620.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3091820.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3091420.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3100820.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3101420.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3101820.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3101620.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3110820.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3111420.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3111820.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 3111620.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 3120820.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3121420.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 3121820.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3121620.0 | 24.0 | BNA | MSY | New Orleans | LA |
| 3130820.0 | 126.0 | BNA | MSY | New Orleans | LA |
| 3131420.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3131820.0 | 55.0 | BNA | MSY | New Orleans | LA |
| 3131620.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 3140820.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 3141420.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 3141820.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 3141620.0 | 36.0 | BNA | MSY | New Orleans | LA |
| 3151335.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3150945.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3151540.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3161620.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3161820.0 | 39.0 | BNA | MSY | New Orleans | LA |
| 3161420.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3170820.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3171420.0 | 112.0 | BNA | MSY | New Orleans | LA |
| 3171820.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3171620.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 3180820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3181420.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3181820.0 | 36.0 | BNA | MSY | New Orleans | LA |
| 3181620.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3190820.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 3191420.0 | 54.0 | BNA | MSY | New Orleans | LA |
| 3191820.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3191620.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 3200820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3201420.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 3201820.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 3201620.0 | 79.0 | BNA | MSY | New Orleans | LA |
| 3210820.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3211420.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3211820.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3211620.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3221335.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 3220945.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3221540.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3231620.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 3231820.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 3231420.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 3240820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3241420.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 3241820.0 | 44.0 | BNA | MSY | New Orleans | LA |
| 3241620.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3250820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3251420.0 | 70.0 | BNA | MSY | New Orleans | LA |
| 3251820.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3251620.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3260820.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3261420.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 3261820.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3261620.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 3270820.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3271420.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3271820.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3271620.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 3280820.0 | -5.0 | BNA | MSY | New Orleans | LA |
| 3281420.0 | 52.0 | BNA | MSY | New Orleans | LA |
| 3281820.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 3281620.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3291335.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3290945.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 3291540.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 3301620.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 3301820.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3301420.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3310820.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 3311420.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 3311820.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3311620.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 1010800.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 1011210.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1011835.0 | 53.0 | BNA | MSY | New Orleans | LA |
| 1021215.0 | 43.0 | BNA | MSY | New Orleans | LA |
| 1021835.0 | 200.0 | BNA | MSY | New Orleans | LA |
| 1020800.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 1031215.0 | 185.0 | BNA | MSY | New Orleans | LA |
| 1031835.0 | 226.0 | BNA | MSY | New Orleans | LA |
| 1030800.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1041420.0 | 174.0 | BNA | MSY | New Orleans | LA |
| 1040835.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1051215.0 | 85.0 | BNA | MSY | New Orleans | LA |
| 1051835.0 | 283.0 | BNA | MSY | New Orleans | LA |
| 1050800.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 1061215.0 | 150.0 | BNA | MSY | New Orleans | LA |
| 1061835.0 | 133.0 | BNA | MSY | New Orleans | LA |
| 1060800.0 | 102.0 | BNA | MSY | New Orleans | LA |
| 1071240.0 | 57.0 | BNA | MSY | New Orleans | LA |
| 1071455.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 1070905.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1071735.0 | 131.0 | BNA | MSY | New Orleans | LA |
| 1081240.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1081455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1080905.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1081735.0 | 34.0 | BNA | MSY | New Orleans | LA |
| 1091240.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 1091455.0 | 24.0 | BNA | MSY | New Orleans | LA |
| 1090905.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1091735.0 | 28.0 | BNA | MSY | New Orleans | LA |
| 1101240.0 | 50.0 | BNA | MSY | New Orleans | LA |
| 1101455.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 1100905.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1101735.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 1111305.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1111805.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1110950.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1121235.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 1121455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1121735.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1131240.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1131455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1130850.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1131735.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 1141240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1141455.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1140850.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 1141735.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1151240.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1151455.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 1150850.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1151735.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1161240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1161455.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1160850.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1161735.0 | 52.0 | BNA | MSY | New Orleans | LA |
| 1171240.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 1171455.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 1170850.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 1171735.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 1181305.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1181805.0 | 42.0 | BNA | MSY | New Orleans | LA |
| 1180950.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1191235.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 1191455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1191735.0 | 64.0 | BNA | MSY | New Orleans | LA |
| 1201240.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 1201455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1200850.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1201735.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 1211240.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 1211455.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1210850.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1211735.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1221240.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1221455.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1220850.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 1221735.0 | 118.0 | BNA | MSY | New Orleans | LA |
| 1231240.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 1231455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1230850.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1231735.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 1241240.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 1241455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1240850.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 1241735.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1251305.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 1251805.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1250950.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1261235.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1261455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1261735.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 1271240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1271455.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 1270850.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1271735.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 1281240.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1281455.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1280850.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1281735.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1291240.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 1291455.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1290850.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 1291735.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1301240.0 | 38.0 | BNA | MSY | New Orleans | LA |
| 1301455.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 1300850.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 1301735.0 | 57.0 | BNA | MSY | New Orleans | LA |
| 1311240.0 | 31.0 | BNA | MSY | New Orleans | LA |
| 1311455.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 1310850.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 1311735.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 2011305.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 2011805.0 | -6.0 | BNA | MSY | New Orleans | LA |
| 2010950.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 2021455.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2021235.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2021650.0 | 77.0 | BNA | MSY | New Orleans | LA |
| 2031240.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 2031455.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2030850.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 2031735.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 2041240.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 2041455.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 2040850.0 | -1.0 | BNA | MSY | New Orleans | LA |
| 2041735.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 2051240.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 2051455.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 2050850.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 2051735.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2061240.0 | 43.0 | BNA | MSY | New Orleans | LA |
| 2061455.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 2060850.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2061735.0 | 34.0 | BNA | MSY | New Orleans | LA |
| 2071240.0 | 88.0 | BNA | MSY | New Orleans | LA |
| 2071455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2070850.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2071735.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 2081305.0 | -6.0 | BNA | MSY | New Orleans | LA |
| 2081805.0 | 0.0 | BNA | MSY | New Orleans | LA |
| 2080950.0 | -3.0 | BNA | MSY | New Orleans | LA |
| 2091235.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2091455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 2091735.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2101240.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 2101455.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 2100850.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2101735.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 2111240.0 | 145.0 | BNA | MSY | New Orleans | LA |
| 2111455.0 | -2.0 | BNA | MSY | New Orleans | LA |
| 2110850.0 | 96.0 | BNA | MSY | New Orleans | LA |
| 2111735.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 2121240.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2121455.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 2120850.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 2121735.0 | 39.0 | BNA | MSY | New Orleans | LA |
| 2131350.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2131610.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2130810.0 | -4.0 | BNA | MSY | New Orleans | LA |
| 2131800.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2141610.0 | 37.0 | BNA | MSY | New Orleans | LA |
| 2140810.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 2141350.0 | 46.0 | BNA | MSY | New Orleans | LA |
| 2141800.0 | 21.0 | BNA | MSY | New Orleans | LA |
// Build `tripGraph` GraphFrame
// This GraphFrame builds up on the vertices and edges based on our trips (flights)
val tripGraph = GraphFrame(tripVertices, tripEdges)
println(tripGraph)
// Build `tripGraphPrime` GraphFrame
// This graphframe contains a smaller subset of data to make it easier to display motifs and subgraphs (below)
val tripEdgesPrime = departureDelays_geo.select("tripid", "delay", "src", "dst")
val tripGraphPrime = GraphFrame(tripVertices, tripEdgesPrime)
GraphFrame(v:[id: string, City: string ... 2 more fields], e:[src: string, dst: string ... 4 more fields])
tripGraph: org.graphframes.GraphFrame = GraphFrame(v:[id: string, City: string ... 2 more fields], e:[src: string, dst: string ... 4 more fields])
tripEdgesPrime: org.apache.spark.sql.DataFrame = [tripid: int, delay: int ... 2 more fields]
tripGraphPrime: org.graphframes.GraphFrame = GraphFrame(v:[id: string, City: string ... 2 more fields], e:[src: string, dst: string ... 2 more fields])
Simple Queries
Let's start with a set of simple graph queries to understand flight performance and departure delays
println(s"Airports: ${tripGraph.vertices.count()}")
println(s"Trips: ${tripGraph.edges.count()}")
Airports: 279
Trips: 1361141
// Finding the longest Delay
val longestDelay = tripGraph.edges.groupBy().max("delay")
display(longestDelay)
| max(delay) |
|---|
| 1642.0 |
1642.0/60.0
res13: Double = 27.366666666666667
// Determining number of on-time / early flights vs. delayed flights
println(s"On-time / Early Flights: ${tripGraph.edges.filter("delay <= 0").count()}")
println(s"Delayed Flights: ${tripGraph.edges.filter("delay > 0").count()}")
On-time / Early Flights: 780469
Delayed Flights: 580672
What flights departing SFO are most likely to have significant delays
Note, delay can be <= 0 meaning the flight left on time or early
//tripGraph.createOrReplaceTempView("tripgraph")
val sfoDelayedTrips = tripGraph.edges.
filter("src = 'SFO' and delay > 0").
groupBy("src", "dst").
avg("delay").
sort(desc("avg(delay)"))
sfoDelayedTrips: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [src: string, dst: string ... 1 more field]
display(sfoDelayedTrips)
| src | dst | avg(delay) |
|---|---|---|
| SFO | OKC | 59.073170731707314 |
| SFO | JAC | 57.13333333333333 |
| SFO | COS | 53.976190476190474 |
| SFO | OTH | 48.09090909090909 |
| SFO | SAT | 47.625 |
| SFO | MOD | 46.80952380952381 |
| SFO | SUN | 46.723404255319146 |
| SFO | CIC | 46.72164948453608 |
| SFO | ABQ | 44.8125 |
| SFO | ASE | 44.285714285714285 |
| SFO | PIT | 43.875 |
| SFO | MIA | 43.81730769230769 |
| SFO | FAT | 43.23972602739726 |
| SFO | MFR | 43.11848341232228 |
| SFO | SBP | 43.09770114942529 |
| SFO | MSP | 42.766917293233085 |
| SFO | BOI | 42.65482233502538 |
| SFO | RDM | 41.98823529411764 |
| SFO | AUS | 41.690677966101696 |
| SFO | SLC | 41.407272727272726 |
| SFO | JFK | 41.01379310344828 |
| SFO | PSP | 40.909909909909906 |
| SFO | PHX | 40.67272727272727 |
| SFO | MRY | 40.61764705882353 |
| SFO | ACV | 40.3728813559322 |
| SFO | LAS | 40.107602339181284 |
| SFO | TUS | 39.853658536585364 |
| SFO | SAN | 38.97361809045226 |
| SFO | SBA | 38.758620689655174 |
| SFO | BFL | 38.51136363636363 |
| SFO | RDU | 38.170731707317074 |
| SFO | STL | 38.13513513513514 |
| SFO | IND | 38.114285714285714 |
| SFO | EUG | 37.573913043478264 |
| SFO | RNO | 36.81372549019608 |
| SFO | BUR | 36.75675675675676 |
| SFO | LGB | 36.752941176470586 |
| SFO | HNL | 36.25367647058823 |
| SFO | LAX | 36.165543071161046 |
| SFO | RDD | 36.11009174311926 |
| SFO | MSY | 35.421052631578945 |
| SFO | SMF | 34.936 |
| SFO | MDW | 34.824742268041234 |
| SFO | FLL | 34.76842105263158 |
| SFO | SEA | 34.68854961832061 |
| SFO | MCI | 34.68571428571428 |
| SFO | DFW | 34.36642599277978 |
| SFO | OGG | 34.171875 |
| SFO | PDX | 34.14430894308943 |
| SFO | ORD | 33.991130820399114 |
| SFO | LIH | 32.93023255813954 |
| SFO | DEN | 32.861491628614914 |
| SFO | PSC | 32.604651162790695 |
| SFO | PHL | 32.440677966101696 |
| SFO | BWI | 31.70212765957447 |
| SFO | ONT | 31.49079754601227 |
| SFO | SNA | 31.18426103646833 |
| SFO | MCO | 31.03488372093023 |
| SFO | MKE | 31.03448275862069 |
| SFO | CLE | 30.979591836734695 |
| SFO | EWR | 30.354285714285716 |
| SFO | BOS | 29.623471882640587 |
| SFO | LMT | 29.233333333333334 |
| SFO | DTW | 28.34722222222222 |
| SFO | IAH | 28.322105263157894 |
| SFO | CVG | 27.03125 |
| SFO | ATL | 26.84860557768924 |
| SFO | IAD | 26.125964010282775 |
| SFO | ANC | 25.5 |
| SFO | BZN | 23.964285714285715 |
| SFO | CLT | 22.636363636363637 |
| SFO | DCA | 21.896103896103895 |
// After displaying tripDelays, use Plot Options to set `state_dst` as a Key.
val tripDelays = tripGraph.edges.filter($"delay" > 0)
display(tripDelays)
| tripid | delay | src | dst | city_dst | state_dst |
|---|---|---|---|---|---|
| 1021111.0 | 7.0 | MSP | INL | International Falls | MN |
| 1061115.0 | 33.0 | MSP | INL | International Falls | MN |
| 1071115.0 | 23.0 | MSP | INL | International Falls | MN |
| 1091115.0 | 11.0 | MSP | INL | International Falls | MN |
| 1171115.0 | 4.0 | MSP | INL | International Falls | MN |
| 2091925.0 | 1.0 | MSP | INL | International Falls | MN |
| 2152015.0 | 16.0 | MSP | INL | International Falls | MN |
| 2161925.0 | 169.0 | MSP | INL | International Falls | MN |
| 2171115.0 | 27.0 | MSP | INL | International Falls | MN |
| 2181115.0 | 96.0 | MSP | INL | International Falls | MN |
| 2281115.0 | 5.0 | MSP | INL | International Falls | MN |
| 3031115.0 | 17.0 | MSP | INL | International Falls | MN |
| 3171115.0 | 25.0 | MSP | INL | International Falls | MN |
| 3181115.0 | 2.0 | MSP | INL | International Falls | MN |
| 3271115.0 | 9.0 | MSP | INL | International Falls | MN |
| 2020709.0 | 59.0 | EWR | MSY | New Orleans | LA |
| 2021654.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2041230.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 2040719.0 | 168.0 | EWR | MSY | New Orleans | LA |
| 2041730.0 | 88.0 | EWR | MSY | New Orleans | LA |
| 2042043.0 | 106.0 | EWR | MSY | New Orleans | LA |
| 2052043.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 2061659.0 | 82.0 | EWR | MSY | New Orleans | LA |
| 2061230.0 | 61.0 | EWR | MSY | New Orleans | LA |
| 2062043.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 2070719.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2071659.0 | 19.0 | EWR | MSY | New Orleans | LA |
| 2071230.0 | 27.0 | EWR | MSY | New Orleans | LA |
| 2072048.0 | 47.0 | EWR | MSY | New Orleans | LA |
| 2091229.0 | 95.0 | EWR | MSY | New Orleans | LA |
| 2092043.0 | 32.0 | EWR | MSY | New Orleans | LA |
| 2100719.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 2101659.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 2111230.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 2110719.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 2120719.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 2120929.0 | 89.0 | EWR | MSY | New Orleans | LA |
| 2122043.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 2182041.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 2190727.0 | 15.0 | EWR | MSY | New Orleans | LA |
| 2202041.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 2220659.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2232041.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2240729.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2260727.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 2262041.0 | 174.0 | EWR | MSY | New Orleans | LA |
| 2270738.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2272041.0 | 32.0 | EWR | MSY | New Orleans | LA |
| 2280729.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 2282041.0 | 49.0 | EWR | MSY | New Orleans | LA |
| 2281000.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 2051230.0 | 216.0 | EWR | MSY | New Orleans | LA |
| 2131536.0 | 273.0 | EWR | MSY | New Orleans | LA |
| 2141536.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2151902.0 | 31.0 | EWR | MSY | New Orleans | LA |
| 2151536.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 2161536.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 2171536.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 2181536.0 | 26.0 | EWR | MSY | New Orleans | LA |
| 2211536.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 2221536.0 | 65.0 | EWR | MSY | New Orleans | LA |
| 2281536.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 2111815.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 2121815.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 2141635.0 | 52.0 | EWR | MSY | New Orleans | LA |
| 2161635.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2171635.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 2191635.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 2211635.0 | 292.0 | EWR | MSY | New Orleans | LA |
| 2220730.0 | 28.0 | EWR | MSY | New Orleans | LA |
| 2251635.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 2261635.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 3032041.0 | 113.0 | EWR | MSY | New Orleans | LA |
| 3052041.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 3061252.0 | 67.0 | EWR | MSY | New Orleans | LA |
| 3062100.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 3072100.0 | 13.0 | EWR | MSY | New Orleans | LA |
| 3081250.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3112059.0 | 181.0 | EWR | MSY | New Orleans | LA |
| 3122059.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 3121252.0 | 161.0 | EWR | MSY | New Orleans | LA |
| 3140705.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 3141252.0 | 39.0 | EWR | MSY | New Orleans | LA |
| 3150700.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3151250.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 3171252.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 3172059.0 | 132.0 | EWR | MSY | New Orleans | LA |
| 3181252.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3192059.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 3202059.0 | 77.0 | EWR | MSY | New Orleans | LA |
| 3211252.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 3212059.0 | 11.0 | EWR | MSY | New Orleans | LA |
| 3231255.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3252059.0 | 121.0 | EWR | MSY | New Orleans | LA |
| 3262059.0 | 49.0 | EWR | MSY | New Orleans | LA |
| 3291600.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 3301255.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 3302059.0 | 166.0 | EWR | MSY | New Orleans | LA |
| 3311252.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 3312059.0 | 7.0 | EWR | MSY | New Orleans | LA |
| 3011902.0 | 64.0 | EWR | MSY | New Orleans | LA |
| 3021536.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 3090715.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 3260659.0 | 63.0 | EWR | MSY | New Orleans | LA |
| 3300715.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 3021635.0 | 16.0 | EWR | MSY | New Orleans | LA |
| 3041635.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 3071635.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3091825.0 | 45.0 | EWR | MSY | New Orleans | LA |
| 3101825.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 3111825.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 3131825.0 | 123.0 | EWR | MSY | New Orleans | LA |
| 3141825.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 3161825.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 3171825.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 3191825.0 | 223.0 | EWR | MSY | New Orleans | LA |
| 3201825.0 | 178.0 | EWR | MSY | New Orleans | LA |
| 3251825.0 | 222.0 | EWR | MSY | New Orleans | LA |
| 3261825.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 3281825.0 | 26.0 | EWR | MSY | New Orleans | LA |
| 3301825.0 | 139.0 | EWR | MSY | New Orleans | LA |
| 3311825.0 | 25.0 | EWR | MSY | New Orleans | LA |
| 1050703.0 | 4.0 | EWR | MSY | New Orleans | LA |
| 1060705.0 | 36.0 | EWR | MSY | New Orleans | LA |
| 1071230.0 | 24.0 | EWR | MSY | New Orleans | LA |
| 1071730.0 | 161.0 | EWR | MSY | New Orleans | LA |
| 1072043.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1081230.0 | 66.0 | EWR | MSY | New Orleans | LA |
| 1082043.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1092043.0 | 63.0 | EWR | MSY | New Orleans | LA |
| 1101659.0 | 244.0 | EWR | MSY | New Orleans | LA |
| 1101230.0 | 110.0 | EWR | MSY | New Orleans | LA |
| 1102043.0 | 43.0 | EWR | MSY | New Orleans | LA |
| 1111230.0 | 87.0 | EWR | MSY | New Orleans | LA |
| 1121230.0 | 21.0 | EWR | MSY | New Orleans | LA |
| 1131230.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 1132043.0 | 51.0 | EWR | MSY | New Orleans | LA |
| 1141230.0 | 30.0 | EWR | MSY | New Orleans | LA |
| 1141730.0 | 69.0 | EWR | MSY | New Orleans | LA |
| 1150719.0 | 42.0 | EWR | MSY | New Orleans | LA |
| 1151659.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 1152043.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 1162043.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1171659.0 | 46.0 | EWR | MSY | New Orleans | LA |
| 1172043.0 | 54.0 | EWR | MSY | New Orleans | LA |
| 1181230.0 | 20.0 | EWR | MSY | New Orleans | LA |
| 1191230.0 | 29.0 | EWR | MSY | New Orleans | LA |
| 1192043.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1202043.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 1211230.0 | 102.0 | EWR | MSY | New Orleans | LA |
| 1221659.0 | 6.0 | EWR | MSY | New Orleans | LA |
| 1222043.0 | 70.0 | EWR | MSY | New Orleans | LA |
| 1230719.0 | 12.0 | EWR | MSY | New Orleans | LA |
| 1231659.0 | 94.0 | EWR | MSY | New Orleans | LA |
| 1231230.0 | 111.0 | EWR | MSY | New Orleans | LA |
| 1232043.0 | 13.0 | EWR | MSY | New Orleans | LA |
| 1241659.0 | 84.0 | EWR | MSY | New Orleans | LA |
| 1242043.0 | 56.0 | EWR | MSY | New Orleans | LA |
| 1250719.0 | 23.0 | EWR | MSY | New Orleans | LA |
| 1261654.0 | 113.0 | EWR | MSY | New Orleans | LA |
| 1261230.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1262043.0 | 31.0 | EWR | MSY | New Orleans | LA |
| 1300719.0 | 2.0 | EWR | MSY | New Orleans | LA |
| 1301659.0 | 9.0 | EWR | MSY | New Orleans | LA |
| 1302043.0 | 93.0 | EWR | MSY | New Orleans | LA |
| 1310719.0 | 34.0 | EWR | MSY | New Orleans | LA |
| 1311230.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1312043.0 | 28.0 | EWR | MSY | New Orleans | LA |
| 1011815.0 | 125.0 | EWR | MSY | New Orleans | LA |
| 1021815.0 | 33.0 | EWR | MSY | New Orleans | LA |
| 1051815.0 | 172.0 | EWR | MSY | New Orleans | LA |
| 1061815.0 | 151.0 | EWR | MSY | New Orleans | LA |
| 1071815.0 | 43.0 | EWR | MSY | New Orleans | LA |
| 1081815.0 | 14.0 | EWR | MSY | New Orleans | LA |
| 1091815.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1101815.0 | 10.0 | EWR | MSY | New Orleans | LA |
| 1141815.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1151815.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1161815.0 | 8.0 | EWR | MSY | New Orleans | LA |
| 1171815.0 | 22.0 | EWR | MSY | New Orleans | LA |
| 1180730.0 | 1.0 | EWR | MSY | New Orleans | LA |
| 1191815.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1201815.0 | 5.0 | EWR | MSY | New Orleans | LA |
| 1221815.0 | 3.0 | EWR | MSY | New Orleans | LA |
| 1241815.0 | 84.0 | EWR | MSY | New Orleans | LA |
| 2021025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 2041025.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 2051750.0 | 29.0 | LAS | MSY | New Orleans | LA |
| 2061025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 2061750.0 | 48.0 | LAS | MSY | New Orleans | LA |
| 2071750.0 | 20.0 | LAS | MSY | New Orleans | LA |
| 2081055.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 2091750.0 | 33.0 | LAS | MSY | New Orleans | LA |
| 2111025.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 2121750.0 | 158.0 | LAS | MSY | New Orleans | LA |
| 2131855.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 2131215.0 | 23.0 | LAS | MSY | New Orleans | LA |
| 2141855.0 | 22.0 | LAS | MSY | New Orleans | LA |
| 2141215.0 | 18.0 | LAS | MSY | New Orleans | LA |
| 2151635.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 2161855.0 | 58.0 | LAS | MSY | New Orleans | LA |
| 2161215.0 | 17.0 | LAS | MSY | New Orleans | LA |
| 2171855.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 2171215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2181855.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 2181215.0 | 58.0 | LAS | MSY | New Orleans | LA |
| 2191215.0 | 32.0 | LAS | MSY | New Orleans | LA |
| 2201855.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 2201215.0 | 28.0 | LAS | MSY | New Orleans | LA |
| 2211855.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 2211215.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 2221635.0 | 133.0 | LAS | MSY | New Orleans | LA |
| 2231215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2241215.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 2251855.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 2251215.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 2261855.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 2261215.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 2271855.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 2271215.0 | 32.0 | LAS | MSY | New Orleans | LA |
| 2281855.0 | 61.0 | LAS | MSY | New Orleans | LA |
| 2281215.0 | 94.0 | LAS | MSY | New Orleans | LA |
| 3010935.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3011635.0 | 39.0 | LAS | MSY | New Orleans | LA |
| 3031855.0 | 25.0 | LAS | MSY | New Orleans | LA |
| 3031215.0 | 8.0 | LAS | MSY | New Orleans | LA |
| 3041215.0 | 28.0 | LAS | MSY | New Orleans | LA |
| 3051215.0 | 27.0 | LAS | MSY | New Orleans | LA |
| 3061855.0 | 20.0 | LAS | MSY | New Orleans | LA |
| 3061215.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3071855.0 | 36.0 | LAS | MSY | New Orleans | LA |
| 3071215.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3080830.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 3091905.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 3101905.0 | 49.0 | LAS | MSY | New Orleans | LA |
| 3111905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3110840.0 | 84.0 | LAS | MSY | New Orleans | LA |
| 3121905.0 | 26.0 | LAS | MSY | New Orleans | LA |
| 3131905.0 | 37.0 | LAS | MSY | New Orleans | LA |
| 3130840.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 3141905.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3150830.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 3151940.0 | 95.0 | LAS | MSY | New Orleans | LA |
| 3171905.0 | 13.0 | LAS | MSY | New Orleans | LA |
| 3170840.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 3181905.0 | 52.0 | LAS | MSY | New Orleans | LA |
| 3180840.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 3201905.0 | 36.0 | LAS | MSY | New Orleans | LA |
| 3200840.0 | 68.0 | LAS | MSY | New Orleans | LA |
| 3220830.0 | 12.0 | LAS | MSY | New Orleans | LA |
| 3221940.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 3231905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3230840.0 | 16.0 | LAS | MSY | New Orleans | LA |
| 3241905.0 | 9.0 | LAS | MSY | New Orleans | LA |
| 3240840.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 3251905.0 | 10.0 | LAS | MSY | New Orleans | LA |
| 3261905.0 | 14.0 | LAS | MSY | New Orleans | LA |
| 3260840.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 3271905.0 | 13.0 | LAS | MSY | New Orleans | LA |
| 3280840.0 | 33.0 | LAS | MSY | New Orleans | LA |
| 3291940.0 | 231.0 | LAS | MSY | New Orleans | LA |
| 3301905.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 3300840.0 | 15.0 | LAS | MSY | New Orleans | LA |
| 3311905.0 | 19.0 | LAS | MSY | New Orleans | LA |
| 1011755.0 | 160.0 | LAS | MSY | New Orleans | LA |
| 1021805.0 | 138.0 | LAS | MSY | New Orleans | LA |
| 1020905.0 | 5.0 | LAS | MSY | New Orleans | LA |
| 1031805.0 | 154.0 | LAS | MSY | New Orleans | LA |
| 1030905.0 | 179.0 | LAS | MSY | New Orleans | LA |
| 1041655.0 | 113.0 | LAS | MSY | New Orleans | LA |
| 1040900.0 | 56.0 | LAS | MSY | New Orleans | LA |
| 1051805.0 | 53.0 | LAS | MSY | New Orleans | LA |
| 1061755.0 | 61.0 | LAS | MSY | New Orleans | LA |
| 1060905.0 | 23.0 | LAS | MSY | New Orleans | LA |
| 1071025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1071750.0 | 302.0 | LAS | MSY | New Orleans | LA |
| 1081025.0 | 7.0 | LAS | MSY | New Orleans | LA |
| 1081750.0 | 52.0 | LAS | MSY | New Orleans | LA |
| 1091750.0 | 8.0 | LAS | MSY | New Orleans | LA |
| 1101750.0 | 92.0 | LAS | MSY | New Orleans | LA |
| 1111055.0 | 31.0 | LAS | MSY | New Orleans | LA |
| 1121025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1141750.0 | 127.0 | LAS | MSY | New Orleans | LA |
| 1151025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1171025.0 | 12.0 | LAS | MSY | New Orleans | LA |
| 1191025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1201750.0 | 6.0 | LAS | MSY | New Orleans | LA |
| 1211025.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1211750.0 | 4.0 | LAS | MSY | New Orleans | LA |
| 1221750.0 | 3.0 | LAS | MSY | New Orleans | LA |
| 1231750.0 | 30.0 | LAS | MSY | New Orleans | LA |
| 1241025.0 | 43.0 | LAS | MSY | New Orleans | LA |
| 1251055.0 | 5.0 | LAS | MSY | New Orleans | LA |
| 1261750.0 | 27.0 | LAS | MSY | New Orleans | LA |
| 1271750.0 | 2.0 | LAS | MSY | New Orleans | LA |
| 1291025.0 | 1.0 | LAS | MSY | New Orleans | LA |
| 1301750.0 | 35.0 | LAS | MSY | New Orleans | LA |
| 1311025.0 | 11.0 | LAS | MSY | New Orleans | LA |
| 1311750.0 | 25.0 | LAS | MSY | New Orleans | LA |
| 2041105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 2051105.0 | 6.0 | MCI | MSY | New Orleans | LA |
| 2061105.0 | 40.0 | MCI | MSY | New Orleans | LA |
| 2071105.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2081530.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2121105.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2140830.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 2150750.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 2170830.0 | 10.0 | MCI | MSY | New Orleans | LA |
| 2180830.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2200830.0 | 9.0 | MCI | MSY | New Orleans | LA |
| 2230930.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 2240830.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 2260830.0 | 251.0 | MCI | MSY | New Orleans | LA |
| 3020930.0 | 35.0 | MCI | MSY | New Orleans | LA |
| 3030830.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3040830.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 3050830.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 3081610.0 | 93.0 | MCI | MSY | New Orleans | LA |
| 3100950.0 | 8.0 | MCI | MSY | New Orleans | LA |
| 3110950.0 | 36.0 | MCI | MSY | New Orleans | LA |
| 3120950.0 | 68.0 | MCI | MSY | New Orleans | LA |
| 3130950.0 | 13.0 | MCI | MSY | New Orleans | LA |
| 3140950.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3151610.0 | 16.0 | MCI | MSY | New Orleans | LA |
| 3180950.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3200950.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 3210950.0 | 7.0 | MCI | MSY | New Orleans | LA |
| 3221610.0 | 38.0 | MCI | MSY | New Orleans | LA |
| 3250950.0 | 4.0 | MCI | MSY | New Orleans | LA |
| 3260950.0 | 5.0 | MCI | MSY | New Orleans | LA |
| 3270950.0 | 12.0 | MCI | MSY | New Orleans | LA |
| 3291610.0 | 12.0 | MCI | MSY | New Orleans | LA |
| 3310950.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 1021635.0 | 96.0 | MCI | MSY | New Orleans | LA |
| 1031635.0 | 3.0 | MCI | MSY | New Orleans | LA |
| 1041205.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1051635.0 | 60.0 | MCI | MSY | New Orleans | LA |
| 1071105.0 | 14.0 | MCI | MSY | New Orleans | LA |
| 1081105.0 | 4.0 | MCI | MSY | New Orleans | LA |
| 1091105.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 1111530.0 | 11.0 | MCI | MSY | New Orleans | LA |
| 1141105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1151105.0 | 9.0 | MCI | MSY | New Orleans | LA |
| 1161105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1181530.0 | 48.0 | MCI | MSY | New Orleans | LA |
| 1211105.0 | 2.0 | MCI | MSY | New Orleans | LA |
| 1221105.0 | 19.0 | MCI | MSY | New Orleans | LA |
| 1241105.0 | 72.0 | MCI | MSY | New Orleans | LA |
| 1251530.0 | 5.0 | MCI | MSY | New Orleans | LA |
| 1301105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 1311105.0 | 1.0 | MCI | MSY | New Orleans | LA |
| 3011530.0 | 72.0 | BNA | MSY | New Orleans | LA |
| 3010850.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 3011245.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 3021610.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 3021350.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3021800.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 3031610.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3041350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3041800.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3050810.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3051350.0 | 48.0 | BNA | MSY | New Orleans | LA |
| 3051800.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 3061610.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 3061350.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3061800.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 3071610.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3071350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3071800.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3081540.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 3080945.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3091820.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3091420.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3101420.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3101820.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 3101620.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3110820.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3111420.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3111820.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 3111620.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 3120820.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3121420.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 3121820.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3121620.0 | 24.0 | BNA | MSY | New Orleans | LA |
| 3130820.0 | 126.0 | BNA | MSY | New Orleans | LA |
| 3131420.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 3131820.0 | 55.0 | BNA | MSY | New Orleans | LA |
| 3131620.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 3141420.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 3141820.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 3141620.0 | 36.0 | BNA | MSY | New Orleans | LA |
| 3150945.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3161620.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3161820.0 | 39.0 | BNA | MSY | New Orleans | LA |
| 3170820.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3171420.0 | 112.0 | BNA | MSY | New Orleans | LA |
| 3171820.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 3171620.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 3181420.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3181820.0 | 36.0 | BNA | MSY | New Orleans | LA |
| 3181620.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3191420.0 | 54.0 | BNA | MSY | New Orleans | LA |
| 3191820.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 3191620.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 3201420.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 3201820.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 3201620.0 | 79.0 | BNA | MSY | New Orleans | LA |
| 3210820.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 3211420.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3211820.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3211620.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3231820.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 3241420.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 3241820.0 | 44.0 | BNA | MSY | New Orleans | LA |
| 3241620.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3251420.0 | 70.0 | BNA | MSY | New Orleans | LA |
| 3251820.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3251620.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 3261420.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 3261820.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3271420.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3271820.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 3271620.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 3281420.0 | 52.0 | BNA | MSY | New Orleans | LA |
| 3281820.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 3281620.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 3291335.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 3290945.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 3291540.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 3301820.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 3301420.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 3311420.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 3311820.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 3311620.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 1011210.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1011835.0 | 53.0 | BNA | MSY | New Orleans | LA |
| 1021215.0 | 43.0 | BNA | MSY | New Orleans | LA |
| 1021835.0 | 200.0 | BNA | MSY | New Orleans | LA |
| 1020800.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 1031215.0 | 185.0 | BNA | MSY | New Orleans | LA |
| 1031835.0 | 226.0 | BNA | MSY | New Orleans | LA |
| 1041420.0 | 174.0 | BNA | MSY | New Orleans | LA |
| 1040835.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1051215.0 | 85.0 | BNA | MSY | New Orleans | LA |
| 1051835.0 | 283.0 | BNA | MSY | New Orleans | LA |
| 1050800.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 1061215.0 | 150.0 | BNA | MSY | New Orleans | LA |
| 1061835.0 | 133.0 | BNA | MSY | New Orleans | LA |
| 1060800.0 | 102.0 | BNA | MSY | New Orleans | LA |
| 1071240.0 | 57.0 | BNA | MSY | New Orleans | LA |
| 1071455.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 1070905.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1071735.0 | 131.0 | BNA | MSY | New Orleans | LA |
| 1081240.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1081455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1081735.0 | 34.0 | BNA | MSY | New Orleans | LA |
| 1091240.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 1091455.0 | 24.0 | BNA | MSY | New Orleans | LA |
| 1091735.0 | 28.0 | BNA | MSY | New Orleans | LA |
| 1101240.0 | 50.0 | BNA | MSY | New Orleans | LA |
| 1101455.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 1100905.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1101735.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 1111805.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1121235.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 1121455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1121735.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1131240.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1131455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1131735.0 | 15.0 | BNA | MSY | New Orleans | LA |
| 1141240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1141735.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1151455.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 1151735.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1161240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1161455.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1161735.0 | 52.0 | BNA | MSY | New Orleans | LA |
| 1171240.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 1171455.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 1170850.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 1171735.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 1181305.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 1181805.0 | 42.0 | BNA | MSY | New Orleans | LA |
| 1180950.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1191235.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 1191455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1191735.0 | 64.0 | BNA | MSY | New Orleans | LA |
| 1201240.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 1201455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1200850.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 1211240.0 | 41.0 | BNA | MSY | New Orleans | LA |
| 1210850.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1211735.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1221240.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1221735.0 | 118.0 | BNA | MSY | New Orleans | LA |
| 1231240.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 1231455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1230850.0 | 18.0 | BNA | MSY | New Orleans | LA |
| 1231735.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 1241240.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 1241455.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 1240850.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 1251305.0 | 27.0 | BNA | MSY | New Orleans | LA |
| 1251805.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 1261455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 1261735.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 1271240.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 1271735.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 1291240.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 1291735.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 1301240.0 | 38.0 | BNA | MSY | New Orleans | LA |
| 1301455.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 1301735.0 | 57.0 | BNA | MSY | New Orleans | LA |
| 1311240.0 | 31.0 | BNA | MSY | New Orleans | LA |
| 1311455.0 | 22.0 | BNA | MSY | New Orleans | LA |
| 1311735.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 2010950.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 2021455.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2021235.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2021650.0 | 77.0 | BNA | MSY | New Orleans | LA |
| 2031240.0 | 30.0 | BNA | MSY | New Orleans | LA |
| 2031455.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2041240.0 | 56.0 | BNA | MSY | New Orleans | LA |
| 2041455.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 2041735.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 2051240.0 | 33.0 | BNA | MSY | New Orleans | LA |
| 2051455.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 2050850.0 | 19.0 | BNA | MSY | New Orleans | LA |
| 2051735.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2061240.0 | 43.0 | BNA | MSY | New Orleans | LA |
| 2060850.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2061735.0 | 34.0 | BNA | MSY | New Orleans | LA |
| 2071240.0 | 88.0 | BNA | MSY | New Orleans | LA |
| 2071455.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2070850.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2071735.0 | 20.0 | BNA | MSY | New Orleans | LA |
| 2091235.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2091455.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 2091735.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2101240.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 2101455.0 | 14.0 | BNA | MSY | New Orleans | LA |
| 2100850.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2111240.0 | 145.0 | BNA | MSY | New Orleans | LA |
| 2110850.0 | 96.0 | BNA | MSY | New Orleans | LA |
| 2111735.0 | 25.0 | BNA | MSY | New Orleans | LA |
| 2121240.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2121455.0 | 29.0 | BNA | MSY | New Orleans | LA |
| 2120850.0 | 3.0 | BNA | MSY | New Orleans | LA |
| 2121735.0 | 39.0 | BNA | MSY | New Orleans | LA |
| 2131350.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2131610.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2131800.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2141610.0 | 37.0 | BNA | MSY | New Orleans | LA |
| 2140810.0 | 13.0 | BNA | MSY | New Orleans | LA |
| 2141350.0 | 46.0 | BNA | MSY | New Orleans | LA |
| 2141800.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 2151530.0 | 35.0 | BNA | MSY | New Orleans | LA |
| 2150850.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 2161800.0 | 17.0 | BNA | MSY | New Orleans | LA |
| 2170810.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2171350.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2180810.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2181350.0 | 40.0 | BNA | MSY | New Orleans | LA |
| 2181800.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2191350.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 2191800.0 | 21.0 | BNA | MSY | New Orleans | LA |
| 2201610.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2200810.0 | 16.0 | BNA | MSY | New Orleans | LA |
| 2201350.0 | 5.0 | BNA | MSY | New Orleans | LA |
| 2201800.0 | 162.0 | BNA | MSY | New Orleans | LA |
| 2211610.0 | 46.0 | BNA | MSY | New Orleans | LA |
| 2210810.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2211350.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2211800.0 | 10.0 | BNA | MSY | New Orleans | LA |
| 2221530.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2231350.0 | 2.0 | BNA | MSY | New Orleans | LA |
| 2231800.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 2241350.0 | 12.0 | BNA | MSY | New Orleans | LA |
| 2250810.0 | 26.0 | BNA | MSY | New Orleans | LA |
| 2251800.0 | 103.0 | BNA | MSY | New Orleans | LA |
| 2261610.0 | 7.0 | BNA | MSY | New Orleans | LA |
| 2260810.0 | 1.0 | BNA | MSY | New Orleans | LA |
| 2261350.0 | 11.0 | BNA | MSY | New Orleans | LA |
| 2261800.0 | 6.0 | BNA | MSY | New Orleans | LA |
| 2271800.0 | 9.0 | BNA | MSY | New Orleans | LA |
| 2281610.0 | 23.0 | BNA | MSY | New Orleans | LA |
| 2281350.0 | 8.0 | BNA | MSY | New Orleans | LA |
| 2281800.0 | 4.0 | BNA | MSY | New Orleans | LA |
| 3011117.0 | 36.0 | CLT | MSY | New Orleans | LA |
| 3011635.0 | 77.0 | CLT | MSY | New Orleans | LA |
| 3021117.0 | 30.0 | CLT | MSY | New Orleans | LA |
| 3031825.0 | 9.0 | CLT | MSY | New Orleans | LA |
| 3061825.0 | 32.0 | CLT | MSY | New Orleans | LA |
| 3062010.0 | 33.0 | CLT | MSY | New Orleans | LA |
| 3070750.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 3071435.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 3081115.0 | 28.0 | CLT | MSY | New Orleans | LA |
| 3091115.0 | 28.0 | CLT | MSY | New Orleans | LA |
| 3090909.0 | 118.0 | CLT | MSY | New Orleans | LA |
| 3102010.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 3110750.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 3121115.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 3122010.0 | 31.0 | CLT | MSY | New Orleans | LA |
| 3121435.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 3141825.0 | 28.0 | CLT | MSY | New Orleans | LA |
| 3141115.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 3140915.0 | 17.0 | CLT | MSY | New Orleans | LA |
| 3161840.0 | 24.0 | CLT | MSY | New Orleans | LA |
| 3162010.0 | 34.0 | CLT | MSY | New Orleans | LA |
| 3161435.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3171825.0 | 23.0 | CLT | MSY | New Orleans | LA |
| 3171115.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3172010.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3171435.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 3170915.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 3180750.0 | 46.0 | CLT | MSY | New Orleans | LA |
| 3182010.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 3180915.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 3190915.0 | 65.0 | CLT | MSY | New Orleans | LA |
| 3202010.0 | 24.0 | CLT | MSY | New Orleans | LA |
| 3201435.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 3210750.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3230909.0 | 14.0 | CLT | MSY | New Orleans | LA |
| 3241115.0 | 21.0 | CLT | MSY | New Orleans | LA |
| 3240915.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 3251435.0 | 56.0 | CLT | MSY | New Orleans | LA |
| 3250915.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 3261115.0 | 9.0 | CLT | MSY | New Orleans | LA |
| 3261435.0 | 96.0 | CLT | MSY | New Orleans | LA |
| 3260915.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 3271115.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 3270915.0 | 23.0 | CLT | MSY | New Orleans | LA |
| 3281115.0 | 37.0 | CLT | MSY | New Orleans | LA |
| 3282010.0 | 46.0 | CLT | MSY | New Orleans | LA |
| 3291825.0 | 150.0 | CLT | MSY | New Orleans | LA |
| 3291115.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 3292010.0 | 53.0 | CLT | MSY | New Orleans | LA |
| 3291635.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 3290915.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 3311115.0 | 18.0 | CLT | MSY | New Orleans | LA |
| 3311435.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1011815.0 | 11.0 | CLT | MSY | New Orleans | LA |
| 1011435.0 | 40.0 | CLT | MSY | New Orleans | LA |
| 1021815.0 | 22.0 | CLT | MSY | New Orleans | LA |
| 1022015.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1021435.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1042020.0 | 53.0 | CLT | MSY | New Orleans | LA |
| 1041435.0 | 15.0 | CLT | MSY | New Orleans | LA |
| 1051815.0 | 45.0 | CLT | MSY | New Orleans | LA |
| 1052015.0 | 19.0 | CLT | MSY | New Orleans | LA |
| 1051435.0 | 60.0 | CLT | MSY | New Orleans | LA |
| 1060750.0 | 7.0 | CLT | MSY | New Orleans | LA |
| 1061120.0 | 15.0 | CLT | MSY | New Orleans | LA |
| 1071825.0 | 58.0 | CLT | MSY | New Orleans | LA |
| 1071435.0 | 11.0 | CLT | MSY | New Orleans | LA |
| 1081435.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 1080915.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1091117.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 1100750.0 | 21.0 | CLT | MSY | New Orleans | LA |
| 1101825.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 1101117.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 1101635.0 | 55.0 | CLT | MSY | New Orleans | LA |
| 1101435.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 1110750.0 | 115.0 | CLT | MSY | New Orleans | LA |
| 1111117.0 | 49.0 | CLT | MSY | New Orleans | LA |
| 1111635.0 | 145.0 | CLT | MSY | New Orleans | LA |
| 1112010.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1111435.0 | 126.0 | CLT | MSY | New Orleans | LA |
| 1110915.0 | 72.0 | CLT | MSY | New Orleans | LA |
| 1121117.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 1121435.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1131435.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 1151117.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 1151435.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1171117.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 1181117.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 1180915.0 | 14.0 | CLT | MSY | New Orleans | LA |
| 1191117.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1200750.0 | 51.0 | CLT | MSY | New Orleans | LA |
| 1211825.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1211435.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 1221117.0 | 10.0 | CLT | MSY | New Orleans | LA |
| 1221435.0 | 25.0 | CLT | MSY | New Orleans | LA |
| 1230750.0 | 23.0 | CLT | MSY | New Orleans | LA |
| 1231117.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 1231635.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 1230915.0 | 131.0 | CLT | MSY | New Orleans | LA |
| 1241117.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 1252010.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1250915.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 1271825.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 1271117.0 | 52.0 | CLT | MSY | New Orleans | LA |
| 1290750.0 | 26.0 | CLT | MSY | New Orleans | LA |
| 1291117.0 | 19.0 | CLT | MSY | New Orleans | LA |
| 1291635.0 | 21.0 | CLT | MSY | New Orleans | LA |
| 1291435.0 | 6.0 | CLT | MSY | New Orleans | LA |
| 1290915.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 1311825.0 | 50.0 | CLT | MSY | New Orleans | LA |
| 1310915.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 2011117.0 | 30.0 | CLT | MSY | New Orleans | LA |
| 2011635.0 | 23.0 | CLT | MSY | New Orleans | LA |
| 2010900.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2031117.0 | 8.0 | CLT | MSY | New Orleans | LA |
| 2031435.0 | 6.0 | CLT | MSY | New Orleans | LA |
| 2041117.0 | 22.0 | CLT | MSY | New Orleans | LA |
| 2051117.0 | 36.0 | CLT | MSY | New Orleans | LA |
| 2051435.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 2050915.0 | 7.0 | CLT | MSY | New Orleans | LA |
| 2060750.0 | 34.0 | CLT | MSY | New Orleans | LA |
| 2061117.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 2061635.0 | 14.0 | CLT | MSY | New Orleans | LA |
| 2071825.0 | 11.0 | CLT | MSY | New Orleans | LA |
| 2090915.0 | 79.0 | CLT | MSY | New Orleans | LA |
| 2101435.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 2121635.0 | 42.0 | CLT | MSY | New Orleans | LA |
| 2121435.0 | 1.0 | CLT | MSY | New Orleans | LA |
| 2140750.0 | 151.0 | CLT | MSY | New Orleans | LA |
| 2141825.0 | 33.0 | CLT | MSY | New Orleans | LA |
| 2142010.0 | 28.0 | CLT | MSY | New Orleans | LA |
| 2141435.0 | 13.0 | CLT | MSY | New Orleans | LA |
| 2140915.0 | 174.0 | CLT | MSY | New Orleans | LA |
| 2150915.0 | 4.0 | CLT | MSY | New Orleans | LA |
| 2161117.0 | 27.0 | CLT | MSY | New Orleans | LA |
| 2161435.0 | 12.0 | CLT | MSY | New Orleans | LA |
| 2171117.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2171435.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2170915.0 | 30.0 | CLT | MSY | New Orleans | LA |
| 2200915.0 | 6.0 | CLT | MSY | New Orleans | LA |
| 2211117.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2211435.0 | 24.0 | CLT | MSY | New Orleans | LA |
| 2210915.0 | 52.0 | CLT | MSY | New Orleans | LA |
| 2232010.0 | 74.0 | CLT | MSY | New Orleans | LA |
| 2251435.0 | 5.0 | CLT | MSY | New Orleans | LA |
| 2261825.0 | 53.0 | CLT | MSY | New Orleans | LA |
| 2272010.0 | 10.0 | CLT | MSY | New Orleans | LA |
| 2271435.0 | 16.0 | CLT | MSY | New Orleans | LA |
| 2280750.0 | 2.0 | CLT | MSY | New Orleans | LA |
| 2281117.0 | 3.0 | CLT | MSY | New Orleans | LA |
| 3011715.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 3011830.0 | 124.0 | DAL | MSY | New Orleans | LA |
| 3021710.0 | 174.0 | DAL | MSY | New Orleans | LA |
| 3021335.0 | 30.0 | DAL | MSY | New Orleans | LA |
| 3022025.0 | 84.0 | DAL | MSY | New Orleans | LA |
| 3021855.0 | 55.0 | DAL | MSY | New Orleans | LA |
| 3030605.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 3031335.0 | 22.0 | DAL | MSY | New Orleans | LA |
| 3032025.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3030920.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3031710.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 3031100.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3031855.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3041335.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3042025.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3052025.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 3051710.0 | 45.0 | DAL | MSY | New Orleans | LA |
| 3051855.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 3061335.0 | 24.0 | DAL | MSY | New Orleans | LA |
| 3062025.0 | 52.0 | DAL | MSY | New Orleans | LA |
| 3060920.0 | 30.0 | DAL | MSY | New Orleans | LA |
| 3061710.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 3061100.0 | 22.0 | DAL | MSY | New Orleans | LA |
| 3070805.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 3071335.0 | 60.0 | DAL | MSY | New Orleans | LA |
| 3072025.0 | 26.0 | DAL | MSY | New Orleans | LA |
| 3071710.0 | 12.0 | DAL | MSY | New Orleans | LA |
| 3071100.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3071855.0 | 66.0 | DAL | MSY | New Orleans | LA |
| 3081440.0 | 107.0 | DAL | MSY | New Orleans | LA |
| 3081745.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3091900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3092055.0 | 35.0 | DAL | MSY | New Orleans | LA |
| 3091810.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3091455.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3101900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3102055.0 | 21.0 | DAL | MSY | New Orleans | LA |
| 3101810.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3111900.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 3112055.0 | 103.0 | DAL | MSY | New Orleans | LA |
| 3111205.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3110825.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3111810.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3111455.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3121900.0 | 50.0 | DAL | MSY | New Orleans | LA |
| 3122055.0 | 57.0 | DAL | MSY | New Orleans | LA |
| 3121205.0 | 30.0 | DAL | MSY | New Orleans | LA |
| 3120825.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3121810.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 3131900.0 | 70.0 | DAL | MSY | New Orleans | LA |
| 3130930.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3132055.0 | 77.0 | DAL | MSY | New Orleans | LA |
| 3130600.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3130825.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3131810.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3141900.0 | 28.0 | DAL | MSY | New Orleans | LA |
| 3142055.0 | 25.0 | DAL | MSY | New Orleans | LA |
| 3140600.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 3141205.0 | 54.0 | DAL | MSY | New Orleans | LA |
| 3141810.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3141455.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3150830.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3151440.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3161900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3162055.0 | 22.0 | DAL | MSY | New Orleans | LA |
| 3161810.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3160930.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3171900.0 | 83.0 | DAL | MSY | New Orleans | LA |
| 3170930.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3172055.0 | 81.0 | DAL | MSY | New Orleans | LA |
| 3170825.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3171810.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 3181900.0 | 46.0 | DAL | MSY | New Orleans | LA |
| 3182055.0 | 148.0 | DAL | MSY | New Orleans | LA |
| 3180600.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 3181205.0 | 32.0 | DAL | MSY | New Orleans | LA |
| 3180825.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3181810.0 | 92.0 | DAL | MSY | New Orleans | LA |
| 3181455.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 3191900.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 3192055.0 | 71.0 | DAL | MSY | New Orleans | LA |
| 3191205.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3191810.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 3191455.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 3201900.0 | 26.0 | DAL | MSY | New Orleans | LA |
| 3202055.0 | 34.0 | DAL | MSY | New Orleans | LA |
| 3200600.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 3201205.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3200825.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3201810.0 | 31.0 | DAL | MSY | New Orleans | LA |
| 3211900.0 | 52.0 | DAL | MSY | New Orleans | LA |
| 3212055.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 3210600.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3211205.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 3210825.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3211810.0 | 14.0 | DAL | MSY | New Orleans | LA |
| 3211455.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 3221750.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3221440.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 3231900.0 | 36.0 | DAL | MSY | New Orleans | LA |
| 3231810.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 3231455.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 3231210.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 3231045.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3230930.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 3241900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3242055.0 | 36.0 | DAL | MSY | New Orleans | LA |
| 3241205.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 3240825.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3241810.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 3241455.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 3251900.0 | 58.0 | DAL | MSY | New Orleans | LA |
| 3252055.0 | 51.0 | DAL | MSY | New Orleans | LA |
| 3251205.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3250825.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 3251810.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 3251455.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3261900.0 | 15.0 | DAL | MSY | New Orleans | LA |
| 3262055.0 | 69.0 | DAL | MSY | New Orleans | LA |
| 3261205.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 3260825.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3261810.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 3261455.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3271900.0 | 36.0 | DAL | MSY | New Orleans | LA |
| 3272055.0 | 78.0 | DAL | MSY | New Orleans | LA |
| 3271205.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 3270825.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 3271810.0 | 45.0 | DAL | MSY | New Orleans | LA |
| 3271455.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 3281900.0 | 46.0 | DAL | MSY | New Orleans | LA |
| 3280930.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 3282055.0 | 65.0 | DAL | MSY | New Orleans | LA |
| 3281205.0 | 13.0 | DAL | MSY | New Orleans | LA |
| 3280825.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 3281810.0 | 49.0 | DAL | MSY | New Orleans | LA |
| 3281455.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3290830.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 3291440.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 3301900.0 | 126.0 | DAL | MSY | New Orleans | LA |
| 3301810.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3301455.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3301045.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 3300930.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 3311900.0 | 47.0 | DAL | MSY | New Orleans | LA |
| 3310930.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 3312055.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 3311205.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 3310825.0 | 21.0 | DAL | MSY | New Orleans | LA |
| 3311810.0 | 18.0 | DAL | MSY | New Orleans | LA |
| 3311455.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1012115.0 | 24.0 | DAL | MSY | New Orleans | LA |
| 1011235.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 1011650.0 | 70.0 | DAL | MSY | New Orleans | LA |
| 1011525.0 | 21.0 | DAL | MSY | New Orleans | LA |
| 1011120.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1021120.0 | 40.0 | DAL | MSY | New Orleans | LA |
| 1021650.0 | 37.0 | DAL | MSY | New Orleans | LA |
| 1020925.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1022110.0 | 115.0 | DAL | MSY | New Orleans | LA |
| 1021235.0 | 66.0 | DAL | MSY | New Orleans | LA |
| 1020605.0 | 31.0 | DAL | MSY | New Orleans | LA |
| 1021525.0 | 91.0 | DAL | MSY | New Orleans | LA |
| 1021910.0 | 12.0 | DAL | MSY | New Orleans | LA |
| 1031120.0 | 121.0 | DAL | MSY | New Orleans | LA |
| 1031650.0 | 154.0 | DAL | MSY | New Orleans | LA |
| 1030925.0 | 46.0 | DAL | MSY | New Orleans | LA |
| 1032110.0 | 120.0 | DAL | MSY | New Orleans | LA |
| 1031235.0 | 43.0 | DAL | MSY | New Orleans | LA |
| 1031525.0 | 45.0 | DAL | MSY | New Orleans | LA |
| 1031910.0 | 194.0 | DAL | MSY | New Orleans | LA |
| 1040710.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 1041730.0 | 108.0 | DAL | MSY | New Orleans | LA |
| 1041420.0 | 49.0 | DAL | MSY | New Orleans | LA |
| 1040930.0 | 45.0 | DAL | MSY | New Orleans | LA |
| 1041540.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1052110.0 | 110.0 | DAL | MSY | New Orleans | LA |
| 1051235.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1051525.0 | 24.0 | DAL | MSY | New Orleans | LA |
| 1051150.0 | 32.0 | DAL | MSY | New Orleans | LA |
| 1051910.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 1050925.0 | 11.0 | DAL | MSY | New Orleans | LA |
| 1051650.0 | 101.0 | DAL | MSY | New Orleans | LA |
| 1061120.0 | 48.0 | DAL | MSY | New Orleans | LA |
| 1061650.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 1062110.0 | 103.0 | DAL | MSY | New Orleans | LA |
| 1061235.0 | 71.0 | DAL | MSY | New Orleans | LA |
| 1061525.0 | 18.0 | DAL | MSY | New Orleans | LA |
| 1072015.0 | 46.0 | DAL | MSY | New Orleans | LA |
| 1071535.0 | 53.0 | DAL | MSY | New Orleans | LA |
| 1070630.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1071930.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1070925.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 1071025.0 | 22.0 | DAL | MSY | New Orleans | LA |
| 1071230.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1071635.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1082015.0 | 28.0 | DAL | MSY | New Orleans | LA |
| 1081535.0 | 31.0 | DAL | MSY | New Orleans | LA |
| 1081930.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 1080925.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 1081025.0 | 24.0 | DAL | MSY | New Orleans | LA |
| 1081230.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 1081635.0 | 29.0 | DAL | MSY | New Orleans | LA |
| 1092015.0 | 89.0 | DAL | MSY | New Orleans | LA |
| 1091535.0 | 20.0 | DAL | MSY | New Orleans | LA |
| 1090925.0 | 39.0 | DAL | MSY | New Orleans | LA |
| 1091025.0 | 94.0 | DAL | MSY | New Orleans | LA |
| 1091230.0 | 73.0 | DAL | MSY | New Orleans | LA |
| 1091635.0 | 35.0 | DAL | MSY | New Orleans | LA |
| 1102015.0 | 100.0 | DAL | MSY | New Orleans | LA |
| 1101535.0 | 70.0 | DAL | MSY | New Orleans | LA |
| 1101930.0 | 118.0 | DAL | MSY | New Orleans | LA |
| 1100925.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 1101025.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 1101230.0 | 21.0 | DAL | MSY | New Orleans | LA |
| 1101635.0 | 57.0 | DAL | MSY | New Orleans | LA |
| 1111750.0 | 36.0 | DAL | MSY | New Orleans | LA |
| 1110645.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1111450.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1111625.0 | 50.0 | DAL | MSY | New Orleans | LA |
| 1122015.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1121535.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 1121620.0 | 33.0 | DAL | MSY | New Orleans | LA |
| 1121930.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 1120925.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1121230.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 1131535.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1130630.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1130925.0 | 9.0 | DAL | MSY | New Orleans | LA |
| 1131025.0 | 18.0 | DAL | MSY | New Orleans | LA |
| 1142015.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 1140630.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1140925.0 | 12.0 | DAL | MSY | New Orleans | LA |
| 1141025.0 | 16.0 | DAL | MSY | New Orleans | LA |
| 1141230.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1141635.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1151930.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1150925.0 | 8.0 | DAL | MSY | New Orleans | LA |
| 1151025.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 1162015.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 1161535.0 | 26.0 | DAL | MSY | New Orleans | LA |
| 1161930.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1160925.0 | 10.0 | DAL | MSY | New Orleans | LA |
| 1161025.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 1161230.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 1161635.0 | 28.0 | DAL | MSY | New Orleans | LA |
| 1172015.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 1171535.0 | 2.0 | DAL | MSY | New Orleans | LA |
| 1171025.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1171230.0 | 17.0 | DAL | MSY | New Orleans | LA |
| 1171635.0 | 30.0 | DAL | MSY | New Orleans | LA |
| 1180645.0 | 19.0 | DAL | MSY | New Orleans | LA |
| 1181450.0 | 3.0 | DAL | MSY | New Orleans | LA |
| 1181625.0 | 7.0 | DAL | MSY | New Orleans | LA |
| 1191535.0 | 23.0 | DAL | MSY | New Orleans | LA |
| 1191620.0 | 4.0 | DAL | MSY | New Orleans | LA |
| 1191930.0 | 219.0 | DAL | MSY | New Orleans | LA |
| 1190925.0 | 5.0 | DAL | MSY | New Orleans | LA |
| 1200925.0 | 12.0 | DAL | MSY | New Orleans | LA |
| 1201025.0 | 33.0 | DAL | MSY | New Orleans | LA |
| 1201230.0 | 1.0 | DAL | MSY | New Orleans | LA |
| 1201635.0 | 6.0 | DAL | MSY | New Orleans | LA |
| 1211535.0 | 95.0 | DAL | MSY | New Orleans | LA |
// States with the longest cumulative delays (with individual delays > 100 minutes) (origin: Seattle)
display(tripGraph.edges.filter($"src" === "SEA" && $"delay" > 100))
| tripid | delay | src | dst | city_dst | state_dst |
|---|---|---|---|---|---|
| 3201938.0 | 108.0 | SEA | BUR | Burbank | CA |
| 3201655.0 | 107.0 | SEA | SNA | Orange County | CA |
| 1011950.0 | 123.0 | SEA | OAK | Oakland | CA |
| 1021950.0 | 194.0 | SEA | OAK | Oakland | CA |
| 1021615.0 | 317.0 | SEA | OAK | Oakland | CA |
| 1021755.0 | 385.0 | SEA | OAK | Oakland | CA |
| 1031950.0 | 283.0 | SEA | OAK | Oakland | CA |
| 1031615.0 | 364.0 | SEA | OAK | Oakland | CA |
| 1031325.0 | 130.0 | SEA | OAK | Oakland | CA |
| 1061755.0 | 107.0 | SEA | OAK | Oakland | CA |
| 1081330.0 | 118.0 | SEA | OAK | Oakland | CA |
| 2282055.0 | 150.0 | SEA | OAK | Oakland | CA |
| 3061600.0 | 130.0 | SEA | OAK | Oakland | CA |
| 3170815.0 | 199.0 | SEA | DCA | Washington DC | null |
| 2151845.0 | 128.0 | SEA | KTN | Ketchikan | AK |
| 2281845.0 | 104.0 | SEA | KTN | Ketchikan | AK |
| 3130720.0 | 117.0 | SEA | KTN | Ketchikan | AK |
| 1011411.0 | 177.0 | SEA | IAH | Houston | TX |
| 1022347.0 | 158.0 | SEA | IAH | Houston | TX |
| 1021411.0 | 170.0 | SEA | IAH | Houston | TX |
| 1031201.0 | 116.0 | SEA | IAH | Houston | TX |
| 1031900.0 | 178.0 | SEA | IAH | Houston | TX |
| 1042347.0 | 114.0 | SEA | IAH | Houston | TX |
| 1062347.0 | 136.0 | SEA | IAH | Houston | TX |
| 1101203.0 | 173.0 | SEA | IAH | Houston | TX |
| 1172300.0 | 125.0 | SEA | IAH | Houston | TX |
| 1250605.0 | 227.0 | SEA | IAH | Houston | TX |
| 1291203.0 | 106.0 | SEA | IAH | Houston | TX |
| 2141157.0 | 127.0 | SEA | IAH | Houston | TX |
| 2150740.0 | 466.0 | SEA | IAH | Houston | TX |
| 2180750.0 | 105.0 | SEA | IAH | Houston | TX |
| 3010740.0 | 103.0 | SEA | IAH | Houston | TX |
| 3021152.0 | 124.0 | SEA | IAH | Houston | TX |
| 3121152.0 | 340.0 | SEA | IAH | Houston | TX |
| 3140600.0 | 139.0 | SEA | IAH | Houston | TX |
| 3171152.0 | 121.0 | SEA | IAH | Houston | TX |
| 3280600.0 | 103.0 | SEA | IAH | Houston | TX |
| 1151746.0 | 229.0 | SEA | HNL | Honolulu, Oahu | HI |
| 1271746.0 | 111.0 | SEA | HNL | Honolulu, Oahu | HI |
| 1030840.0 | 294.0 | SEA | HNL | Honolulu, Oahu | HI |
| 2091035.0 | 132.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3141035.0 | 295.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3141930.0 | 128.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3311930.0 | 104.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3030840.0 | 185.0 | SEA | HNL | Honolulu, Oahu | HI |
| 3240845.0 | 114.0 | SEA | HNL | Honolulu, Oahu | HI |
| 1041740.0 | 256.0 | SEA | SJC | San Jose | CA |
| 1061815.0 | 200.0 | SEA | SJC | San Jose | CA |
| 3031600.0 | 161.0 | SEA | SJC | San Jose | CA |
| 1031100.0 | 145.0 | SEA | LGB | Long Beach | CA |
| 1041737.0 | 320.0 | SEA | LGB | Long Beach | CA |
| 1081728.0 | 122.0 | SEA | LGB | Long Beach | CA |
| 1221140.0 | 189.0 | SEA | LGB | Long Beach | CA |
| 2121140.0 | 365.0 | SEA | LGB | Long Beach | CA |
| 3070710.0 | 115.0 | SEA | LGB | Long Beach | CA |
| 3180935.0 | 135.0 | SEA | LGB | Long Beach | CA |
| 2141245.0 | 176.0 | SEA | RNO | Reno | NV |
| 1052147.0 | 110.0 | SEA | BOS | Boston | MA |
| 1081420.0 | 109.0 | SEA | BOS | Boston | MA |
| 1112313.0 | 110.0 | SEA | BOS | Boston | MA |
| 1232313.0 | 110.0 | SEA | BOS | Boston | MA |
| 2140945.0 | 105.0 | SEA | BOS | Boston | MA |
| 2032313.0 | 114.0 | SEA | BOS | Boston | MA |
| 2121420.0 | 108.0 | SEA | BOS | Boston | MA |
| 2141415.0 | 152.0 | SEA | BOS | Boston | MA |
| 3282307.0 | 119.0 | SEA | BOS | Boston | MA |
| 1110905.0 | 130.0 | SEA | EWR | Newark | NJ |
| 1022227.0 | 236.0 | SEA | EWR | Newark | NJ |
| 1180703.0 | 138.0 | SEA | EWR | Newark | NJ |
| 2030905.0 | 212.0 | SEA | EWR | Newark | NJ |
| 3141530.0 | 131.0 | SEA | EWR | Newark | NJ |
| 3171530.0 | 181.0 | SEA | EWR | Newark | NJ |
| 3202200.0 | 168.0 | SEA | EWR | Newark | NJ |
| 1032115.0 | 196.0 | SEA | LAS | Las Vegas | NV |
| 1201840.0 | 113.0 | SEA | LAS | Las Vegas | NV |
| 1260840.0 | 165.0 | SEA | LAS | Las Vegas | NV |
| 1261840.0 | 121.0 | SEA | LAS | Las Vegas | NV |
| 1031905.0 | 103.0 | SEA | LAS | Las Vegas | NV |
| 1041550.0 | 156.0 | SEA | LAS | Las Vegas | NV |
| 1061820.0 | 213.0 | SEA | LAS | Las Vegas | NV |
| 1061515.0 | 116.0 | SEA | LAS | Las Vegas | NV |
| 1080805.0 | 235.0 | SEA | LAS | Las Vegas | NV |
| 1170800.0 | 247.0 | SEA | LAS | Las Vegas | NV |
| 2191235.0 | 210.0 | SEA | LAS | Las Vegas | NV |
| 2281430.0 | 121.0 | SEA | LAS | Las Vegas | NV |
| 2281235.0 | 187.0 | SEA | LAS | Las Vegas | NV |
| 2170830.0 | 610.0 | SEA | LAS | Las Vegas | NV |
| 2051205.0 | 110.0 | SEA | LAS | Las Vegas | NV |
| 2111835.0 | 133.0 | SEA | LAS | Las Vegas | NV |
| 2132005.0 | 135.0 | SEA | LAS | Las Vegas | NV |
| 2272005.0 | 102.0 | SEA | LAS | Las Vegas | NV |
| 3031430.0 | 108.0 | SEA | LAS | Las Vegas | NV |
| 3141410.0 | 102.0 | SEA | LAS | Las Vegas | NV |
| 3151205.0 | 103.0 | SEA | LAS | Las Vegas | NV |
| 3281245.0 | 140.0 | SEA | LAS | Las Vegas | NV |
| 3181520.0 | 103.0 | SEA | LAS | Las Vegas | NV |
| 3292000.0 | 107.0 | SEA | LAS | Las Vegas | NV |
| 1051955.0 | 160.0 | SEA | FAI | Fairbanks | AK |
| 1040955.0 | 126.0 | SEA | DEN | Denver | CO |
| 1040650.0 | 103.0 | SEA | DEN | Denver | CO |
| 1160650.0 | 120.0 | SEA | DEN | Denver | CO |
| 1011940.0 | 167.0 | SEA | DEN | Denver | CO |
| 1041039.0 | 113.0 | SEA | DEN | Denver | CO |
| 1041437.0 | 256.0 | SEA | DEN | Denver | CO |
| 1041937.0 | 191.0 | SEA | DEN | Denver | CO |
| 1040605.0 | 138.0 | SEA | DEN | Denver | CO |
| 1061937.0 | 111.0 | SEA | DEN | Denver | CO |
| 1121937.0 | 118.0 | SEA | DEN | Denver | CO |
| 1171937.0 | 104.0 | SEA | DEN | Denver | CO |
| 1021523.0 | 118.0 | SEA | DEN | Denver | CO |
| 1041521.0 | 177.0 | SEA | DEN | Denver | CO |
| 1061055.0 | 154.0 | SEA | DEN | Denver | CO |
| 1281515.0 | 137.0 | SEA | DEN | Denver | CO |
| 1011450.0 | 102.0 | SEA | DEN | Denver | CO |
| 1021205.0 | 425.0 | SEA | DEN | Denver | CO |
| 1031450.0 | 211.0 | SEA | DEN | Denver | CO |
| 2210955.0 | 132.0 | SEA | DEN | Denver | CO |
| 2111537.0 | 156.0 | SEA | DEN | Denver | CO |
| 2110700.0 | 625.0 | SEA | DEN | Denver | CO |
| 2191039.0 | 148.0 | SEA | DEN | Denver | CO |
| 2211039.0 | 105.0 | SEA | DEN | Denver | CO |
| 2031055.0 | 174.0 | SEA | DEN | Denver | CO |
| 2051055.0 | 112.0 | SEA | DEN | Denver | CO |
| 2211110.0 | 106.0 | SEA | DEN | Denver | CO |
| 3131405.0 | 154.0 | SEA | DEN | Denver | CO |
| 3181930.0 | 115.0 | SEA | DEN | Denver | CO |
| 3191749.0 | 103.0 | SEA | DEN | Denver | CO |
| 3011405.0 | 109.0 | SEA | DEN | Denver | CO |
| 3121519.0 | 231.0 | SEA | DEN | Denver | CO |
| 3061445.0 | 148.0 | SEA | DEN | Denver | CO |
| 3181440.0 | 132.0 | SEA | DEN | Denver | CO |
| 1011306.0 | 206.0 | SEA | IAD | Washington DC | null |
| 1050806.0 | 105.0 | SEA | IAD | Washington DC | null |
| 1052226.0 | 111.0 | SEA | IAD | Washington DC | null |
| 1291256.0 | 108.0 | SEA | IAD | Washington DC | null |
| 2031256.0 | 185.0 | SEA | IAD | Washington DC | null |
| 2041256.0 | 129.0 | SEA | IAD | Washington DC | null |
| 2101256.0 | 144.0 | SEA | IAD | Washington DC | null |
| 2191316.0 | 122.0 | SEA | IAD | Washington DC | null |
| 3080800.0 | 273.0 | SEA | IAD | Washington DC | null |
| 3162225.0 | 174.0 | SEA | IAD | Washington DC | null |
| 3201316.0 | 111.0 | SEA | IAD | Washington DC | null |
| 3261311.0 | 104.0 | SEA | IAD | Washington DC | null |
| 1111300.0 | 133.0 | SEA | PSP | Palm Springs | CA |
| 1110910.0 | 171.0 | SEA | PSP | Palm Springs | CA |
| 3170915.0 | 114.0 | SEA | PSP | Palm Springs | CA |
| 3310645.0 | 179.0 | SEA | PSP | Palm Springs | CA |
| 1161050.0 | 132.0 | SEA | SBA | Santa Barbara | CA |
| 2091050.0 | 310.0 | SEA | SBA | Santa Barbara | CA |
| 3191045.0 | 109.0 | SEA | SBA | Santa Barbara | CA |
| 3241045.0 | 140.0 | SEA | SBA | Santa Barbara | CA |
| 2122225.0 | 228.0 | SEA | CLT | Charlotte | NC |
| 2142225.0 | 104.0 | SEA | CLT | Charlotte | NC |
| 2152225.0 | 113.0 | SEA | CLT | Charlotte | NC |
| 3081110.0 | 110.0 | SEA | CLT | Charlotte | NC |
| 1290940.0 | 316.0 | SEA | ABQ | Albuquerque | NM |
| 1171153.0 | 119.0 | SEA | PDX | Portland | OR |
| 1171439.0 | 112.0 | SEA | PDX | Portland | OR |
| 2091041.0 | 309.0 | SEA | PDX | Portland | OR |
| 2090845.0 | 133.0 | SEA | PDX | Portland | OR |
| 3021455.0 | 226.0 | SEA | PDX | Portland | OR |
| 3021914.0 | 165.0 | SEA | PDX | Portland | OR |
| 3090926.0 | 112.0 | SEA | PDX | Portland | OR |
| 3251041.0 | 274.0 | SEA | PDX | Portland | OR |
| 3261728.0 | 109.0 | SEA | PDX | Portland | OR |
| 3301728.0 | 108.0 | SEA | PDX | Portland | OR |
| 1112205.0 | 101.0 | SEA | MIA | Miami | FL |
| 1262205.0 | 130.0 | SEA | MIA | Miami | FL |
| 2142215.0 | 229.0 | SEA | MIA | Miami | FL |
| 3292220.0 | 117.0 | SEA | MIA | Miami | FL |
| 1012140.0 | 136.0 | SEA | SMF | Sacramento | CA |
| 1010715.0 | 164.0 | SEA | SMF | Sacramento | CA |
| 1021930.0 | 109.0 | SEA | SMF | Sacramento | CA |
| 1031630.0 | 113.0 | SEA | SMF | Sacramento | CA |
| 1041440.0 | 137.0 | SEA | SMF | Sacramento | CA |
| 1041710.0 | 102.0 | SEA | SMF | Sacramento | CA |
| 2211915.0 | 103.0 | SEA | SMF | Sacramento | CA |
| 3262140.0 | 113.0 | SEA | SMF | Sacramento | CA |
| 3271855.0 | 113.0 | SEA | SMF | Sacramento | CA |
| 3191020.0 | 139.0 | SEA | SMF | Sacramento | CA |
| 1291410.0 | 136.0 | SEA | PHX | Phoenix | AZ |
| 1040830.0 | 294.0 | SEA | PHX | Phoenix | AZ |
| 1300830.0 | 371.0 | SEA | PHX | Phoenix | AZ |
| 1031510.0 | 119.0 | SEA | PHX | Phoenix | AZ |
| 1061510.0 | 180.0 | SEA | PHX | Phoenix | AZ |
| 1290720.0 | 147.0 | SEA | PHX | Phoenix | AZ |
| 2082020.0 | 130.0 | SEA | PHX | Phoenix | AZ |
| 2281855.0 | 186.0 | SEA | PHX | Phoenix | AZ |
| 2110520.0 | 463.0 | SEA | PHX | Phoenix | AZ |
| 2111132.0 | 107.0 | SEA | PHX | Phoenix | AZ |
| 2160830.0 | 110.0 | SEA | PHX | Phoenix | AZ |
| 2241132.0 | 107.0 | SEA | PHX | Phoenix | AZ |
| 2251615.0 | 103.0 | SEA | PHX | Phoenix | AZ |
| 3041455.0 | 108.0 | SEA | PHX | Phoenix | AZ |
| 3051745.0 | 169.0 | SEA | PHX | Phoenix | AZ |
| 3091455.0 | 126.0 | SEA | PHX | Phoenix | AZ |
| 3251132.0 | 123.0 | SEA | PHX | Phoenix | AZ |
| 3221530.0 | 121.0 | SEA | PHX | Phoenix | AZ |
| 1060830.0 | 116.0 | SEA | DFW | Dallas | TX |
| 1180830.0 | 132.0 | SEA | DFW | Dallas | TX |
| 1281350.0 | 115.0 | SEA | DFW | Dallas | TX |
| 1291545.0 | 247.0 | SEA | DFW | Dallas | TX |
| 2031545.0 | 230.0 | SEA | DFW | Dallas | TX |
| 2040830.0 | 135.0 | SEA | DFW | Dallas | TX |
| 2061115.0 | 110.0 | SEA | DFW | Dallas | TX |
| 2061545.0 | 125.0 | SEA | DFW | Dallas | TX |
| 2061350.0 | 126.0 | SEA | DFW | Dallas | TX |
| 2080830.0 | 356.0 | SEA | DFW | Dallas | TX |
| 2090830.0 | 128.0 | SEA | DFW | Dallas | TX |
| 3012320.0 | 123.0 | SEA | DFW | Dallas | TX |
| 3152320.0 | 143.0 | SEA | DFW | Dallas | TX |
| 3151400.0 | 146.0 | SEA | DFW | Dallas | TX |
| 3301400.0 | 277.0 | SEA | DFW | Dallas | TX |
| 1171220.0 | 110.0 | SEA | SFO | San Francisco | CA |
| 1291220.0 | 103.0 | SEA | SFO | San Francisco | CA |
| 1031125.0 | 107.0 | SEA | SFO | San Francisco | CA |
| 1120924.0 | 268.0 | SEA | SFO | San Francisco | CA |
| 1161058.0 | 131.0 | SEA | SFO | San Francisco | CA |
| 1171058.0 | 138.0 | SEA | SFO | San Francisco | CA |
| 1010955.0 | 104.0 | SEA | SFO | San Francisco | CA |
| 1021914.0 | 105.0 | SEA | SFO | San Francisco | CA |
| 1041830.0 | 131.0 | SEA | SFO | San Francisco | CA |
| 1051214.0 | 139.0 | SEA | SFO | San Francisco | CA |
| 1061214.0 | 384.0 | SEA | SFO | San Francisco | CA |
| 1111310.0 | 124.0 | SEA | SFO | San Francisco | CA |
| 1241310.0 | 133.0 | SEA | SFO | San Francisco | CA |
| 1281310.0 | 250.0 | SEA | SFO | San Francisco | CA |
| 1051855.0 | 166.0 | SEA | SFO | San Francisco | CA |
| 2060955.0 | 148.0 | SEA | SFO | San Francisco | CA |
| 2072125.0 | 105.0 | SEA | SFO | San Francisco | CA |
| 2071845.0 | 203.0 | SEA | SFO | San Francisco | CA |
| 2071220.0 | 139.0 | SEA | SFO | San Francisco | CA |
| 2071100.0 | 113.0 | SEA | SFO | San Francisco | CA |
| 2071405.0 | 144.0 | SEA | SFO | San Francisco | CA |
| 2080955.0 | 121.0 | SEA | SFO | San Francisco | CA |
| 2091835.0 | 124.0 | SEA | SFO | San Francisco | CA |
| 2091405.0 | 101.0 | SEA | SFO | San Francisco | CA |
| 2101220.0 | 134.0 | SEA | SFO | San Francisco | CA |
| 2100955.0 | 107.0 | SEA | SFO | San Francisco | CA |
| 2101100.0 | 105.0 | SEA | SFO | San Francisco | CA |
| 2101405.0 | 105.0 | SEA | SFO | San Francisco | CA |
| 2130955.0 | 108.0 | SEA | SFO | San Francisco | CA |
| 2131100.0 | 139.0 | SEA | SFO | San Francisco | CA |
| 2141100.0 | 107.0 | SEA | SFO | San Francisco | CA |
| 2281100.0 | 130.0 | SEA | SFO | San Francisco | CA |
| 2231058.0 | 136.0 | SEA | SFO | San Francisco | CA |
| 2021310.0 | 115.0 | SEA | SFO | San Francisco | CA |
| 2091820.0 | 149.0 | SEA | SFO | San Francisco | CA |
| 2181526.0 | 106.0 | SEA | SFO | San Francisco | CA |
| 2271905.0 | 154.0 | SEA | SFO | San Francisco | CA |
| 2021450.0 | 139.0 | SEA | SFO | San Francisco | CA |
| 2060950.0 | 150.0 | SEA | SFO | San Francisco | CA |
| 2061450.0 | 140.0 | SEA | SFO | San Francisco | CA |
| 2061855.0 | 119.0 | SEA | SFO | San Francisco | CA |
| 2071855.0 | 146.0 | SEA | SFO | San Francisco | CA |
| 2081330.0 | 164.0 | SEA | SFO | San Francisco | CA |
| 2091450.0 | 106.0 | SEA | SFO | San Francisco | CA |
| 2091855.0 | 182.0 | SEA | SFO | San Francisco | CA |
| 2261450.0 | 259.0 | SEA | SFO | San Francisco | CA |
| 2261855.0 | 224.0 | SEA | SFO | San Francisco | CA |
| 2270950.0 | 174.0 | SEA | SFO | San Francisco | CA |
| 2271450.0 | 240.0 | SEA | SFO | San Francisco | CA |
| 2280950.0 | 207.0 | SEA | SFO | San Francisco | CA |
| 2281450.0 | 331.0 | SEA | SFO | San Francisco | CA |
| 2281855.0 | 124.0 | SEA | SFO | San Francisco | CA |
| 3142043.0 | 101.0 | SEA | SFO | San Francisco | CA |
| 3180950.0 | 155.0 | SEA | SFO | San Francisco | CA |
| 3260950.0 | 117.0 | SEA | SFO | San Francisco | CA |
| 3281430.0 | 112.0 | SEA | SFO | San Francisco | CA |
| 3291050.0 | 138.0 | SEA | SFO | San Francisco | CA |
| 3062055.0 | 113.0 | SEA | SFO | San Francisco | CA |
| 3091028.0 | 143.0 | SEA | SFO | San Francisco | CA |
| 3112055.0 | 116.0 | SEA | SFO | San Francisco | CA |
| 3261043.0 | 121.0 | SEA | SFO | San Francisco | CA |
| 3311043.0 | 257.0 | SEA | SFO | San Francisco | CA |
| 3171237.0 | 182.0 | SEA | SFO | San Francisco | CA |
| 3251933.0 | 197.0 | SEA | SFO | San Francisco | CA |
| 3011330.0 | 115.0 | SEA | SFO | San Francisco | CA |
| 3311045.0 | 189.0 | SEA | SFO | San Francisco | CA |
| 3311440.0 | 101.0 | SEA | SFO | San Francisco | CA |
| 1031152.0 | 397.0 | SEA | ATL | Atlanta | GA |
| 1050830.0 | 106.0 | SEA | ATL | Atlanta | GA |
| 1051152.0 | 107.0 | SEA | ATL | Atlanta | GA |
| 1121159.0 | 201.0 | SEA | ATL | Atlanta | GA |
| 1250630.0 | 206.0 | SEA | ATL | Atlanta | GA |
| 1301159.0 | 117.0 | SEA | ATL | Atlanta | GA |
| 1301322.0 | 179.0 | SEA | ATL | Atlanta | GA |
| 2021159.0 | 145.0 | SEA | ATL | Atlanta | GA |
| 2171320.0 | 133.0 | SEA | ATL | Atlanta | GA |
| 3171320.0 | 109.0 | SEA | ATL | Atlanta | GA |
| 1092015.0 | 119.0 | SEA | FAT | Fresno | CA |
| 2012015.0 | 189.0 | SEA | FAT | Fresno | CA |
| 2091150.0 | 232.0 | SEA | FAT | Fresno | CA |
| 1021425.0 | 298.0 | SEA | ORD | Chicago | IL |
| 1030600.0 | 103.0 | SEA | ORD | Chicago | IL |
| 1081205.0 | 135.0 | SEA | ORD | Chicago | IL |
| 1161205.0 | 582.0 | SEA | ORD | Chicago | IL |
| 1241205.0 | 174.0 | SEA | ORD | Chicago | IL |
| 1300815.0 | 209.0 | SEA | ORD | Chicago | IL |
| 1021235.0 | 113.0 | SEA | ORD | Chicago | IL |
| 1020830.0 | 151.0 | SEA | ORD | Chicago | IL |
| 1051235.0 | 240.0 | SEA | ORD | Chicago | IL |
| 1050830.0 | 163.0 | SEA | ORD | Chicago | IL |
| 1241235.0 | 171.0 | SEA | ORD | Chicago | IL |
| 1301235.0 | 111.0 | SEA | ORD | Chicago | IL |
| 1300830.0 | 141.0 | SEA | ORD | Chicago | IL |
| 1012359.0 | 227.0 | SEA | ORD | Chicago | IL |
| 1040859.0 | 302.0 | SEA | ORD | Chicago | IL |
| 1241110.0 | 223.0 | SEA | ORD | Chicago | IL |
| 1311411.0 | 142.0 | SEA | ORD | Chicago | IL |
| 2051235.0 | 115.0 | SEA | ORD | Chicago | IL |
| 2050830.0 | 128.0 | SEA | ORD | Chicago | IL |
| 2171235.0 | 204.0 | SEA | ORD | Chicago | IL |
| 2170830.0 | 220.0 | SEA | ORD | Chicago | IL |
| 2031615.0 | 185.0 | SEA | ORD | Chicago | IL |
| 2171415.0 | 164.0 | SEA | ORD | Chicago | IL |
| 2261415.0 | 138.0 | SEA | ORD | Chicago | IL |
| 3120820.0 | 179.0 | SEA | ORD | Chicago | IL |
| 3121200.0 | 151.0 | SEA | ORD | Chicago | IL |
| 3200600.0 | 140.0 | SEA | ORD | Chicago | IL |
| 3051235.0 | 140.0 | SEA | ORD | Chicago | IL |
| 3120830.0 | 204.0 | SEA | ORD | Chicago | IL |
| 3132240.0 | 127.0 | SEA | ORD | Chicago | IL |
| 3162240.0 | 150.0 | SEA | ORD | Chicago | IL |
| 3181417.0 | 127.0 | SEA | ORD | Chicago | IL |
| 1091350.0 | 133.0 | SEA | MDW | Chicago | IL |
| 1261350.0 | 109.0 | SEA | MDW | Chicago | IL |
| 3171420.0 | 111.0 | SEA | MDW | Chicago | IL |
| 3310600.0 | 206.0 | SEA | MDW | Chicago | IL |
| 2271820.0 | 203.0 | SEA | COS | Colorado Springs | CO |
| 3171825.0 | 205.0 | SEA | COS | Colorado Springs | CO |
| 1250755.0 | 233.0 | SEA | JNU | Juneau | AK |
| 1310755.0 | 210.0 | SEA | JNU | Juneau | AK |
| 1311120.0 | 105.0 | SEA | JNU | Juneau | AK |
| 3030755.0 | 110.0 | SEA | JNU | Juneau | AK |
| 3130750.0 | 320.0 | SEA | JNU | Juneau | AK |
| 1062320.0 | 107.0 | SEA | DTW | Detroit | MI |
| 3121405.0 | 135.0 | SEA | ONT | Ontario | CA |
| 3130725.0 | 174.0 | SEA | ONT | Ontario | CA |
| 1171425.0 | 105.0 | SEA | LAX | Los Angeles | CA |
| 1041700.0 | 264.0 | SEA | LAX | Los Angeles | CA |
| 1051700.0 | 136.0 | SEA | LAX | Los Angeles | CA |
| 1071645.0 | 151.0 | SEA | LAX | Los Angeles | CA |
| 1311645.0 | 136.0 | SEA | LAX | Los Angeles | CA |
| 1251020.0 | 130.0 | SEA | LAX | Los Angeles | CA |
| 1261020.0 | 108.0 | SEA | LAX | Los Angeles | CA |
| 1291258.0 | 126.0 | SEA | LAX | Los Angeles | CA |
| 2022140.0 | 131.0 | SEA | LAX | Los Angeles | CA |
| 2130610.0 | 135.0 | SEA | LAX | Los Angeles | CA |
| 2091645.0 | 104.0 | SEA | LAX | Los Angeles | CA |
| 2131645.0 | 134.0 | SEA | LAX | Los Angeles | CA |
| 2031805.0 | 212.0 | SEA | LAX | Los Angeles | CA |
| 2071805.0 | 105.0 | SEA | LAX | Los Angeles | CA |
| 2071649.0 | 126.0 | SEA | LAX | Los Angeles | CA |
| 2081649.0 | 154.0 | SEA | LAX | Los Angeles | CA |
| 2091649.0 | 245.0 | SEA | LAX | Los Angeles | CA |
| 2131705.0 | 107.0 | SEA | LAX | Los Angeles | CA |
| 2161010.0 | 124.0 | SEA | LAX | Los Angeles | CA |
| 2271149.0 | 192.0 | SEA | LAX | Los Angeles | CA |
| 3141650.0 | 197.0 | SEA | LAX | Los Angeles | CA |
| 3152105.0 | 115.0 | SEA | LAX | Los Angeles | CA |
| 3171700.0 | 131.0 | SEA | LAX | Los Angeles | CA |
| 3291700.0 | 118.0 | SEA | LAX | Los Angeles | CA |
| 1050037.0 | 102.0 | SEA | MSP | Minneapolis | MN |
| 1070700.0 | 193.0 | SEA | MSP | Minneapolis | MN |
| 1130700.0 | 429.0 | SEA | MSP | Minneapolis | MN |
| 2240655.0 | 109.0 | SEA | MSP | Minneapolis | MN |
| 2121515.0 | 118.0 | SEA | MSP | Minneapolis | MN |
| 1060800.0 | 132.0 | SEA | MCO | Orlando | FL |
| 2220955.0 | 157.0 | SEA | SAN | San Diego | CA |
| 3010955.0 | 108.0 | SEA | SAN | San Diego | CA |
| 1031300.0 | 108.0 | SEA | ANC | Anchorage | AK |
| 1062110.0 | 149.0 | SEA | ANC | Anchorage | AK |
| 1132112.0 | 106.0 | SEA | ANC | Anchorage | AK |
| 2072350.0 | 141.0 | SEA | ANC | Anchorage | AK |
| 2091815.0 | 133.0 | SEA | ANC | Anchorage | AK |
| 3181605.0 | 187.0 | SEA | ANC | Anchorage | AK |
| 3231915.0 | 115.0 | SEA | ANC | Anchorage | AK |
| 3311605.0 | 135.0 | SEA | ANC | Anchorage | AK |
| 1022258.0 | 180.0 | SEA | JFK | New York | NY |
| 1042258.0 | 285.0 | SEA | JFK | New York | NY |
| 1022259.0 | 116.0 | SEA | JFK | New York | NY |
| 2090715.0 | 110.0 | SEA | JFK | New York | NY |
| 2022145.0 | 101.0 | SEA | JFK | New York | NY |
| 2032145.0 | 165.0 | SEA | JFK | New York | NY |
| 2031535.0 | 181.0 | SEA | JFK | New York | NY |
| 2042145.0 | 201.0 | SEA | JFK | New York | NY |
| 2142140.0 | 113.0 | SEA | JFK | New York | NY |
| 2222140.0 | 118.0 | SEA | JFK | New York | NY |
| 2032258.0 | 130.0 | SEA | JFK | New York | NY |
| 2220700.0 | 404.0 | SEA | JFK | New York | NY |
| 3310715.0 | 385.0 | SEA | JFK | New York | NY |
| 3192140.0 | 119.0 | SEA | JFK | New York | NY |
| 3091300.0 | 125.0 | SEA | JFK | New York | NY |
| 1241000.0 | 806.0 | SEA | OGG | Kahului, Maui | HI |
| 1030845.0 | 342.0 | SEA | PHL | Philadelphia | PA |
| 1050845.0 | 174.0 | SEA | PHL | Philadelphia | PA |
| 1210845.0 | 866.0 | SEA | PHL | Philadelphia | PA |
| 1022215.0 | 121.0 | SEA | PHL | Philadelphia | PA |
| 1031130.0 | 146.0 | SEA | PHL | Philadelphia | PA |
| 1090835.0 | 148.0 | SEA | PHL | Philadelphia | PA |
| 1170835.0 | 203.0 | SEA | PHL | Philadelphia | PA |
| 2030845.0 | 202.0 | SEA | PHL | Philadelphia | PA |
| 3130835.0 | 290.0 | SEA | PHL | Philadelphia | PA |
| 1031810.0 | 142.0 | SEA | SLC | Salt Lake City | UT |
| 1041016.0 | 117.0 | SEA | SLC | Salt Lake City | UT |
| 1291725.0 | 111.0 | SEA | SLC | Salt Lake City | UT |
| 1021555.0 | 175.0 | SEA | SLC | Salt Lake City | UT |
| 1041825.0 | 110.0 | SEA | SLC | Salt Lake City | UT |
| 2131635.0 | 134.0 | SEA | SLC | Salt Lake City | UT |
| 2230710.0 | 739.0 | SEA | SLC | Salt Lake City | UT |
| 2240710.0 | 477.0 | SEA | SLC | Salt Lake City | UT |
| 2061005.0 | 119.0 | SEA | SLC | Salt Lake City | UT |
| 3051315.0 | 123.0 | SEA | SLC | Salt Lake City | UT |
| 3260710.0 | 149.0 | SEA | SLC | Salt Lake City | UT |
| 3281750.0 | 125.0 | SEA | SLC | Salt Lake City | UT |
Vertex Degrees
inDegrees: Incoming connections to the airportoutDegrees: Outgoing connections from the airportdegrees: Total connections to and from the airport
Reviewing the various properties of the property graph to understand the incoming and outgoing connections between airports.
// Degrees
// The number of degrees - the number of incoming and outgoing connections - for various airports within this sample dataset
display(tripGraph.degrees.sort($"degree".desc).limit(20))
| id | degree |
|---|---|
| ATL | 179774.0 |
| DFW | 133966.0 |
| ORD | 125405.0 |
| LAX | 106853.0 |
| DEN | 103699.0 |
| IAH | 85685.0 |
| PHX | 79672.0 |
| SFO | 77635.0 |
| LAS | 66101.0 |
| CLT | 56103.0 |
| EWR | 54407.0 |
| MCO | 54300.0 |
| LGA | 50927.0 |
| SLC | 50780.0 |
| BOS | 49936.0 |
| DTW | 46705.0 |
| MSP | 46235.0 |
| SEA | 45816.0 |
| JFK | 43661.0 |
| BWI | 42526.0 |
City / Flight Relationships through Motif Finding
To more easily understand the complex relationship of city airports and their flights with each other, we can use motifs to find patterns of airports (i.e. vertices) connected by flights (i.e. edges). The result is a DataFrame in which the column names are given by the motif keys.
/*
Using tripGraphPrime to more easily display
- The associated edge (ab, bc) relationships
- With the different the city / airports (a, b, c) where SFO is the connecting city (b)
- Ensuring that flight ab (i.e. the flight to SFO) occured before flight bc (i.e. flight leaving SFO)
- Note, TripID was generated based on time in the format of MMDDHHMM converted to int
- Therefore bc.tripid < ab.tripid + 10000 means the second flight (bc) occured within approx a day of the first flight (ab)
Note: In reality, we would need to be more careful to link trips ab and bc.
*/
val motifs = tripGraphPrime.
find("(a)-[ab]->(b); (b)-[bc]->(c)").
filter("(b.id = 'SFO') and (ab.delay > 500 or bc.delay > 500) and bc.tripid > ab.tripid and bc.tripid < ab.tripid + 10000")
display(motifs)
Determining Airport Ranking using PageRank
There are a large number of flights and connections through these various airports included in this Departure Delay Dataset. Using the pageRank algorithm, Spark iteratively traverses the graph and determines a rough estimate of how important the airport is.
// Determining Airport ranking of importance using `pageRank`
val ranks = tripGraph.pageRank.resetProbability(0.15).maxIter(5).run()
ranks: org.graphframes.GraphFrame = GraphFrame(v:[id: string, City: string ... 3 more fields], e:[src: string, dst: string ... 5 more fields])
display(ranks.vertices.orderBy($"pagerank".desc).limit(20))
| id | City | State | Country | pagerank |
|---|---|---|---|---|
| ATL | Atlanta | GA | USA | 18.910104616729814 |
| DFW | Dallas | TX | USA | 13.699227467378964 |
| ORD | Chicago | IL | USA | 13.163049993795985 |
| DEN | Denver | CO | USA | 9.723388283811563 |
| LAX | Los Angeles | CA | USA | 8.703656827807166 |
| IAH | Houston | TX | USA | 7.991324463091128 |
| SFO | San Francisco | CA | USA | 6.903242998287933 |
| PHX | Phoenix | AZ | USA | 6.505886984498643 |
| SLC | Salt Lake City | UT | USA | 5.799587684561128 |
| LAS | Las Vegas | NV | USA | 5.25359244560915 |
| SEA | Seattle | WA | USA | 4.626877547905697 |
| EWR | Newark | NJ | USA | 4.401221169028188 |
| MCO | Orlando | FL | USA | 4.389045874474043 |
| CLT | Charlotte | NC | USA | 4.378459524081744 |
| DTW | Detroit | MI | USA | 4.223377976049847 |
| MSP | Minneapolis | MN | USA | 4.1490048912541795 |
| LGA | New York | NY | USA | 4.129454491295321 |
| BOS | Boston | MA | USA | 3.812077076528526 |
| BWI | Baltimore | MD | USA | 3.53116352570383 |
| JFK | New York | NY | USA | 3.521942669296845 |
BTW, A lot more delicate air-traffic arithmetic is possible for a full month of airplane co-trajectories over the radar range of Atlanta, Georgia, one of the busiest airports in the world.
See for instance:
- Statistical regular pavings to analyze massive data of aircraft trajectories, Gloria Teng, Kenneth Kuhn and Raazesh Sainudiin, Journal of Aerospace Computing, Information, and Communication, Vol. 9, No. 1, pp. 14-25, doi: 10.2514/1.I010015, 2012. See free preprint: http://lamastex.org/preprints/AAIASubPavingATC.pdf.
Most popular flights (single city hops)
Using the tripGraph, we can quickly determine what are the most popular single city hop flights
// Determine the most popular flights (single city hops)
import org.apache.spark.sql.functions._
val topTrips = tripGraph.edges.
groupBy("src", "dst").
agg(count("delay").as("trips"))
import org.apache.spark.sql.functions._
topTrips: org.apache.spark.sql.DataFrame = [src: string, dst: string ... 1 more field]
// Show the top 20 most popular flights (single city hops)
display(topTrips.orderBy($"trips".desc).limit(20))
| src | dst | trips |
|---|---|---|
| SFO | LAX | 3232.0 |
| LAX | SFO | 3198.0 |
| LAS | LAX | 3016.0 |
| LAX | LAS | 2964.0 |
| JFK | LAX | 2720.0 |
| LAX | JFK | 2719.0 |
| ATL | LGA | 2501.0 |
| LGA | ATL | 2500.0 |
| LAX | PHX | 2394.0 |
| PHX | LAX | 2387.0 |
| HNL | OGG | 2380.0 |
| OGG | HNL | 2379.0 |
| LAX | SAN | 2215.0 |
| SAN | LAX | 2214.0 |
| SJC | LAX | 2208.0 |
| LAX | SJC | 2201.0 |
| ATL | MCO | 2136.0 |
| MCO | ATL | 2090.0 |
| JFK | SFO | 2084.0 |
| SFO | JFK | 2084.0 |
Top Transfer Cities
Many airports are used as transfer points instead of the final Destination. An easy way to calculate this is by calculating the ratio of inDegree (the number of flights to the airport) / outDegree (the number of flights leaving the airport). Values close to 1 may indicate many transfers, whereas values < 1 indicate many outgoing flights and > 1 indicate many incoming flights. Note, this is a simple calculation that does not take into account of timing or scheduling of flights, just the overall aggregate number within the dataset.
// Calculate the inDeg (flights into the airport) and outDeg (flights leaving the airport)
val inDeg = tripGraph.inDegrees
val outDeg = tripGraph.outDegrees
// Calculate the degreeRatio (inDeg/outDeg), perform inner join on "id" column
val degreeRatio = inDeg.join(outDeg, inDeg("id") === outDeg("id")).
drop(outDeg("id")).
selectExpr("id", "double(inDegree)/double(outDegree) as degreeRatio").
cache()
// Join back to the `airports` DataFrame (instead of registering temp table as above)
val nonTransferAirports = degreeRatio.as("d").join(airports.as("a"), $"d.id" === $"a.IATA").
selectExpr("id", "city", "degreeRatio").
filter("degreeRatio < 0.9 or degreeRatio > 1.1")
// List out the city airports which have abnormal degree ratios
display(nonTransferAirports)
| id | city | degreeRatio |
|---|---|---|
| GFK | Grand Forks | 1.3333333333333333 |
| FAI | Fairbanks | 1.1232686980609419 |
| OME | Nome | 0.5084745762711864 |
| BRW | Barrow | 0.28651685393258425 |
// Join back to the `airports` DataFrame (instead of registering temp table as above)
val transferAirports = degreeRatio.as("d").join(airports.as("a"), $"d.id" === $"a.IATA"). //degreeRatio.join(airports, degreeRatio("id") === airports("IATA")).
selectExpr("id", "city", "degreeRatio").
filter("degreeRatio between 0.9 and 1.1")
// List out the top 10 transfer city airports
display(transferAirports.orderBy("degreeRatio").limit(10))
| id | city | degreeRatio |
|---|---|---|
| MSP | Minneapolis | 0.9375183338222353 |
| DEN | Denver | 0.958025717037065 |
| DFW | Dallas | 0.964339653074092 |
| ORD | Chicago | 0.9671063983310065 |
| SLC | Salt Lake City | 0.9827417906368358 |
| IAH | Houston | 0.9846895050147083 |
| PHX | Phoenix | 0.9891643572266746 |
| OGG | Kahului, Maui | 0.9898718478710211 |
| HNL | Honolulu, Oahu | 0.990535889872173 |
| SFO | San Francisco | 0.9909473252295224 |
Breadth First Search
Breadth-first search (BFS) is designed to traverse the graph to quickly find the desired vertices (i.e. airports) and edges (i.e flights). Let's try to find the shortest number of connections between cities based on the dataset. Note, these examples do not take into account of time or distance, just hops between cities.
// Example 1: Direct Seattle to San Francisco
// This method returns a DataFrame of valid shortest paths from vertices matching "fromExpr" to vertices matching "toExpr"
val filteredPaths = tripGraph.bfs.fromExpr((col("id") === "SEA")).toExpr(col("id") === "SFO").maxPathLength(1).run()
display(filteredPaths)
As you can see, there are a number of direct flights between Seattle and San Francisco.
// Example 2: Direct San Francisco and Buffalo
// You can also specify expression as a String, instead of Column
val filteredPaths = tripGraph.bfs.fromExpr("id = 'SFO'").toExpr("id = 'BUF'").maxPathLength(1).run()
filteredPaths: org.apache.spark.sql.DataFrame = [id: string, City: string ... 2 more fields]
filteredPaths.show()
+---+----+-----+-------+
| id|City|State|Country|
+---+----+-----+-------+
+---+----+-----+-------+
display(filteredPaths) // display instead of show - same diference
But there are no direct flights between San Francisco and Buffalo.
// Example 2a: Flying from San Francisco to Buffalo
val filteredPaths = tripGraph.bfs.fromExpr("id = 'SFO'").toExpr("id = 'BUF'").maxPathLength(2).run()
display(filteredPaths)
But there are flights from San Francisco to Buffalo with Minneapolis as the transfer point.
Loading the D3 Visualization
Using the airports D3 visualization to visualize airports and flight paths
Warning: classes defined within packages cannot be redefined without a cluster restart.
Compilation successful.
d3a1.graphs.help()
Produces a force-directed graph given a collection of edges of the following form: case class Edge(src: String, dest: String, count: Long)
Usage:
%scala
import d3._
graphs.force(
height = 500,
width = 500,
clicks: Dataset[Edge])
// On-time and Early Arrivals
import d3a1._
graphs.force(
height = 800,
width = 1200,
clicks = sql("select src, dst as dest, count(1) as count from departureDelays_geo where delay <= 0 group by src, dst").as[Edge])
You try!
NOW or Later as HOMEWORK
-
Try to do the same process for the State of the Union Addresses dataset from Week1. As a first step, first locate where that data is... Go to week1 and try to see if each SoU can be treated as a document for topic modeling and whether there is temporal clustering of SoU's within the same topic.
-
Try to improve the tuning by elaborating the pipeline with stemming, lemmatization, etc in this news-group dataset (if you want to do a project based on this, perhaps). You can also parse the input to bring in the newsgroup id's from the directories (consider exploiting the file names in the
wholeTextFilesmethod) as this will let you explore how well your unsupervised algorithm is doing relative to the known newsgroups each document falls in (note you generally won't have the luxury of knowing the topic labels for typical datasets in the unsupervised topic modeling domain). -
Try to parse the data closer to the clean dataset available in
/databricks-datasets/news20.binary/*and walk through the following notebook (but in Scala!):
ls /databricks-datasets/news20.binary/data-001
| path | name | size |
|---|---|---|
| dbfs:/databricks-datasets/news20.binary/data-001/test/ | test/ | 0.0 |
| dbfs:/databricks-datasets/news20.binary/data-001/training/ | training/ | 0.0 |
Step 1. Downloading and Loading Data into DBFS
you don't have to do the download in databricks if above cell has contents in /databricks-datasets/news20.binary/data-001
Here are the steps taken for downloading and saving data to the distributed file system. Uncomment them for repeating this process on your databricks cluster or for downloading a new source of data.
wget http://kdd.ics.uci.edu/databases/20newsgroups/mini_newsgroups.tar.gz -O /tmp/newsgroups.tar.gz
--2020-11-18 16:58:10-- http://kdd.ics.uci.edu/databases/20newsgroups/mini_newsgroups.tar.gz
Resolving kdd.ics.uci.edu (kdd.ics.uci.edu)... 128.195.1.86
Connecting to kdd.ics.uci.edu (kdd.ics.uci.edu)|128.195.1.86|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1860687 (1.8M) [application/x-gzip]
Saving to: ‘/tmp/newsgroups.tar.gz’
0K .......... .......... .......... .......... .......... 2% 765K 2s
50K .......... .......... .......... .......... .......... 5% 1.68M 2s
100K .......... .......... .......... .......... .......... 8% 165M 1s
150K .......... .......... .......... .......... .......... 11% 325M 1s
200K .......... .......... .......... .......... .......... 13% 1.79M 1s
250K .......... .......... .......... .......... .......... 16% 103M 1s
300K .......... .......... .......... .......... .......... 19% 116M 1s
350K .......... .......... .......... .......... .......... 22% 103M 0s
400K .......... .......... .......... .......... .......... 24% 1.88M 0s
450K .......... .......... .......... .......... .......... 27% 103M 0s
500K .......... .......... .......... .......... .......... 30% 90.5M 0s
550K .......... .......... .......... .......... .......... 33% 133M 0s
600K .......... .......... .......... .......... .......... 35% 96.6M 0s
650K .......... .......... .......... .......... .......... 38% 93.3M 0s
700K .......... .......... .......... .......... .......... 41% 93.7M 0s
750K .......... .......... .......... .......... .......... 44% 99.0M 0s
800K .......... .......... .......... .......... .......... 46% 2.07M 0s
850K .......... .......... .......... .......... .......... 49% 135M 0s
900K .......... .......... .......... .......... .......... 52% 104M 0s
950K .......... .......... .......... .......... .......... 55% 96.3M 0s
1000K .......... .......... .......... .......... .......... 57% 128M 0s
1050K .......... .......... .......... .......... .......... 60% 91.8M 0s
1100K .......... .......... .......... .......... .......... 63% 35.4M 0s
1150K .......... .......... .......... .......... .......... 66% 22.0M 0s
1200K .......... .......... .......... .......... .......... 68% 28.4M 0s
1250K .......... .......... .......... .......... .......... 71% 97.4M 0s
1300K .......... .......... .......... .......... .......... 74% 226M 0s
1350K .......... .......... .......... .......... .......... 77% 314M 0s
1400K .......... .......... .......... .......... .......... 79% 363M 0s
1450K .......... .......... .......... .......... .......... 82% 375M 0s
1500K .......... .......... .......... .......... .......... 85% 281M 0s
1550K .......... .......... .......... .......... .......... 88% 364M 0s
1600K .......... .......... .......... .......... .......... 90% 2.65M 0s
1650K .......... .......... .......... .......... .......... 93% 34.2M 0s
1700K .......... .......... .......... .......... .......... 96% 240M 0s
1750K .......... .......... .......... .......... .......... 99% 310M 0s
1800K .......... ....... 100% 211M=0.2s
2020-11-18 16:58:11 (8.63 MB/s) - ‘/tmp/newsgroups.tar.gz’ saved [1860687/1860687]
Untar the file into the /tmp/ folder.
tar xvfz /tmp/newsgroups.tar.gz -C /tmp/
mini_newsgroups/alt.atheism/
mini_newsgroups/alt.atheism/51127
mini_newsgroups/alt.atheism/51310
mini_newsgroups/alt.atheism/53539
mini_newsgroups/alt.atheism/53336
mini_newsgroups/alt.atheism/53212
mini_newsgroups/alt.atheism/51199
mini_newsgroups/alt.atheism/54144
mini_newsgroups/alt.atheism/54170
mini_newsgroups/alt.atheism/51126
mini_newsgroups/alt.atheism/51313
mini_newsgroups/alt.atheism/51166
mini_newsgroups/alt.atheism/53760
mini_newsgroups/alt.atheism/53211
mini_newsgroups/alt.atheism/54251
mini_newsgroups/alt.atheism/53188
mini_newsgroups/alt.atheism/54237
mini_newsgroups/alt.atheism/51227
mini_newsgroups/alt.atheism/51146
mini_newsgroups/alt.atheism/53542
mini_newsgroups/alt.atheism/53291
mini_newsgroups/alt.atheism/53150
mini_newsgroups/alt.atheism/53427
mini_newsgroups/alt.atheism/53061
mini_newsgroups/alt.atheism/53564
mini_newsgroups/alt.atheism/53574
mini_newsgroups/alt.atheism/53351
mini_newsgroups/alt.atheism/53334
mini_newsgroups/alt.atheism/53610
mini_newsgroups/alt.atheism/51195
mini_newsgroups/alt.atheism/53753
mini_newsgroups/alt.atheism/53410
mini_newsgroups/alt.atheism/53303
mini_newsgroups/alt.atheism/53565
mini_newsgroups/alt.atheism/51170
mini_newsgroups/alt.atheism/51305
mini_newsgroups/alt.atheism/54137
mini_newsgroups/alt.atheism/53312
mini_newsgroups/alt.atheism/53575
mini_newsgroups/alt.atheism/53458
mini_newsgroups/alt.atheism/53249
mini_newsgroups/alt.atheism/53299
mini_newsgroups/alt.atheism/53393
mini_newsgroups/alt.atheism/54485
mini_newsgroups/alt.atheism/54254
mini_newsgroups/alt.atheism/54171
mini_newsgroups/alt.atheism/51281
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mini_newsgroups/talk.politics.misc/178862
mini_newsgroups/talk.politics.misc/178661
mini_newsgroups/talk.politics.misc/178433
mini_newsgroups/talk.politics.misc/178327
mini_newsgroups/talk.politics.misc/176884
mini_newsgroups/talk.politics.misc/178865
mini_newsgroups/talk.politics.misc/178656
mini_newsgroups/talk.politics.misc/178631
mini_newsgroups/talk.politics.misc/176878
mini_newsgroups/talk.politics.misc/178775
mini_newsgroups/talk.politics.misc/176986
mini_newsgroups/talk.politics.misc/178564
mini_newsgroups/talk.politics.misc/176869
mini_newsgroups/talk.politics.misc/178390
mini_newsgroups/talk.politics.misc/176984
mini_newsgroups/talk.politics.misc/178745
mini_newsgroups/talk.politics.misc/178994
mini_newsgroups/talk.politics.misc/176895
mini_newsgroups/talk.politics.misc/178789
mini_newsgroups/talk.politics.misc/178678
mini_newsgroups/talk.politics.misc/178887
mini_newsgroups/talk.politics.misc/178945
mini_newsgroups/talk.politics.misc/178997
mini_newsgroups/talk.politics.misc/178788
mini_newsgroups/talk.politics.misc/178532
mini_newsgroups/talk.politics.misc/176926
mini_newsgroups/talk.politics.misc/178718
mini_newsgroups/talk.politics.misc/178824
mini_newsgroups/talk.politics.misc/178907
mini_newsgroups/talk.politics.misc/178569
mini_newsgroups/talk.politics.misc/178960
mini_newsgroups/talk.politics.misc/178765
mini_newsgroups/talk.politics.misc/178870
mini_newsgroups/talk.politics.misc/178489
mini_newsgroups/talk.politics.misc/178738
mini_newsgroups/talk.politics.misc/176904
mini_newsgroups/talk.politics.misc/178566
mini_newsgroups/talk.politics.misc/176930
mini_newsgroups/talk.politics.misc/176983
mini_newsgroups/talk.politics.misc/178682
mini_newsgroups/talk.politics.misc/178301
mini_newsgroups/talk.politics.misc/176956
mini_newsgroups/talk.politics.misc/179066
mini_newsgroups/talk.politics.misc/178368
mini_newsgroups/talk.politics.misc/178382
mini_newsgroups/talk.politics.misc/178906
mini_newsgroups/talk.politics.misc/179070
mini_newsgroups/talk.politics.misc/178481
mini_newsgroups/talk.politics.misc/178851
mini_newsgroups/talk.politics.misc/178799
mini_newsgroups/talk.politics.misc/178924
mini_newsgroups/talk.politics.misc/178721
mini_newsgroups/talk.politics.misc/178451
mini_newsgroups/talk.politics.misc/178792
mini_newsgroups/talk.politics.misc/176881
mini_newsgroups/talk.politics.misc/178654
mini_newsgroups/talk.politics.misc/176951
mini_newsgroups/talk.politics.misc/178801
mini_newsgroups/talk.politics.misc/179018
mini_newsgroups/talk.politics.misc/176886
mini_newsgroups/talk.politics.misc/178360
mini_newsgroups/talk.politics.misc/178699
mini_newsgroups/talk.politics.misc/176988
mini_newsgroups/talk.politics.misc/179097
mini_newsgroups/talk.politics.misc/179067
mini_newsgroups/talk.politics.misc/178318
mini_newsgroups/talk.politics.misc/178447
mini_newsgroups/talk.politics.misc/178455
mini_newsgroups/talk.politics.misc/177008
mini_newsgroups/talk.politics.misc/178527
mini_newsgroups/talk.politics.misc/178337
mini_newsgroups/talk.politics.misc/178556
mini_newsgroups/talk.politics.misc/178522
mini_newsgroups/talk.politics.misc/178517
mini_newsgroups/talk.politics.misc/178793
mini_newsgroups/talk.politics.misc/178579
mini_newsgroups/talk.politics.misc/178837
mini_newsgroups/talk.politics.misc/178731
mini_newsgroups/talk.politics.misc/178668
mini_newsgroups/talk.politics.misc/178939
mini_newsgroups/talk.politics.misc/178560
mini_newsgroups/talk.politics.misc/178571
mini_newsgroups/talk.politics.misc/178487
mini_newsgroups/talk.politics.misc/178965
mini_newsgroups/talk.politics.misc/178998
mini_newsgroups/talk.politics.misc/176982
mini_newsgroups/talk.politics.misc/178606
mini_newsgroups/talk.politics.misc/178341
mini_newsgroups/talk.politics.misc/178622
mini_newsgroups/talk.politics.misc/178309
mini_newsgroups/talk.politics.misc/178769
mini_newsgroups/talk.politics.misc/178927
mini_newsgroups/talk.politics.misc/178751
mini_newsgroups/talk.politics.misc/178349
mini_newsgroups/talk.politics.misc/179095
mini_newsgroups/talk.politics.misc/178993
mini_newsgroups/talk.politics.misc/178361
mini_newsgroups/talk.politics.misc/178610
mini_newsgroups/talk.religion.misc/
mini_newsgroups/talk.religion.misc/83682
mini_newsgroups/talk.religion.misc/83790
mini_newsgroups/talk.religion.misc/83751
mini_newsgroups/talk.religion.misc/83618
mini_newsgroups/talk.religion.misc/83535
mini_newsgroups/talk.religion.misc/84068
mini_newsgroups/talk.religion.misc/83732
mini_newsgroups/talk.religion.misc/83798
mini_newsgroups/talk.religion.misc/83564
mini_newsgroups/talk.religion.misc/84351
mini_newsgroups/talk.religion.misc/84212
mini_newsgroups/talk.religion.misc/83601
mini_newsgroups/talk.religion.misc/83775
mini_newsgroups/talk.religion.misc/83919
mini_newsgroups/talk.religion.misc/84355
mini_newsgroups/talk.religion.misc/82799
mini_newsgroups/talk.religion.misc/83518
mini_newsgroups/talk.religion.misc/84053
mini_newsgroups/talk.religion.misc/83866
mini_newsgroups/talk.religion.misc/83599
mini_newsgroups/talk.religion.misc/83788
mini_newsgroups/talk.religion.misc/84350
mini_newsgroups/talk.religion.misc/83641
mini_newsgroups/talk.religion.misc/84316
mini_newsgroups/talk.religion.misc/83771
mini_newsgroups/talk.religion.misc/83717
mini_newsgroups/talk.religion.misc/83645
mini_newsgroups/talk.religion.misc/83742
mini_newsgroups/talk.religion.misc/83979
mini_newsgroups/talk.religion.misc/84356
mini_newsgroups/talk.religion.misc/83567
mini_newsgroups/talk.religion.misc/84060
mini_newsgroups/talk.religion.misc/83843
mini_newsgroups/talk.religion.misc/84413
mini_newsgroups/talk.religion.misc/83474
mini_newsgroups/talk.religion.misc/84510
mini_newsgroups/talk.religion.misc/83748
mini_newsgroups/talk.religion.misc/83829
mini_newsgroups/talk.religion.misc/83548
mini_newsgroups/talk.religion.misc/84400
mini_newsgroups/talk.religion.misc/83827
mini_newsgroups/talk.religion.misc/84302
mini_newsgroups/talk.religion.misc/82796
mini_newsgroups/talk.religion.misc/84178
mini_newsgroups/talk.religion.misc/84398
mini_newsgroups/talk.religion.misc/84567
mini_newsgroups/talk.religion.misc/84324
mini_newsgroups/talk.religion.misc/82812
mini_newsgroups/talk.religion.misc/83817
mini_newsgroups/talk.religion.misc/83558
mini_newsgroups/talk.religion.misc/83683
mini_newsgroups/talk.religion.misc/84103
mini_newsgroups/talk.religion.misc/83791
mini_newsgroups/talk.religion.misc/84115
mini_newsgroups/talk.religion.misc/83797
mini_newsgroups/talk.religion.misc/83673
mini_newsgroups/talk.religion.misc/84255
mini_newsgroups/talk.religion.misc/84164
mini_newsgroups/talk.religion.misc/83977
mini_newsgroups/talk.religion.misc/84359
mini_newsgroups/talk.religion.misc/83773
mini_newsgroups/talk.religion.misc/84244
mini_newsgroups/talk.religion.misc/84194
mini_newsgroups/talk.religion.misc/84290
mini_newsgroups/talk.religion.misc/84317
mini_newsgroups/talk.religion.misc/84251
mini_newsgroups/talk.religion.misc/83898
mini_newsgroups/talk.religion.misc/83983
mini_newsgroups/talk.religion.misc/83807
mini_newsgroups/talk.religion.misc/84152
mini_newsgroups/talk.religion.misc/83480
mini_newsgroups/talk.religion.misc/82804
mini_newsgroups/talk.religion.misc/82758
mini_newsgroups/talk.religion.misc/83785
mini_newsgroups/talk.religion.misc/84309
mini_newsgroups/talk.religion.misc/84195
mini_newsgroups/talk.religion.misc/83678
mini_newsgroups/talk.religion.misc/84293
mini_newsgroups/talk.religion.misc/84345
mini_newsgroups/talk.religion.misc/83900
mini_newsgroups/talk.religion.misc/84120
mini_newsgroups/talk.religion.misc/83730
mini_newsgroups/talk.religion.misc/83575
mini_newsgroups/talk.religion.misc/83719
mini_newsgroups/talk.religion.misc/83574
mini_newsgroups/talk.religion.misc/84436
mini_newsgroups/talk.religion.misc/83861
mini_newsgroups/talk.religion.misc/83563
mini_newsgroups/talk.religion.misc/84278
mini_newsgroups/talk.religion.misc/83780
mini_newsgroups/talk.religion.misc/83862
mini_newsgroups/talk.religion.misc/83435
mini_newsgroups/talk.religion.misc/83763
mini_newsgroups/talk.religion.misc/83796
mini_newsgroups/talk.religion.misc/84083
mini_newsgroups/talk.religion.misc/83437
mini_newsgroups/talk.religion.misc/83750
mini_newsgroups/talk.religion.misc/83571
mini_newsgroups/talk.religion.misc/83981
mini_newsgroups/talk.religion.misc/82764
The below cell takes about 10mins to run.
NOTE: It is slow partly because each file is small and we are facing the 'small files problem' with distributed file systems that need meta-data for each file. If the file name is not needed then it may be better to create one large stream of the contents of all the files into dbfs. We leave this as it is to show what happens when we upload a dataset of lots of little files into dbfs.
cp -r file:/tmp/mini_newsgroups dbfs:/datasets/mini_newsgroups
res1: Boolean = true
display(dbutils.fs.ls("dbfs:/datasets/mini_newsgroups"))
| path | name | size |
|---|---|---|
| dbfs:/datasets/mini_newsgroups/alt.atheism/ | alt.atheism/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/comp.graphics/ | comp.graphics/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/comp.os.ms-windows.misc/ | comp.os.ms-windows.misc/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/comp.sys.ibm.pc.hardware/ | comp.sys.ibm.pc.hardware/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/comp.sys.mac.hardware/ | comp.sys.mac.hardware/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/comp.windows.x/ | comp.windows.x/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/misc.forsale/ | misc.forsale/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/rec.autos/ | rec.autos/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/rec.motorcycles/ | rec.motorcycles/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/rec.sport.baseball/ | rec.sport.baseball/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/rec.sport.hockey/ | rec.sport.hockey/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/sci.crypt/ | sci.crypt/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/sci.electronics/ | sci.electronics/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/sci.med/ | sci.med/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/sci.space/ | sci.space/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/soc.religion.christian/ | soc.religion.christian/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/talk.politics.guns/ | talk.politics.guns/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/talk.politics.mideast/ | talk.politics.mideast/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/talk.politics.misc/ | talk.politics.misc/ | 0.0 |
| dbfs:/datasets/mini_newsgroups/talk.religion.misc/ | talk.religion.misc/ | 0.0 |
Topic Modeling of Movie Dialogs with Latent Dirichlet Allocation
Let us cluster the conversations from different movies!
This notebook will provide a brief algorithm summary, links for further reading, and an example of how to use LDA for Topic Modeling.
not tested in Spark 2.2+ or 3.x yet (see 034 notebook for syntactic issues, if any)
Algorithm Summary
- Task: Identify topics from a collection of text documents
- Input: Vectors of word counts
- Optimizers:
- EMLDAOptimizer using Expectation Maximization
- OnlineLDAOptimizer using Iterative Mini-Batch Sampling for Online Variational Bayes
Links
Readings for LDA
- A high-level introduction to the topic from Communications of the ACM
- A very good high-level humanities introduction to the topic (recommended by Chris Thomson in English Department at UC, Ilam):
Also read the methodological and more formal papers cited in the above links if you want to know more.
Let's get a bird's eye view of LDA from http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf next.
- See pictures (hopefully you read the paper last night!)
- Algorithm of the generative model (this is unsupervised clustering)
- For a careful introduction to the topic see Section 27.3 and 27.4 (pages 950-970) pf Murphy's Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
- We will be quite application focussed or applied here!
Probabilistic Topic Modeling Example
This is an outline of our Topic Modeling workflow. Feel free to jump to any subtopic to find out more.
- Step 0. Dataset Review
- Step 1. Downloading and Loading Data into DBFS
- (Step 1. only needs to be done once per shard - see details at the end of the notebook for Step 1.)
- Step 2. Loading the Data and Data Cleaning
- Step 3. Text Tokenization
- Step 4. Remove Stopwords
- Step 5. Vector of Token Counts
- Step 6. Create LDA model with Online Variational Bayes
- Step 7. Review Topics
- Step 8. Model Tuning - Refilter Stopwords
- Step 9. Create LDA model with Expectation Maximization
- Step 10. Visualize Results
Step 0. Dataset Review
In this example, we will use the Cornell Movie Dialogs Corpus.
Here is the README.txt:
Cornell Movie-Dialogs Corpus
Distributed together with:
"Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs" Cristian Danescu-Niculescu-Mizil and Lillian Lee Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011.
(this paper is included in this zip file)
NOTE: If you have results to report on these corpora, please send email to cristian@cs.cornell.edu or llee@cs.cornell.edu so we can add you to our list of people using this data. Thanks!
Contents of this README:
A) Brief description
B) Files description
C) Details on the collection procedure
D) Contact
A) Brief description:
This corpus contains a metadata-rich collection of fictional conversations extracted from raw movie scripts:
- 220,579 conversational exchanges between 10,292 pairs of movie characters
- involves 9,035 characters from 617 movies
- in total 304,713 utterances
- movie metadata included: - genres - release year - IMDB rating - number of IMDB votes - IMDB rating
- character metadata included: - gender (for 3,774 characters) - position on movie credits (3,321 characters)
B) Files description:
In all files the field separator is " +++$+++ "
-
movietitlesmetadata.txt - contains information about each movie title - fields: - movieID, - movie title, - movie year, - IMDB rating, - no. IMDB votes, - genres in the format ['genre1','genre2',...,'genreN']
-
moviecharactersmetadata.txt - contains information about each movie character - fields: - characterID - character name - movieID - movie title - gender ("?" for unlabeled cases) - position in credits ("?" for unlabeled cases)
-
movie_lines.txt - contains the actual text of each utterance - fields: - lineID - characterID (who uttered this phrase) - movieID - character name - text of the utterance
-
movieconversations.txt - the structure of the conversations - fields - characterID of the first character involved in the conversation - characterID of the second character involved in the conversation - movieID of the movie in which the conversation occurred - list of the utterances that make the conversation, in chronological order: ['lineID1','lineID2',...,'lineIDN'] has to be matched with movielines.txt to reconstruct the actual content
-
rawscripturls.txt - the urls from which the raw sources were retrieved
C) Details on the collection procedure:
We started from raw publicly available movie scripts (sources acknowledged in rawscripturls.txt). In order to collect the metadata necessary for this study and to distinguish between two script versions of the same movie, we automatically matched each script with an entry in movie database provided by IMDB (The Internet Movie Database; data interfaces available at http://www.imdb.com/interfaces). Some amount of manual correction was also involved. When more than one movie with the same title was found in IMBD, the match was made with the most popular title (the one that received most IMDB votes)
After discarding all movies that could not be matched or that had less than 5 IMDB votes, we were left with 617 unique titles with metadata including genre, release year, IMDB rating and no. of IMDB votes and cast distribution. We then identified the pairs of characters that interact and separated their conversations automatically using simple data processing heuristics. After discarding all pairs that exchanged less than 5 conversational exchanges there were 10,292 left, exchanging 220,579 conversational exchanges (304,713 utterances). After automatically matching the names of the 9,035 involved characters to the list of cast distribution, we used the gender of each interpreting actor to infer the fictional gender of a subset of 3,321 movie characters (we raised the number of gendered 3,774 characters through manual annotation). Similarly, we collected the end credit position of a subset of 3,321 characters as a proxy for their status.
D) Contact:
Please email any questions to: cristian@cs.cornell.edu (Cristian Danescu-Niculescu-Mizil)
Step 2. Loading the Data and Data Cleaning
We have already used the wget command to download the file, and put it in our distributed file system (this process takes about 1 minute). To repeat these steps or to download data from another source follow the steps at the bottom of this worksheet on Step 1. Downloading and Loading Data into DBFS.
Let's make sure these files are in dbfs now:
// this is where the data resides in dbfs (see below to download it first, if you go to a new shard!)
display(dbutils.fs.ls("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/"))
| path | name | size |
|---|---|---|
| dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/README.txt | README.txt | 4181.0 |
| dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_characters_metadata.txt | movie_characters_metadata.txt | 705695.0 |
| dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_conversations.txt | movie_conversations.txt | 6760930.0 |
| dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_lines.txt | movie_lines.txt | 3.4641919e7 |
| dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_titles_metadata.txt | movie_titles_metadata.txt | 67289.0 |
| dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/raw_script_urls.txt | raw_script_urls.txt | 56177.0 |
sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_conversations.txt").top(5).foreach(println)
u999 +++$+++ u1006 +++$+++ m65 +++$+++ ['L227588', 'L227589', 'L227590', 'L227591', 'L227592', 'L227593', 'L227594', 'L227595', 'L227596']
u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228159', 'L228160']
u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228157', 'L228158']
u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228130', 'L228131']
u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228127', 'L228128', 'L228129']
// Load text file, leave out file paths, convert all strings to lowercase
val conversationsRaw = sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_conversations.txt").zipWithIndex()
conversationsRaw: org.apache.spark.rdd.RDD[(String, Long)] = ZippedWithIndexRDD[3709] at zipWithIndex at command-753740454082219:2
Review first 5 lines to get a sense for the data format.
conversationsRaw.top(5).foreach(println) // the first five Strings in the RDD
(u999 +++$+++ u1006 +++$+++ m65 +++$+++ ['L227588', 'L227589', 'L227590', 'L227591', 'L227592', 'L227593', 'L227594', 'L227595', 'L227596'],8954)
(u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228159', 'L228160'],8952)
(u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228157', 'L228158'],8951)
(u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228130', 'L228131'],8950)
(u998 +++$+++ u1005 +++$+++ m65 +++$+++ ['L228127', 'L228128', 'L228129'],8949)
conversationsRaw.count // there are over 83,000 conversations in total
res1: Long = 83097
import scala.util.{Failure, Success}
val regexConversation = """\s*(\w+)\s+(\+{3}\$\+{3})\s*(\w+)\s+(\2)\s*(\w+)\s+(\2)\s*(\[.*\]\s*$)""".r
case class conversationLine(a: String, b: String, c: String, d: String)
val conversationsRaw = sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_conversations.txt")
.zipWithIndex()
.map(x =>
{
val id:Long = x._2
val line = x._1
val pLine = regexConversation.findFirstMatchIn(line)
.map(m => conversationLine(m.group(1), m.group(3), m.group(5), m.group(7)))
match {
case Some(l) => Success(l)
case None => Failure(new Exception(s"Non matching input: $line"))
}
(id,pLine)
}
)
import scala.util.{Failure, Success}
regexConversation: scala.util.matching.Regex = \s*(\w+)\s+(\+{3}\$\+{3})\s*(\w+)\s+(\2)\s*(\w+)\s+(\2)\s*(\[.*\]\s*$)
defined class conversationLine
conversationsRaw: org.apache.spark.rdd.RDD[(Long, Product with Serializable with scala.util.Try[conversationLine])] = MapPartitionsRDD[3713] at map at command-753740454082223:9
conversationsRaw.filter(x => x._2.isSuccess).count()
res2: Long = 83097
conversationsRaw.filter(x => x._2.isFailure).count()
res3: Long = 0
The conversation number and line numbers of each conversation are in one line in conversationsRaw.
conversationsRaw.filter(x => x._2.isSuccess).take(5).foreach(println)
(0,Success(conversationLine(u0,u2,m0,['L194', 'L195', 'L196', 'L197'])))
(1,Success(conversationLine(u0,u2,m0,['L198', 'L199'])))
(2,Success(conversationLine(u0,u2,m0,['L200', 'L201', 'L202', 'L203'])))
(3,Success(conversationLine(u0,u2,m0,['L204', 'L205', 'L206'])))
(4,Success(conversationLine(u0,u2,m0,['L207', 'L208'])))
Let's create conversations that have just the coversation id and line-number with order information.
val conversations
= conversationsRaw
.filter(x => x._2.isSuccess)
.flatMap {
case (id,Success(l))
=> { val conv = l.d.replace("[","").replace("]","").replace("'","").replace(" ","")
val convLinesIndexed = conv.split(",").zipWithIndex
convLinesIndexed.map( cLI => (id, cLI._2, cLI._1))
}
}.toDF("conversationID","intraConversationID","lineID")
notebook:4: warning: match may not be exhaustive.
It would fail on the following input: (_, Failure(_))
.flatMap {
^
conversations: org.apache.spark.sql.DataFrame = [conversationID: bigint, intraConversationID: int ... 1 more field]
conversations.show(15)
+--------------+-------------------+------+
|conversationID|intraConversationID|lineID|
+--------------+-------------------+------+
| 0| 0| L194|
| 0| 1| L195|
| 0| 2| L196|
| 0| 3| L197|
| 1| 0| L198|
| 1| 1| L199|
| 2| 0| L200|
| 2| 1| L201|
| 2| 2| L202|
| 2| 3| L203|
| 3| 0| L204|
| 3| 1| L205|
| 3| 2| L206|
| 4| 0| L207|
| 4| 1| L208|
+--------------+-------------------+------+
only showing top 15 rows
val moviesMetaDataRaw = sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_titles_metadata.txt")
moviesMetaDataRaw.top(5).foreach(println)
m99 +++$+++ indiana jones and the temple of doom +++$+++ 1984 +++$+++ 7.50 +++$+++ 112054 +++$+++ ['action', 'adventure']
m98 +++$+++ indiana jones and the last crusade +++$+++ 1989 +++$+++ 8.30 +++$+++ 174947 +++$+++ ['action', 'adventure', 'thriller', 'action', 'adventure', 'fantasy']
m97 +++$+++ independence day +++$+++ 1996 +++$+++ 6.60 +++$+++ 151698 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller']
m96 +++$+++ invaders from mars +++$+++ 1953 +++$+++ 6.40 +++$+++ 2115 +++$+++ ['horror', 'sci-fi']
m95 +++$+++ i am legend +++$+++ 2007 +++$+++ 7.10 +++$+++ 156084 +++$+++ ['drama', 'sci-fi', 'thriller']
moviesMetaDataRaw: org.apache.spark.rdd.RDD[String] = dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_titles_metadata.txt MapPartitionsRDD[3722] at textFile at command-753740454082232:1
moviesMetaDataRaw.count() // number of movies
res8: Long = 617
import scala.util.{Failure, Success}
/* - contains information about each movie title
- fields:
- movieID,
- movie title,
- movie year,
- IMDB rating,
- no. IMDB votes,
- genres in the format ['genre1','genre2',...,'genreN']
*/
val regexMovieMetaData = """\s*(\w+)\s+(\+{3}\$\+{3})\s*(.+)\s+(\2)\s+(.+)\s+(\2)\s+(.+)\s+(\2)\s+(.+)\s+(\2)\s+(\[.*\]\s*$)""".r
case class lineInMovieMetaData(movieID: String, movieTitle: String, movieYear: String, IMDBRating: String, NumIMDBVotes: String, genres: String)
val moviesMetaDataRaw = sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_titles_metadata.txt")
.map(line =>
{
val pLine = regexMovieMetaData.findFirstMatchIn(line)
.map(m => lineInMovieMetaData(m.group(1), m.group(3), m.group(5), m.group(7), m.group(9), m.group(11)))
match {
case Some(l) => Success(l)
case None => Failure(new Exception(s"Non matching input: $line"))
}
pLine
}
)
import scala.util.{Failure, Success}
regexMovieMetaData: scala.util.matching.Regex = \s*(\w+)\s+(\+{3}\$\+{3})\s*(.+)\s+(\2)\s+(.+)\s+(\2)\s+(.+)\s+(\2)\s+(.+)\s+(\2)\s+(\[.*\]\s*$)
defined class lineInMovieMetaData
moviesMetaDataRaw: org.apache.spark.rdd.RDD[Product with Serializable with scala.util.Try[lineInMovieMetaData]] = MapPartitionsRDD[3725] at map at command-753740454082234:17
moviesMetaDataRaw.count
res9: Long = 617
moviesMetaDataRaw.filter(x => x.isSuccess).count()
res10: Long = 617
moviesMetaDataRaw.filter(x => x.isSuccess).take(10).foreach(println)
Success(lineInMovieMetaData(m0,10 things i hate about you,1999,6.90,62847,['comedy', 'romance']))
Success(lineInMovieMetaData(m1,1492: conquest of paradise,1992,6.20,10421,['adventure', 'biography', 'drama', 'history']))
Success(lineInMovieMetaData(m2,15 minutes,2001,6.10,25854,['action', 'crime', 'drama', 'thriller']))
Success(lineInMovieMetaData(m3,2001: a space odyssey,1968,8.40,163227,['adventure', 'mystery', 'sci-fi']))
Success(lineInMovieMetaData(m4,48 hrs.,1982,6.90,22289,['action', 'comedy', 'crime', 'drama', 'thriller']))
Success(lineInMovieMetaData(m5,the fifth element,1997,7.50,133756,['action', 'adventure', 'romance', 'sci-fi', 'thriller']))
Success(lineInMovieMetaData(m6,8mm,1999,6.30,48212,['crime', 'mystery', 'thriller']))
Success(lineInMovieMetaData(m7,a nightmare on elm street 4: the dream master,1988,5.20,13590,['fantasy', 'horror', 'thriller']))
Success(lineInMovieMetaData(m8,a nightmare on elm street: the dream child,1989,4.70,11092,['fantasy', 'horror', 'thriller']))
Success(lineInMovieMetaData(m9,the atomic submarine,1959,4.90,513,['sci-fi', 'thriller']))
//moviesMetaDataRaw.filter(x => x.isFailure).take(10).foreach(println) // to regex refine for casting
val moviesMetaData
= moviesMetaDataRaw
.filter(x => x.isSuccess)
.map { case Success(l) => l }
.toDF().select("movieID","movieTitle","movieYear")
notebook:4: warning: match may not be exhaustive.
It would fail on the following input: Failure(_)
.map { case Success(l) => l }
^
moviesMetaData: org.apache.spark.sql.DataFrame = [movieID: string, movieTitle: string ... 1 more field]
moviesMetaData.show(10,false)
+-------+---------------------------------------------+---------+
|movieID|movieTitle |movieYear|
+-------+---------------------------------------------+---------+
|m0 |10 things i hate about you |1999 |
|m1 |1492: conquest of paradise |1992 |
|m2 |15 minutes |2001 |
|m3 |2001: a space odyssey |1968 |
|m4 |48 hrs. |1982 |
|m5 |the fifth element |1997 |
|m6 |8mm |1999 |
|m7 |a nightmare on elm street 4: the dream master|1988 |
|m8 |a nightmare on elm street: the dream child |1989 |
|m9 |the atomic submarine |1959 |
+-------+---------------------------------------------+---------+
only showing top 10 rows
val linesRaw = sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_lines.txt")
linesRaw: org.apache.spark.rdd.RDD[String] = dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_lines.txt MapPartitionsRDD[3733] at textFile at command-753740454082242:1
linesRaw.count() // number of lines making up the conversations
res15: Long = 304713
Review first 5 lines to get a sense for the data format.
linesRaw.top(5).foreach(println)
L99999 +++$+++ u4166 +++$+++ m278 +++$+++ DULANEY +++$+++ You didn't know about it before that?
L99998 +++$+++ u4168 +++$+++ m278 +++$+++ JOANNE +++$+++ To show you this. It's a letter from that lawyer, Koehler. He wrote it to me the day after I saw him. He's the one who told me I could get the money if Miss Lawson went to jail.
L99997 +++$+++ u4166 +++$+++ m278 +++$+++ DULANEY +++$+++ Why'd you come here?
L99996 +++$+++ u4168 +++$+++ m278 +++$+++ JOANNE +++$+++ I'm gonna go to jail. I know they're gonna make it look like I did it. They gotta put it on someone.
L99995 +++$+++ u4168 +++$+++ m278 +++$+++ JOANNE +++$+++ What do you think I've got? A gun? Maybe I'm gonna kill you too. Maybe I'll blow your head off right now.
To see 5 random lines in the lines.txt evaluate the following cell.
linesRaw.takeSample(false, 5).foreach(println)
L216035 +++$+++ u5302 +++$+++ m351 +++$+++ RAMBO +++$+++ Colonel.
L597568 +++$+++ u8300 +++$+++ m564 +++$+++ LOMBARD +++$+++ I don�t.
L513032 +++$+++ u7667 +++$+++ m518 +++$+++ LINDA +++$+++ He's no more an Indian than I am though. Anyhow, Doyle's gonna try and tease you and be mean to you to show off to his friends. Just like he does to Frank and me sometimes. You just ignore it. Or stay out here away from 'em if he'll let you. He's an okay guy till he gets drunk but tonight he'll get drunk. I guarantee it.
L35914 +++$+++ u313 +++$+++ m19 +++$+++ JESSE +++$+++ Yesss, but I was thinking, I could come by, and then take Zee out. Some place near. With other folk. Near. Here. But out.
L426481 +++$+++ u2391 +++$+++ m153 +++$+++ COOLEY +++$+++ - and share one of your graves.
import scala.util.{Failure, Success}
/* field in line.txt are:
- lineID
- characterID (who uttered this phrase)
- movieID
- character name
- text of the utterance
*/
val regexLine = """\s*(\w+)\s+(\+{3}\$\+{3})\s*(\w+)\s+(\2)\s*(\w+)\s+(\2)\s*(.+)\s+(\2)\s*(.*$)""".r
case class lineInMovie(lineID: String, characterID: String, movieID: String, characterName: String, text: String)
val linesRaw = sc.textFile("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_lines.txt")
.map(line =>
{
val pLine = regexLine.findFirstMatchIn(line)
.map(m => lineInMovie(m.group(1), m.group(3), m.group(5), m.group(7), m.group(9)))
match {
case Some(l) => Success(l)
case None => Failure(new Exception(s"Non matching input: $line"))
}
pLine
}
)
import scala.util.{Failure, Success}
regexLine: scala.util.matching.Regex = \s*(\w+)\s+(\+{3}\$\+{3})\s*(\w+)\s+(\2)\s*(\w+)\s+(\2)\s*(.+)\s+(\2)\s*(.*$)
defined class lineInMovie
linesRaw: org.apache.spark.rdd.RDD[Product with Serializable with scala.util.Try[lineInMovie]] = MapPartitionsRDD[3737] at map at command-753740454082248:15
linesRaw.filter(x => x.isSuccess).count()
res18: Long = 304713
linesRaw.filter(x => x.isFailure).count()
res19: Long = 0
linesRaw.filter(x => x.isSuccess).take(5).foreach(println)
Success(lineInMovie(L1045,u0,m0,BIANCA,They do not!))
Success(lineInMovie(L1044,u2,m0,CAMERON,They do to!))
Success(lineInMovie(L985,u0,m0,BIANCA,I hope so.))
Success(lineInMovie(L984,u2,m0,CAMERON,She okay?))
Success(lineInMovie(L925,u0,m0,BIANCA,Let's go.))
Let's make a DataFrame out of the successfully parsed line.
val lines
= linesRaw
.filter(x => x.isSuccess)
.map { case Success(l) => l }
.toDF()
.join(moviesMetaData, "movieID") // and join it to get movie meta data
notebook:4: warning: match may not be exhaustive.
It would fail on the following input: Failure(_)
.map { case Success(l) => l }
^
lines: org.apache.spark.sql.DataFrame = [movieID: string, lineID: string ... 5 more fields]
lines.show(5)
+-------+-------+-----------+-------------+--------------------+-------------+---------+
|movieID| lineID|characterID|characterName| text| movieTitle|movieYear|
+-------+-------+-----------+-------------+--------------------+-------------+---------+
| m203|L593445| u3102| HAGEN|You owe the Don a...|the godfather| 1972|
| m203|L593444| u3094| BONASERA|Yes, I understand...|the godfather| 1972|
| m203|L593443| u3102| HAGEN|This is Tom Hagen...|the godfather| 1972|
| m203|L593425| u3102| HAGEN| Yes. |the godfather| 1972|
| m203|L593424| u3094| BONASERA|The Don himself i...|the godfather| 1972|
+-------+-------+-----------+-------------+--------------------+-------------+---------+
only showing top 5 rows
Dialogs with Lines
Let's join ght two DataFrames on lineID next.
val convLines = conversations.join(lines, "lineID").sort($"conversationID", $"intraConversationID")
convLines: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [lineID: string, conversationID: bigint ... 7 more fields]
convLines.count
res24: Long = 304713
conversations.count
res25: Long = 304713
display(convLines)
| lineID | conversationID | intraConversationID | movieID | characterID | characterName | text | movieTitle | movieYear |
|---|---|---|---|---|---|---|---|---|
| L194 | 0.0 | 0.0 | m0 | u0 | BIANCA | Can we make this quick? Roxanne Korrine and Andrew Barrett are having an incredibly horrendous public break- up on the quad. Again. | 10 things i hate about you | 1999 |
| L195 | 0.0 | 1.0 | m0 | u2 | CAMERON | Well, I thought we'd start with pronunciation, if that's okay with you. | 10 things i hate about you | 1999 |
| L196 | 0.0 | 2.0 | m0 | u0 | BIANCA | Not the hacking and gagging and spitting part. Please. | 10 things i hate about you | 1999 |
| L197 | 0.0 | 3.0 | m0 | u2 | CAMERON | Okay... then how 'bout we try out some French cuisine. Saturday? Night? | 10 things i hate about you | 1999 |
| L198 | 1.0 | 0.0 | m0 | u0 | BIANCA | You're asking me out. That's so cute. What's your name again? | 10 things i hate about you | 1999 |
| L199 | 1.0 | 1.0 | m0 | u2 | CAMERON | Forget it. | 10 things i hate about you | 1999 |
| L200 | 2.0 | 0.0 | m0 | u0 | BIANCA | No, no, it's my fault -- we didn't have a proper introduction --- | 10 things i hate about you | 1999 |
| L201 | 2.0 | 1.0 | m0 | u2 | CAMERON | Cameron. | 10 things i hate about you | 1999 |
| L202 | 2.0 | 2.0 | m0 | u0 | BIANCA | The thing is, Cameron -- I'm at the mercy of a particularly hideous breed of loser. My sister. I can't date until she does. | 10 things i hate about you | 1999 |
| L203 | 2.0 | 3.0 | m0 | u2 | CAMERON | Seems like she could get a date easy enough... | 10 things i hate about you | 1999 |
| L204 | 3.0 | 0.0 | m0 | u2 | CAMERON | Why? | 10 things i hate about you | 1999 |
| L205 | 3.0 | 1.0 | m0 | u0 | BIANCA | Unsolved mystery. She used to be really popular when she started high school, then it was just like she got sick of it or something. | 10 things i hate about you | 1999 |
| L206 | 3.0 | 2.0 | m0 | u2 | CAMERON | That's a shame. | 10 things i hate about you | 1999 |
| L207 | 4.0 | 0.0 | m0 | u0 | BIANCA | Gosh, if only we could find Kat a boyfriend... | 10 things i hate about you | 1999 |
| L208 | 4.0 | 1.0 | m0 | u2 | CAMERON | Let me see what I can do. | 10 things i hate about you | 1999 |
| L271 | 5.0 | 0.0 | m0 | u0 | BIANCA | C'esc ma tete. This is my head | 10 things i hate about you | 1999 |
| L272 | 5.0 | 1.0 | m0 | u2 | CAMERON | Right. See? You're ready for the quiz. | 10 things i hate about you | 1999 |
| L273 | 5.0 | 2.0 | m0 | u0 | BIANCA | I don't want to know how to say that though. I want to know useful things. Like where the good stores are. How much does champagne cost? Stuff like Chat. I have never in my life had to point out my head to someone. | 10 things i hate about you | 1999 |
| L274 | 5.0 | 3.0 | m0 | u2 | CAMERON | That's because it's such a nice one. | 10 things i hate about you | 1999 |
| L275 | 5.0 | 4.0 | m0 | u0 | BIANCA | Forget French. | 10 things i hate about you | 1999 |
| L276 | 6.0 | 0.0 | m0 | u0 | BIANCA | How is our little Find the Wench A Date plan progressing? | 10 things i hate about you | 1999 |
| L277 | 6.0 | 1.0 | m0 | u2 | CAMERON | Well, there's someone I think might be -- | 10 things i hate about you | 1999 |
| L280 | 7.0 | 0.0 | m0 | u2 | CAMERON | There. | 10 things i hate about you | 1999 |
| L281 | 7.0 | 1.0 | m0 | u0 | BIANCA | Where? | 10 things i hate about you | 1999 |
| L363 | 8.0 | 0.0 | m0 | u2 | CAMERON | You got something on your mind? | 10 things i hate about you | 1999 |
| L364 | 8.0 | 1.0 | m0 | u0 | BIANCA | I counted on you to help my cause. You and that thug are obviously failing. Aren't we ever going on our date? | 10 things i hate about you | 1999 |
| L365 | 9.0 | 0.0 | m0 | u2 | CAMERON | You have my word. As a gentleman | 10 things i hate about you | 1999 |
| L366 | 9.0 | 1.0 | m0 | u0 | BIANCA | You're sweet. | 10 things i hate about you | 1999 |
| L367 | 10.0 | 0.0 | m0 | u2 | CAMERON | How do you get your hair to look like that? | 10 things i hate about you | 1999 |
| L368 | 10.0 | 1.0 | m0 | u0 | BIANCA | Eber's Deep Conditioner every two days. And I never, ever use a blowdryer without the diffuser attachment. | 10 things i hate about you | 1999 |
| L401 | 11.0 | 0.0 | m0 | u2 | CAMERON | Sure have. | 10 things i hate about you | 1999 |
| L402 | 11.0 | 1.0 | m0 | u0 | BIANCA | I really, really, really wanna go, but I can't. Not unless my sister goes. | 10 things i hate about you | 1999 |
| L403 | 11.0 | 2.0 | m0 | u2 | CAMERON | I'm workin' on it. But she doesn't seem to be goin' for him. | 10 things i hate about you | 1999 |
| L404 | 12.0 | 0.0 | m0 | u2 | CAMERON | She's not a... | 10 things i hate about you | 1999 |
| L405 | 12.0 | 1.0 | m0 | u0 | BIANCA | Lesbian? No. I found a picture of Jared Leto in one of her drawers, so I'm pretty sure she's not harboring same-sex tendencies. | 10 things i hate about you | 1999 |
| L406 | 12.0 | 2.0 | m0 | u2 | CAMERON | So that's the kind of guy she likes? Pretty ones? | 10 things i hate about you | 1999 |
| L407 | 12.0 | 3.0 | m0 | u0 | BIANCA | Who knows? All I've ever heard her say is that she'd dip before dating a guy that smokes. | 10 things i hate about you | 1999 |
| L575 | 13.0 | 0.0 | m0 | u0 | BIANCA | Hi. | 10 things i hate about you | 1999 |
| L576 | 13.0 | 1.0 | m0 | u2 | CAMERON | Looks like things worked out tonight, huh? | 10 things i hate about you | 1999 |
| L577 | 14.0 | 0.0 | m0 | u0 | BIANCA | You know Chastity? | 10 things i hate about you | 1999 |
| L578 | 14.0 | 1.0 | m0 | u2 | CAMERON | I believe we share an art instructor | 10 things i hate about you | 1999 |
| L662 | 15.0 | 0.0 | m0 | u2 | CAMERON | Have fun tonight? | 10 things i hate about you | 1999 |
| L663 | 15.0 | 1.0 | m0 | u0 | BIANCA | Tons | 10 things i hate about you | 1999 |
| L693 | 16.0 | 0.0 | m0 | u2 | CAMERON | I looked for you back at the party, but you always seemed to be "occupied". | 10 things i hate about you | 1999 |
| L694 | 16.0 | 1.0 | m0 | u0 | BIANCA | I was? | 10 things i hate about you | 1999 |
| L695 | 16.0 | 2.0 | m0 | u2 | CAMERON | You never wanted to go out with 'me, did you? | 10 things i hate about you | 1999 |
| L696 | 17.0 | 0.0 | m0 | u0 | BIANCA | Well, no... | 10 things i hate about you | 1999 |
| L697 | 17.0 | 1.0 | m0 | u2 | CAMERON | Then that's all you had to say. | 10 things i hate about you | 1999 |
| L698 | 17.0 | 2.0 | m0 | u0 | BIANCA | But | 10 things i hate about you | 1999 |
| L699 | 17.0 | 3.0 | m0 | u2 | CAMERON | You always been this selfish? | 10 things i hate about you | 1999 |
| L860 | 18.0 | 0.0 | m0 | u0 | BIANCA | Then Guillermo says, "If you go any lighter, you're gonna look like an extra on 90210." | 10 things i hate about you | 1999 |
| L861 | 18.0 | 1.0 | m0 | u2 | CAMERON | No... | 10 things i hate about you | 1999 |
| L862 | 19.0 | 0.0 | m0 | u0 | BIANCA | do you listen to this crap? | 10 things i hate about you | 1999 |
| L863 | 19.0 | 1.0 | m0 | u2 | CAMERON | What crap? | 10 things i hate about you | 1999 |
| L864 | 19.0 | 2.0 | m0 | u0 | BIANCA | Me. This endless ...blonde babble. I'm like, boring myself. | 10 things i hate about you | 1999 |
| L865 | 19.0 | 3.0 | m0 | u2 | CAMERON | Thank God! If I had to hear one more story about your coiffure... | 10 things i hate about you | 1999 |
| L866 | 20.0 | 0.0 | m0 | u2 | CAMERON | I figured you'd get to the good stuff eventually. | 10 things i hate about you | 1999 |
| L867 | 20.0 | 1.0 | m0 | u0 | BIANCA | What good stuff? | 10 things i hate about you | 1999 |
| L868 | 20.0 | 2.0 | m0 | u2 | CAMERON | The "real you". | 10 things i hate about you | 1999 |
| L869 | 20.0 | 3.0 | m0 | u0 | BIANCA | Like my fear of wearing pastels? | 10 things i hate about you | 1999 |
| L870 | 21.0 | 0.0 | m0 | u0 | BIANCA | I'm kidding. You know how sometimes you just become this "persona"? And you don't know how to quit? | 10 things i hate about you | 1999 |
| L871 | 21.0 | 1.0 | m0 | u2 | CAMERON | No | 10 things i hate about you | 1999 |
| L872 | 21.0 | 2.0 | m0 | u0 | BIANCA | Okay -- you're gonna need to learn how to lie. | 10 things i hate about you | 1999 |
| L924 | 22.0 | 0.0 | m0 | u2 | CAMERON | Wow | 10 things i hate about you | 1999 |
| L925 | 22.0 | 1.0 | m0 | u0 | BIANCA | Let's go. | 10 things i hate about you | 1999 |
| L984 | 23.0 | 0.0 | m0 | u2 | CAMERON | She okay? | 10 things i hate about you | 1999 |
| L985 | 23.0 | 1.0 | m0 | u0 | BIANCA | I hope so. | 10 things i hate about you | 1999 |
| L1044 | 24.0 | 0.0 | m0 | u2 | CAMERON | They do to! | 10 things i hate about you | 1999 |
| L1045 | 24.0 | 1.0 | m0 | u0 | BIANCA | They do not! | 10 things i hate about you | 1999 |
| L49 | 25.0 | 0.0 | m0 | u0 | BIANCA | Did you change your hair? | 10 things i hate about you | 1999 |
| L50 | 25.0 | 1.0 | m0 | u3 | CHASTITY | No. | 10 things i hate about you | 1999 |
| L51 | 25.0 | 2.0 | m0 | u0 | BIANCA | You might wanna think about it | 10 things i hate about you | 1999 |
| L571 | 26.0 | 0.0 | m0 | u0 | BIANCA | Where did he go? He was just here. | 10 things i hate about you | 1999 |
| L572 | 26.0 | 1.0 | m0 | u3 | CHASTITY | Who? | 10 things i hate about you | 1999 |
| L573 | 26.0 | 2.0 | m0 | u0 | BIANCA | Joey. | 10 things i hate about you | 1999 |
| L579 | 27.0 | 0.0 | m0 | u3 | CHASTITY | Great | 10 things i hate about you | 1999 |
| L580 | 27.0 | 1.0 | m0 | u0 | BIANCA | Would you mind getting me a drink, Cameron? | 10 things i hate about you | 1999 |
| L595 | 28.0 | 0.0 | m0 | u0 | BIANCA | He practically proposed when he found out we had the same dermatologist. I mean. Dr. Bonchowski is great an all, but he's not exactly relevant party conversation. | 10 things i hate about you | 1999 |
| L596 | 28.0 | 1.0 | m0 | u3 | CHASTITY | Is he oily or dry? | 10 things i hate about you | 1999 |
| L597 | 28.0 | 2.0 | m0 | u0 | BIANCA | Combination. I don't know -- I thought he'd be different. More of a gentleman... | 10 things i hate about you | 1999 |
| L598 | 29.0 | 0.0 | m0 | u3 | CHASTITY | Bianca, I don't think the highlights of dating Joey Dorsey are going to include door-opening and coat-holding. | 10 things i hate about you | 1999 |
| L599 | 29.0 | 1.0 | m0 | u0 | BIANCA | Sometimes I wonder if the guys we're supposed to want to go out with are the ones we actually want to go out with, you know? | 10 things i hate about you | 1999 |
| L600 | 29.0 | 2.0 | m0 | u3 | CHASTITY | All I know is -- I'd give up my private line to go out with a guy like Joey. | 10 things i hate about you | 1999 |
| L659 | 30.0 | 0.0 | m0 | u0 | BIANCA | I have to be home in twenty minutes. | 10 things i hate about you | 1999 |
| L660 | 30.0 | 1.0 | m0 | u3 | CHASTITY | I don't have to be home 'til two. | 10 things i hate about you | 1999 |
| L952 | 31.0 | 0.0 | m0 | u3 | CHASTITY | You think you ' re the only sophomore at the prom? | 10 things i hate about you | 1999 |
| L953 | 31.0 | 1.0 | m0 | u0 | BIANCA | I did. | 10 things i hate about you | 1999 |
| L394 | 32.0 | 0.0 | m0 | u4 | JOEY | It's more | 10 things i hate about you | 1999 |
| L395 | 32.0 | 1.0 | m0 | u0 | BIANCA | Expensive? | 10 things i hate about you | 1999 |
| L396 | 33.0 | 0.0 | m0 | u4 | JOEY | Exactly So, you going to Bogey Lowenbrau's thing on Saturday? | 10 things i hate about you | 1999 |
| L397 | 33.0 | 1.0 | m0 | u0 | BIANCA | Hopefully. | 10 things i hate about you | 1999 |
| L589 | 34.0 | 0.0 | m0 | u4 | JOEY | So yeah, I've got the Sears catalog thing going -- and the tube sock gig " that's gonna be huge. And then I'm up for an ad for Queen Harry next week. | 10 things i hate about you | 1999 |
| L590 | 34.0 | 1.0 | m0 | u0 | BIANCA | Queen Harry? | 10 things i hate about you | 1999 |
| L591 | 34.0 | 2.0 | m0 | u4 | JOEY | It's a gay cruise line, but I'll be, like, wearing a uniform and stuff. | 10 things i hate about you | 1999 |
| L592 | 35.0 | 0.0 | m0 | u0 | BIANCA | Neat... | 10 things i hate about you | 1999 |
| L593 | 35.0 | 1.0 | m0 | u4 | JOEY | My agent says I've got a good shot at being the Prada guy next year. | 10 things i hate about you | 1999 |
| L756 | 36.0 | 0.0 | m0 | u4 | JOEY | Hey, sweet cheeks. | 10 things i hate about you | 1999 |
| L757 | 36.0 | 1.0 | m0 | u0 | BIANCA | Hi, Joey. | 10 things i hate about you | 1999 |
| L758 | 36.0 | 2.0 | m0 | u4 | JOEY | You're concentrating awfully hard considering it's gym class. | 10 things i hate about you | 1999 |
| L759 | 37.0 | 0.0 | m0 | u4 | JOEY | Listen, I want to talk to you about the prom. | 10 things i hate about you | 1999 |
| L760 | 37.0 | 1.0 | m0 | u0 | BIANCA | You know the deal. I can ' t go if Kat doesn't go -- | 10 things i hate about you | 1999 |
| L164 | 38.0 | 0.0 | m0 | u5 | KAT | Where've you been? | 10 things i hate about you | 1999 |
| L165 | 38.0 | 1.0 | m0 | u0 | BIANCA | Nowhere... Hi, Daddy. | 10 things i hate about you | 1999 |
| L319 | 39.0 | 0.0 | m0 | u5 | KAT | I have the potential to smack the crap out of you if you don't get out of my way. | 10 things i hate about you | 1999 |
| L320 | 39.0 | 1.0 | m0 | u0 | BIANCA | Can you at least start wearing a bra? | 10 things i hate about you | 1999 |
| L441 | 40.0 | 0.0 | m0 | u0 | BIANCA | Oh my God, does this mean you're becoming normal? | 10 things i hate about you | 1999 |
| L442 | 40.0 | 1.0 | m0 | u5 | KAT | It means that Gigglepuss is playing at Club Skunk and we're going. | 10 things i hate about you | 1999 |
| L443 | 40.0 | 2.0 | m0 | u0 | BIANCA | Oh, I thought you might have a date I don't know why I'm bothering to ask, but are you going to Bogey Lowenstein's party Saturday night? | 10 things i hate about you | 1999 |
| L444 | 40.0 | 3.0 | m0 | u5 | KAT | What do you think? | 10 things i hate about you | 1999 |
| L445 | 40.0 | 4.0 | m0 | u0 | BIANCA | I think you're a freak. I think you do this to torture me. And I think you suck. | 10 things i hate about you | 1999 |
| L525 | 41.0 | 0.0 | m0 | u0 | BIANCA | You're ruining my life' Because you won't be normal, I can't be normal. | 10 things i hate about you | 1999 |
| L526 | 41.0 | 1.0 | m0 | u5 | KAT | What's normal? | 10 things i hate about you | 1999 |
| L527 | 41.0 | 2.0 | m0 | u0 | BIANCA | Bogey Lowenstein's party is normal, but you're too busy listening to Bitches Who Need Prozac to know that. | 10 things i hate about you | 1999 |
| L529 | 42.0 | 0.0 | m0 | u0 | BIANCA | Can't you forget for just one night that you're completely wretched? | 10 things i hate about you | 1999 |
| L530 | 42.0 | 1.0 | m0 | u5 | KAT | At least I'm not a clouted fen- sucked hedge-pig. | 10 things i hate about you | 1999 |
| L531 | 43.0 | 0.0 | m0 | u0 | BIANCA | Like I'm supposed to know what that even means. | 10 things i hate about you | 1999 |
| L532 | 43.0 | 1.0 | m0 | u5 | KAT | It's Shakespeare. Maybe you've heard of him? | 10 things i hate about you | 1999 |
| L533 | 43.0 | 2.0 | m0 | u0 | BIANCA | Yeah, he's your freak friend Mandella's boyfriend. I guess since I'm not allowed to go out, I should obsess over a dead guy, too. | 10 things i hate about you | 1999 |
| L542 | 44.0 | 0.0 | m0 | u0 | BIANCA | You are so completely unbalanced. | 10 things i hate about you | 1999 |
| L543 | 44.0 | 1.0 | m0 | u5 | KAT | Can we go now? | 10 things i hate about you | 1999 |
| L601 | 45.0 | 0.0 | m0 | u5 | KAT | Bianca, I need to talk to you -- I need to tell you -- | 10 things i hate about you | 1999 |
| L602 | 45.0 | 1.0 | m0 | u0 | BIANCA | I really don't think I need any social advice from you right now. | 10 things i hate about you | 1999 |
| L655 | 46.0 | 0.0 | m0 | u0 | BIANCA | I don't get you. You act like you're too good for any of this, and then you go totally apeshit when you get here. | 10 things i hate about you | 1999 |
| L656 | 46.0 | 1.0 | m0 | u5 | KAT | You're welcome. | 10 things i hate about you | 1999 |
| L889 | 47.0 | 0.0 | m0 | u5 | KAT | Listen, I know you hate having to sit home because I'm not Susie High School. | 10 things i hate about you | 1999 |
| L890 | 47.0 | 1.0 | m0 | u0 | BIANCA | Like you care. | 10 things i hate about you | 1999 |
| L891 | 47.0 | 2.0 | m0 | u5 | KAT | I do care. But I'm a firm believer in doing something for your own reasons, not someone else ' s . | 10 things i hate about you | 1999 |
| L892 | 47.0 | 3.0 | m0 | u0 | BIANCA | I wish I had that luxury. I'm the only sophomore that got asked to the prom and I can't go, because you won ' t. | 10 things i hate about you | 1999 |
| L893 | 48.0 | 0.0 | m0 | u5 | KAT | Joey never told you we went out, did he? | 10 things i hate about you | 1999 |
| L894 | 48.0 | 1.0 | m0 | u0 | BIANCA | What? | 10 things i hate about you | 1999 |
| L895 | 48.0 | 2.0 | m0 | u5 | KAT | In 9th. For a month | 10 things i hate about you | 1999 |
| L896 | 48.0 | 3.0 | m0 | u0 | BIANCA | Why? | 10 things i hate about you | 1999 |
| L897 | 48.0 | 4.0 | m0 | u5 | KAT | He was, like, a total babe | 10 things i hate about you | 1999 |
| L898 | 48.0 | 5.0 | m0 | u0 | BIANCA | But you hate Joey | 10 things i hate about you | 1999 |
| L899 | 48.0 | 6.0 | m0 | u5 | KAT | Now I do. Back then, was a different story. | 10 things i hate about you | 1999 |
| L900 | 48.0 | 7.0 | m0 | u0 | BIANCA | As in... | 10 things i hate about you | 1999 |
| L901 | 49.0 | 0.0 | m0 | u5 | KAT | He said everyone was doing it. So I did it. | 10 things i hate about you | 1999 |
| L902 | 49.0 | 1.0 | m0 | u0 | BIANCA | You did what? | 10 things i hate about you | 1999 |
| L903 | 49.0 | 2.0 | m0 | u5 | KAT | Just once. Afterwards, I told him I didn't want to anymore. I wasn't ready. He got pissed. Then he broke up with me. | 10 things i hate about you | 1999 |
| L904 | 50.0 | 0.0 | m0 | u0 | BIANCA | But | 10 things i hate about you | 1999 |
| L905 | 50.0 | 1.0 | m0 | u5 | KAT | After that, I swore I'd never do anything just because "everyone else" was doing it. And I haven't since. Except for Bogey's party, and my stunning gastro-intestinal display -- | 10 things i hate about you | 1999 |
| L906 | 50.0 | 2.0 | m0 | u0 | BIANCA | Why didn't you tell me? | 10 things i hate about you | 1999 |
| L907 | 50.0 | 3.0 | m0 | u5 | KAT | I wanted to let you make up your own mind about him. | 10 things i hate about you | 1999 |
| L908 | 50.0 | 4.0 | m0 | u0 | BIANCA | No. you didn't! If you really thought I could make my own decisions, you would've let me go out with him instead of helping Daddy hold me hostage. | 10 things i hate about you | 1999 |
| L909 | 51.0 | 0.0 | m0 | u5 | KAT | That's not | 10 things i hate about you | 1999 |
| L910 | 51.0 | 1.0 | m0 | u0 | BIANCA | I'm not stupid enough to repeat your mistakes. | 10 things i hate about you | 1999 |
| L911 | 51.0 | 2.0 | m0 | u5 | KAT | I guess I thought I was protecting you. | 10 things i hate about you | 1999 |
| L912 | 51.0 | 3.0 | m0 | u0 | BIANCA | God, you're just like him! Just keep me locked away in the dark, so I can't experience anything for myself | 10 things i hate about you | 1999 |
| L913 | 51.0 | 4.0 | m0 | u5 | KAT | Not all experiences are good, Bianca. You can't always trust the people you want to. | 10 things i hate about you | 1999 |
| L914 | 51.0 | 5.0 | m0 | u0 | BIANCA | I guess I'll never know, will I? | 10 things i hate about you | 1999 |
| L982 | 52.0 | 0.0 | m0 | u0 | BIANCA | You looked beautiful last night, you know. | 10 things i hate about you | 1999 |
| L983 | 52.0 | 1.0 | m0 | u5 | KAT | So did you | 10 things i hate about you | 1999 |
| L1007 | 53.0 | 0.0 | m0 | u0 | BIANCA | Let go! | 10 things i hate about you | 1999 |
| L1008 | 53.0 | 1.0 | m0 | u5 | KAT | You set me up. | 10 things i hate about you | 1999 |
| L1009 | 53.0 | 2.0 | m0 | u0 | BIANCA | I just wanted -- | 10 things i hate about you | 1999 |
| L1010 | 53.0 | 3.0 | m0 | u5 | KAT | What? To completely damage me? To send me to therapy forever? What? | 10 things i hate about you | 1999 |
| L1011 | 53.0 | 4.0 | m0 | u0 | BIANCA | No! I just wanted | 10 things i hate about you | 1999 |
| L1021 | 54.0 | 0.0 | m0 | u0 | BIANCA | Is that woman a complete fruit-loop or is it just me? | 10 things i hate about you | 1999 |
| L1022 | 54.0 | 1.0 | m0 | u5 | KAT | It's just you. | 10 things i hate about you | 1999 |
| L1051 | 55.0 | 0.0 | m0 | u0 | BIANCA | Patrick -- is that- a. | 10 things i hate about you | 1999 |
| L1052 | 55.0 | 1.0 | m0 | u5 | KAT | Perm? | 10 things i hate about you | 1999 |
| L179 | 56.0 | 0.0 | m0 | u0 | BIANCA | Now don't get upset. Daddy, but there's this boy... and I think he might ask... | 10 things i hate about you | 1999 |
| L180 | 56.0 | 1.0 | m0 | u11 | WALTER | No! You're not dating until your sister starts dating. End of discussion. | 10 things i hate about you | 1999 |
| L181 | 56.0 | 2.0 | m0 | u0 | BIANCA | What if she never starts dating? | 10 things i hate about you | 1999 |
| L182 | 56.0 | 3.0 | m0 | u11 | WALTER | Then neither will you. And I'll get to sleep at night. | 10 things i hate about you | 1999 |
| L183 | 56.0 | 4.0 | m0 | u0 | BIANCA | But it's not fair -- she's a mutant, Daddy! | 10 things i hate about you | 1999 |
| L189 | 57.0 | 0.0 | m0 | u0 | BIANCA | But she doesn't want to date. | 10 things i hate about you | 1999 |
| L190 | 57.0 | 1.0 | m0 | u11 | WALTER | Exactly my point | 10 things i hate about you | 1999 |
| L517 | 58.0 | 0.0 | m0 | u0 | BIANCA | Daddy, I -- | 10 things i hate about you | 1999 |
| L518 | 58.0 | 1.0 | m0 | u11 | WALTER | And where're you going? | 10 things i hate about you | 1999 |
| L519 | 58.0 | 2.0 | m0 | u0 | BIANCA | If you must know, we were attempting to go to a small study group of friends. | 10 things i hate about you | 1999 |
| L520 | 58.0 | 3.0 | m0 | u11 | WALTER | Otherwise known as an orgy? | 10 things i hate about you | 1999 |
| L521 | 58.0 | 4.0 | m0 | u0 | BIANCA | It's just a party. Daddy, but I knew you'd forbid me to go since "Gloria Steinem" over there isn't going -- | 10 things i hate about you | 1999 |
| L523 | 59.0 | 0.0 | m0 | u0 | BIANCA | Daddy, people expect me to be there! | 10 things i hate about you | 1999 |
| L524 | 59.0 | 1.0 | m0 | u11 | WALTER | If Kat's not going, you're not going. | 10 things i hate about you | 1999 |
| L536 | 60.0 | 0.0 | m0 | u11 | WALTER | Oh, God. It's starting. | 10 things i hate about you | 1999 |
| L537 | 60.0 | 1.0 | m0 | u0 | BIANCA | It's just a party. Daddy. | 10 things i hate about you | 1999 |
| L538 | 61.0 | 0.0 | m0 | u11 | WALTER | Wear the belly before you go. | 10 things i hate about you | 1999 |
| L539 | 61.0 | 1.0 | m0 | u0 | BIANCA | Daddy, no! | 10 things i hate about you | 1999 |
| L540 | 61.0 | 2.0 | m0 | u11 | WALTER | Just for a minute | 10 things i hate about you | 1999 |
| L544 | 62.0 | 0.0 | m0 | u11 | WALTER | Promise me you won't talk to any boys unless your sister is present. | 10 things i hate about you | 1999 |
| L545 | 62.0 | 1.0 | m0 | u0 | BIANCA | Why? | 10 things i hate about you | 1999 |
| L546 | 62.0 | 2.0 | m0 | u11 | WALTER | Because she'll scare them away. | 10 things i hate about you | 1999 |
| L878 | 63.0 | 0.0 | m0 | u0 | BIANCA | Daddy, I want to discuss the prom with you. It's tomorrow night -- | 10 things i hate about you | 1999 |
| L879 | 63.0 | 1.0 | m0 | u11 | WALTER | The prom? Kat has a date? | 10 things i hate about you | 1999 |
| L880 | 63.0 | 2.0 | m0 | u0 | BIANCA | No, but | 10 things i hate about you | 1999 |
| L881 | 63.0 | 3.0 | m0 | u11 | WALTER | It's that hot rod Joey, right? That ' s who you want me to bend my rules for? | 10 things i hate about you | 1999 |
| L882 | 63.0 | 4.0 | m0 | u0 | BIANCA | He's not a "hot rod". Whatever that is. | 10 things i hate about you | 1999 |
| L883 | 63.0 | 5.0 | m0 | u11 | WALTER | You're not going unless your sister goes. End of story. | 10 things i hate about you | 1999 |
| L884 | 63.0 | 6.0 | m0 | u0 | BIANCA | Fine. I see that I'm a prisoner in my own house. I'm not a daughter. I'm a possession! | 10 things i hate about you | 1999 |
| L922 | 64.0 | 0.0 | m0 | u11 | WALTER | I'm missing something. | 10 things i hate about you | 1999 |
| L923 | 64.0 | 1.0 | m0 | u0 | BIANCA | I have a date, Daddy. And he ' s not a captain of oppression like some men we know. | 10 things i hate about you | 1999 |
| L458 | 65.0 | 0.0 | m0 | u9 | PATRICK | Always a pleasure, Brucie. | 10 things i hate about you | 1999 |
| L459 | 65.0 | 1.0 | m0 | u1 | BRUCE | Didn't have you pegged for a Gigglepuss fan. Aren't they a little too pre-teen belly-button ring for you? | 10 things i hate about you | 1999 |
| L460 | 65.0 | 2.0 | m0 | u9 | PATRICK | Fan of a fan. You see a couple of minors come in? | 10 things i hate about you | 1999 |
| L461 | 65.0 | 3.0 | m0 | u1 | BRUCE | Never | 10 things i hate about you | 1999 |
| L462 | 65.0 | 4.0 | m0 | u9 | PATRICK | Padua girls. One tall, decent body. The other one kinda short and undersexed? | 10 things i hate about you | 1999 |
| L463 | 65.0 | 5.0 | m0 | u1 | BRUCE | Just sent 'em through. | 10 things i hate about you | 1999 |
| L63 | 66.0 | 0.0 | m0 | u7 | MICHAEL | You the new guy? | 10 things i hate about you | 1999 |
| L64 | 66.0 | 1.0 | m0 | u2 | CAMERON | So they tell me... | 10 things i hate about you | 1999 |
| L65 | 66.0 | 2.0 | m0 | u7 | MICHAEL | C'mon. I'm supposed to give you the tour. | 10 things i hate about you | 1999 |
| L66 | 67.0 | 0.0 | m0 | u7 | MICHAEL | So -- which Dakota you from? | 10 things i hate about you | 1999 |
| L67 | 67.0 | 1.0 | m0 | u2 | CAMERON | North, actually. How'd you ? | 10 things i hate about you | 1999 |
| L68 | 67.0 | 2.0 | m0 | u7 | MICHAEL | I was kidding. People actually live there? | 10 things i hate about you | 1999 |
| L69 | 67.0 | 3.0 | m0 | u2 | CAMERON | Yeah. A couple. We're outnumbered by the cows, though. | 10 things i hate about you | 1999 |
| L70 | 67.0 | 4.0 | m0 | u7 | MICHAEL | How many people were in your old school? | 10 things i hate about you | 1999 |
| L71 | 67.0 | 5.0 | m0 | u2 | CAMERON | Thirty-two. | 10 things i hate about you | 1999 |
| L72 | 67.0 | 6.0 | m0 | u7 | MICHAEL | Get out! | 10 things i hate about you | 1999 |
| L73 | 67.0 | 7.0 | m0 | u2 | CAMERON | How many people go here? | 10 things i hate about you | 1999 |
| L74 | 67.0 | 8.0 | m0 | u7 | MICHAEL | Couple thousand. Most of them evil | 10 things i hate about you | 1999 |
| L77 | 68.0 | 0.0 | m0 | u2 | CAMERON | That I'm used to. | 10 things i hate about you | 1999 |
| L78 | 68.0 | 1.0 | m0 | u7 | MICHAEL | Yeah, but these guys have never seen a horse. They just jack off to Clint Eastwood. | 10 things i hate about you | 1999 |
| L87 | 69.0 | 0.0 | m0 | u2 | CAMERON | That girl -- I -- | 10 things i hate about you | 1999 |
| L88 | 69.0 | 1.0 | m0 | u7 | MICHAEL | You burn, you pine, you perish? | 10 things i hate about you | 1999 |
| L89 | 69.0 | 2.0 | m0 | u2 | CAMERON | Who is she? | 10 things i hate about you | 1999 |
| L90 | 69.0 | 3.0 | m0 | u7 | MICHAEL | Bianca Stratford. Sophomore. Don't even think about it | 10 things i hate about you | 1999 |
| L91 | 69.0 | 4.0 | m0 | u2 | CAMERON | Why not? | 10 things i hate about you | 1999 |
| L92 | 69.0 | 5.0 | m0 | u7 | MICHAEL | I could start with your haircut, but it doesn't matter. She's not allowed to date until her older sister does. And that's an impossibility. | 10 things i hate about you | 1999 |
| L139 | 70.0 | 0.0 | m0 | u2 | CAMERON | Why do girls like that always like guys like that? | 10 things i hate about you | 1999 |
| L140 | 70.0 | 1.0 | m0 | u7 | MICHAEL | Because they're bred to. Their mothers liked guys like that, and their grandmothers before them. Their gene pool is rarely diluted. | 10 things i hate about you | 1999 |
| L141 | 70.0 | 2.0 | m0 | u2 | CAMERON | He always have that shit-eating grin? | 10 things i hate about you | 1999 |
| L142 | 70.0 | 3.0 | m0 | u7 | MICHAEL | Joey Dorsey? Perma-shit-grin. I wish I could say he's a moron, but he's number twelve in the class. And a model. Mostly regional stuff, but he's rumored to have a big tube sock ad coming out. | 10 things i hate about you | 1999 |
| L143 | 71.0 | 0.0 | m0 | u7 | MICHAEL | You know French? | 10 things i hate about you | 1999 |
| L144 | 71.0 | 1.0 | m0 | u2 | CAMERON | Sure do ... my Mom's from Canada | 10 things i hate about you | 1999 |
| L145 | 71.0 | 2.0 | m0 | u7 | MICHAEL | Guess who just signed up for a tutor? | 10 things i hate about you | 1999 |
| L146 | 71.0 | 3.0 | m0 | u2 | CAMERON | You mean I'd get a chance to talk to her? | 10 things i hate about you | 1999 |
| L147 | 71.0 | 4.0 | m0 | u7 | MICHAEL | You could consecrate with her, my friend. | 10 things i hate about you | 1999 |
| L157 | 72.0 | 0.0 | m0 | u7 | MICHAEL | Yeah, just a minor encounter with the shrew. | 10 things i hate about you | 1999 |
| L158 | 72.0 | 1.0 | m0 | u2 | CAMERON | That's her? Bianca's sister? | 10 things i hate about you | 1999 |
| L159 | 72.0 | 2.0 | m0 | u7 | MICHAEL | The mewling, rampalian wretch herself. | 10 things i hate about you | 1999 |
| L210 | 73.0 | 0.0 | m0 | u2 | CAMERON | I teach her French, get to know her, dazzle her with charm and she falls in love with me. | 10 things i hate about you | 1999 |
| L211 | 73.0 | 1.0 | m0 | u7 | MICHAEL | Unlikely, but even so, she still can't go out with you. So what's the point? | 10 things i hate about you | 1999 |
| L212 | 74.0 | 0.0 | m0 | u2 | CAMERON | What about him? | 10 things i hate about you | 1999 |
| L213 | 74.0 | 1.0 | m0 | u7 | MICHAEL | You wanna go out with him? | 10 things i hate about you | 1999 |
| L215 | 75.0 | 0.0 | m0 | u7 | MICHAEL | What makes you think he'll do it? | 10 things i hate about you | 1999 |
| L216 | 75.0 | 1.0 | m0 | u2 | CAMERON | He seems like he thrives on danger | 10 things i hate about you | 1999 |
| L217 | 75.0 | 2.0 | m0 | u7 | MICHAEL | No kidding. He's a criminal. I heard he lit a state trooper on fire. He just got out of Alcatraz... | 10 things i hate about you | 1999 |
| L218 | 75.0 | 3.0 | m0 | u2 | CAMERON | They always let felons sit in on Honors Biology? | 10 things i hate about you | 1999 |
| L219 | 75.0 | 4.0 | m0 | u7 | MICHAEL | I'm serious, man, he's whacked. He sold his own liver on the black market so he could buy new speakers. | 10 things i hate about you | 1999 |
| L220 | 75.0 | 5.0 | m0 | u2 | CAMERON | Forget his reputation. Do you think we've got a plan or not? | 10 things i hate about you | 1999 |
| L221 | 75.0 | 6.0 | m0 | u7 | MICHAEL | Did she actually say she'd go out with you? | 10 things i hate about you | 1999 |
| L222 | 75.0 | 7.0 | m0 | u2 | CAMERON | That's what I just said | 10 things i hate about you | 1999 |
| L223 | 76.0 | 0.0 | m0 | u7 | MICHAEL | You know, if you do go out with Bianca, you'd be set. You'd outrank everyone. Strictly A-list. With me by your side. | 10 things i hate about you | 1999 |
| L224 | 76.0 | 1.0 | m0 | u2 | CAMERON | I thought you hated those people. | 10 things i hate about you | 1999 |
| L225 | 76.0 | 2.0 | m0 | u7 | MICHAEL | Hey -- I've gotta have a few clients when I get to Wall Street. | 10 things i hate about you | 1999 |
| L244 | 77.0 | 0.0 | m0 | u2 | CAMERON | You got him involved? | 10 things i hate about you | 1999 |
| L245 | 77.0 | 1.0 | m0 | u7 | MICHAEL | Like we had a choice? Besides -- when you let the enemy think he's orchestrating the battle, you're in a position of power. We let him pretend he's calling the shots, and while he's busy setting up the plan, you have time to woo Bianca. | 10 things i hate about you | 1999 |
| L388 | 78.0 | 0.0 | m0 | u2 | CAMERON | This is it. A golden opportunity. Patrick can ask Katarina to the party. | 10 things i hate about you | 1999 |
| L389 | 78.0 | 1.0 | m0 | u7 | MICHAEL | In that case, we'll need to make it a school-wide blow out. | 10 things i hate about you | 1999 |
| L390 | 78.0 | 2.0 | m0 | u2 | CAMERON | Will Bogey get bent? | 10 things i hate about you | 1999 |
| L391 | 78.0 | 3.0 | m0 | u7 | MICHAEL | Are you kidding? He'll piss himself with joy. He's the ultimate kiss ass. | 10 things i hate about you | 1999 |
| L416 | 79.0 | 0.0 | m0 | u2 | CAMERON | Number one. She hates smokers | 10 things i hate about you | 1999 |
| L417 | 79.0 | 1.0 | m0 | u7 | MICHAEL | It's a lung cancer issue | 10 things i hate about you | 1999 |
| L418 | 79.0 | 2.0 | m0 | u2 | CAMERON | Her favorite uncle | 10 things i hate about you | 1999 |
| L419 | 79.0 | 3.0 | m0 | u7 | MICHAEL | Dead at forty-one. | 10 things i hate about you | 1999 |
| L424 | 80.0 | 0.0 | m0 | u7 | MICHAEL | He's pretty! | 10 things i hate about you | 1999 |
| L425 | 80.0 | 1.0 | m0 | u2 | CAMERON | Okay! I wasn't sure | 10 things i hate about you | 1999 |
| L432 | 81.0 | 0.0 | m0 | u7 | MICHAEL | Assail your ears for one night. | 10 things i hate about you | 1999 |
| L433 | 81.0 | 1.0 | m0 | u2 | CAMERON | It's her favorite band. | 10 things i hate about you | 1999 |
| L499 | 82.0 | 0.0 | m0 | u7 | MICHAEL | You told me that part already. | 10 things i hate about you | 1999 |
| L500 | 82.0 | 1.0 | m0 | u2 | CAMERON | Hell, I've just been going over the whole thing in my head and - | 10 things i hate about you | 1999 |
| L583 | 83.0 | 0.0 | m0 | u7 | MICHAEL | Extremely unfortunate maneuver. | 10 things i hate about you | 1999 |
| L584 | 83.0 | 1.0 | m0 | u2 | CAMERON | The hell is that? What kind of 'guy just picks up a girl and carries her away while you're talking to her? | 10 things i hate about you | 1999 |
| L585 | 83.0 | 2.0 | m0 | u7 | MICHAEL | Buttholus extremus. But hey, you're making progress. | 10 things i hate about you | 1999 |
| L586 | 83.0 | 3.0 | m0 | u2 | CAMERON | No, I ' m not. | 10 things i hate about you | 1999 |
| L778 | 84.0 | 0.0 | m0 | u2 | CAMERON | You humiliated the woman! Sacrifice yourself on the altar of dignity and even the score. | 10 things i hate about you | 1999 |
| L779 | 84.0 | 1.0 | m0 | u7 | MICHAEL | Best case scenario, you're back on the payroll for awhile. | 10 things i hate about you | 1999 |
| L385 | 85.0 | 0.0 | m0 | u2 | CAMERON | And he means that strictly in a non- prison-movie type of way. | 10 things i hate about you | 1999 |
| L386 | 85.0 | 1.0 | m0 | u9 | PATRICK | Yeah -- we'll see. | 10 things i hate about you | 1999 |
| L411 | 86.0 | 0.0 | m0 | u9 | PATRICK | What've you got for me? | 10 things i hate about you | 1999 |
| L412 | 86.0 | 1.0 | m0 | u2 | CAMERON | I've retrieved certain pieces of information on Miss Katarina Stratford I think you'll find helpful. | 10 things i hate about you | 1999 |
| L426 | 87.0 | 0.0 | m0 | u2 | CAMERON | Okay -- Likes: Thai food, feminist prose, and "angry, stinky girl music of the indie-rock persuasion". | 10 things i hate about you | 1999 |
| L427 | 87.0 | 1.0 | m0 | u9 | PATRICK | So what does that give me? I'm supposed to buy her some noodles and a book and sit around listening to chicks who can't play their instruments? | 10 things i hate about you | 1999 |
| L430 | 88.0 | 0.0 | m0 | u2 | CAMERON | Gigglepuss is playing there tomorrow night. | 10 things i hate about you | 1999 |
| L431 | 88.0 | 1.0 | m0 | u9 | PATRICK | Don't make me do it, man | 10 things i hate about you | 1999 |
| L619 | 89.0 | 0.0 | m0 | u9 | PATRICK | Cameron, I'm a little busy | 10 things i hate about you | 1999 |
| L620 | 89.0 | 1.0 | m0 | u2 | CAMERON | It's off. The whole thing. | 10 things i hate about you | 1999 |
| L621 | 90.0 | 0.0 | m0 | u9 | PATRICK | What 're you talking about? | 10 things i hate about you | 1999 |
| L622 | 90.0 | 1.0 | m0 | u2 | CAMERON | She's partial to Joey, not me | 10 things i hate about you | 1999 |
| L623 | 91.0 | 0.0 | m0 | u9 | PATRICK | Cameron -- do you like the girl? | 10 things i hate about you | 1999 |
| L624 | 91.0 | 1.0 | m0 | u2 | CAMERON | Sure | 10 things i hate about you | 1999 |
| L625 | 91.0 | 2.0 | m0 | u9 | PATRICK | Then, go get her | 10 things i hate about you | 1999 |
| L725 | 92.0 | 0.0 | m0 | u2 | CAMERON | What'd you do to her? | 10 things i hate about you | 1999 |
| L726 | 92.0 | 1.0 | m0 | u9 | PATRICK | I don ' t know. I decided not to nail her when she was too drunk to remember it. | 10 things i hate about you | 1999 |
| L740 | 93.0 | 0.0 | m0 | u2 | CAMERON | She hates you with the fire of a thousand suns . That's a direct quote | 10 things i hate about you | 1999 |
| L741 | 93.0 | 1.0 | m0 | u9 | PATRICK | She just needs time to cool off I'll give it a day. | 10 things i hate about you | 1999 |
| L743 | 94.0 | 0.0 | m0 | u9 | PATRICK | You makin' any headway? | 10 things i hate about you | 1999 |
| L744 | 94.0 | 1.0 | m0 | u2 | CAMERON | She kissed me. | 10 things i hate about you | 1999 |
| L745 | 94.0 | 2.0 | m0 | u9 | PATRICK | Where? | 10 things i hate about you | 1999 |
| L780 | 95.0 | 0.0 | m0 | u9 | PATRICK | What's the worst? | 10 things i hate about you | 1999 |
| L781 | 95.0 | 1.0 | m0 | u2 | CAMERON | You get the girl. | 10 things i hate about you | 1999 |
| L148 | 96.0 | 0.0 | m0 | u4 | JOEY | The vintage look is over, Kat. Haven't you been reading your Sassy? | 10 things i hate about you | 1999 |
| L149 | 96.0 | 1.0 | m0 | u5 | KAT | Yeah, and I noticed the only part of you featured in your big Kmart spread was your elbow. Tough break. | 10 things i hate about you | 1999 |
| L150 | 96.0 | 2.0 | m0 | u4 | JOEY | They're running the rest of me next month. | 10 things i hate about you | 1999 |
| L335 | 97.0 | 0.0 | m0 | u5 | KAT | Hey -- do you mind? | 10 things i hate about you | 1999 |
| L336 | 97.0 | 1.0 | m0 | u4 | JOEY | Not at all | 10 things i hate about you | 1999 |
| L554 | 98.0 | 0.0 | m0 | u4 | JOEY | Where ya goin? | 10 things i hate about you | 1999 |
| L555 | 98.0 | 1.0 | m0 | u5 | KAT | Away. | 10 things i hate about you | 1999 |
| L556 | 98.0 | 2.0 | m0 | u4 | JOEY | Your sister here? | 10 things i hate about you | 1999 |
| L557 | 99.0 | 0.0 | m0 | u5 | KAT | Leave my sister alone. | 10 things i hate about you | 1999 |
| L558 | 99.0 | 1.0 | m0 | u4 | JOEY | And why would I do that? | 10 things i hate about you | 1999 |
| L285 | 100.0 | 0.0 | m0 | u9 | PATRICK | Yeah | 10 things i hate about you | 1999 |
| L286 | 100.0 | 1.0 | m0 | u4 | JOEY | What do you think? | 10 things i hate about you | 1999 |
| L287 | 101.0 | 0.0 | m0 | u9 | PATRICK | Two legs, nice rack... | 10 things i hate about you | 1999 |
| L288 | 101.0 | 1.0 | m0 | u4 | JOEY | Yeah, whatever. I want you to go out with her. | 10 things i hate about you | 1999 |
| L289 | 101.0 | 2.0 | m0 | u9 | PATRICK | Sure, Sparky. I'll get right on it. | 10 things i hate about you | 1999 |
| L290 | 101.0 | 3.0 | m0 | u4 | JOEY | You just said | 10 things i hate about you | 1999 |
| L291 | 101.0 | 4.0 | m0 | u9 | PATRICK | You need money to take a girl out | 10 things i hate about you | 1999 |
| L292 | 101.0 | 5.0 | m0 | u4 | JOEY | But you'd go out with her if you had the cake? | 10 things i hate about you | 1999 |
| L294 | 102.0 | 0.0 | m0 | u4 | JOEY | You got it, Verona. I pick up the tab, you do the honors. | 10 things i hate about you | 1999 |
| L295 | 102.0 | 1.0 | m0 | u9 | PATRICK | You're gonna pay me to take out some girl? | 10 things i hate about you | 1999 |
| L296 | 102.0 | 2.0 | m0 | u4 | JOEY | I can't date her sister until that one gets a boyfriend. And that's the catch. She doesn't want a boyfriend. | 10 things i hate about you | 1999 |
| L297 | 102.0 | 3.0 | m0 | u9 | PATRICK | How much? | 10 things i hate about you | 1999 |
| L298 | 103.0 | 0.0 | m0 | u9 | PATRICK | I can't take a girl like that out on twenty bucks. | 10 things i hate about you | 1999 |
| L299 | 103.0 | 1.0 | m0 | u4 | JOEY | Fine, thirty. | 10 things i hate about you | 1999 |
| L300 | 104.0 | 0.0 | m0 | u4 | JOEY | Take it or leave it. This isn't a negotiation. | 10 things i hate about you | 1999 |
| L301 | 104.0 | 1.0 | m0 | u9 | PATRICK | Fifty, and you've got your man. | 10 things i hate about you | 1999 |
| L349 | 105.0 | 0.0 | m0 | u4 | JOEY | When I shell out fifty, I expect results. | 10 things i hate about you | 1999 |
| L350 | 105.0 | 1.0 | m0 | u9 | PATRICK | I'm on it | 10 things i hate about you | 1999 |
| L351 | 105.0 | 2.0 | m0 | u4 | JOEY | Watching the bitch trash my car doesn't count as a date. | 10 things i hate about you | 1999 |
| L352 | 105.0 | 3.0 | m0 | u9 | PATRICK | I got her under control. She just acts crazed in public to keep up the image. | 10 things i hate about you | 1999 |
| L354 | 106.0 | 0.0 | m0 | u9 | PATRICK | I just upped my price | 10 things i hate about you | 1999 |
| L355 | 106.0 | 1.0 | m0 | u4 | JOEY | What? | 10 things i hate about you | 1999 |
| L356 | 106.0 | 2.0 | m0 | u9 | PATRICK | A hundred bucks a date. | 10 things i hate about you | 1999 |
| L357 | 106.0 | 3.0 | m0 | u4 | JOEY | Forget it. | 10 things i hate about you | 1999 |
| L358 | 106.0 | 4.0 | m0 | u9 | PATRICK | Forget her sister, then. | 10 things i hate about you | 1999 |
| L605 | 107.0 | 0.0 | m0 | u9 | PATRICK | It's about time. | 10 things i hate about you | 1999 |
| L606 | 107.0 | 1.0 | m0 | u4 | JOEY | A deal's a deal. | 10 things i hate about you | 1999 |
| L607 | 108.0 | 0.0 | m0 | u4 | JOEY | How'd you do it? | 10 things i hate about you | 1999 |
| L608 | 108.0 | 1.0 | m0 | u9 | PATRICK | Do what? | 10 things i hate about you | 1999 |
| L609 | 108.0 | 2.0 | m0 | u4 | JOEY | Get her to act like a human | 10 things i hate about you | 1999 |
| L747 | 109.0 | 0.0 | m0 | u9 | PATRICK | I don't know, Dorsey. ..the limo.-the flowers. Another hundred for the tux -- | 10 things i hate about you | 1999 |
| L748 | 109.0 | 1.0 | m0 | u4 | JOEY | Enough with the Barbie n' Ken shit. I know. | 10 things i hate about you | 1999 |
| L231 | 110.0 | 0.0 | m0 | u7 | MICHAEL | Hey. | 10 things i hate about you | 1999 |
| L232 | 110.0 | 1.0 | m0 | u4 | JOEY | Are you lost? | 10 things i hate about you | 1999 |
| L233 | 110.0 | 2.0 | m0 | u7 | MICHAEL | Nope - just came by to chat | 10 things i hate about you | 1999 |
| L234 | 110.0 | 3.0 | m0 | u4 | JOEY | We don't chat. | 10 things i hate about you | 1999 |
| L235 | 110.0 | 4.0 | m0 | u7 | MICHAEL | Well, actually, I thought I'd run an idea by you. You know, just to see if you're interested. | 10 things i hate about you | 1999 |
| L236 | 110.0 | 5.0 | m0 | u4 | JOEY | We're not. | 10 things i hate about you | 1999 |
| L238 | 111.0 | 0.0 | m0 | u7 | MICHAEL | But she can't go out with you because her sister is this insane head case and no one will go out with her. right? | 10 things i hate about you | 1999 |
| L239 | 111.0 | 1.0 | m0 | u4 | JOEY | Does this conversation have a purpose? | 10 things i hate about you | 1999 |
| L240 | 111.0 | 2.0 | m0 | u7 | MICHAEL | So what you need to do is recruit a guy who'll go out with her. Someone who's up for the job. | 10 things i hate about you | 1999 |
| L502 | 112.0 | 0.0 | m0 | u4 | JOEY | I hear you're helpin' Verona. | 10 things i hate about you | 1999 |
| L503 | 112.0 | 1.0 | m0 | u7 | MICHAEL | Uh, yeah. We're old friend* | 10 things i hate about you | 1999 |
| L504 | 112.0 | 2.0 | m0 | u4 | JOEY | You and Verona? | 10 things i hate about you | 1999 |
| L505 | 112.0 | 3.0 | m0 | u7 | MICHAEL | What? We took bathes together when we were kids. | 10 things i hate about you | 1999 |
| L508 | 113.0 | 0.0 | m0 | u4 | JOEY | You better not fuck this up. I'm heavily invested. | 10 things i hate about you | 1999 |
| L509 | 113.0 | 1.0 | m0 | u7 | MICHAEL | Hey -- it's all for the higher good right? | 10 things i hate about you | 1999 |
| L123 | 114.0 | 0.0 | m0 | u6 | MANDELLA | Who's that? | 10 things i hate about you | 1999 |
| L124 | 114.0 | 1.0 | m0 | u5 | KAT | Patrick Verona Random skid. | 10 things i hate about you | 1999 |
| L125 | 114.0 | 2.0 | m0 | u6 | MANDELLA | That's Pat Verona? The one who was gone for a year? I heard he was doing porn movies. | 10 things i hate about you | 1999 |
| L126 | 114.0 | 3.0 | m0 | u5 | KAT | I'm sure he's completely incapable of doing anything that interesting. | 10 things i hate about you | 1999 |
| L127 | 114.0 | 4.0 | m0 | u6 | MANDELLA | He always look so | 10 things i hate about you | 1999 |
| L128 | 114.0 | 5.0 | m0 | u5 | KAT | Block E? | 10 things i hate about you | 1999 |
| L129 | 115.0 | 0.0 | m0 | u5 | KAT | Mandella, eat. Starving yourself is a very slow way to die. | 10 things i hate about you | 1999 |
| L130 | 115.0 | 1.0 | m0 | u6 | MANDELLA | Just a little. | 10 things i hate about you | 1999 |
| L131 | 116.0 | 0.0 | m0 | u5 | KAT | What's this? | 10 things i hate about you | 1999 |
| L132 | 116.0 | 1.0 | m0 | u6 | MANDELLA | An attempted slit. | 10 things i hate about you | 1999 |
| L133 | 117.0 | 0.0 | m0 | u5 | KAT | I realize that the men of this fine institution are severely lacking, but killing yourself so you can be with William Shakespeare is beyond the scope of normal teenage obsessions. You're venturing far past daytime talk show fodder and entering the world of those who need very expensive therapy. | 10 things i hate about you | 1999 |
| L134 | 117.0 | 1.0 | m0 | u6 | MANDELLA | But imagine the things he'd say during sex. | 10 things i hate about you | 1999 |
| L151 | 118.0 | 0.0 | m0 | u5 | KAT | The people at this school are so incredibly foul. | 10 things i hate about you | 1999 |
| L152 | 118.0 | 1.0 | m0 | u6 | MANDELLA | You could always go with me. I'm sure William has some friends. | 10 things i hate about you | 1999 |
| L247 | 119.0 | 0.0 | m0 | u5 | KAT | So he has this huge raging fit about Sarah Lawrence and insists that I go to his male-dominated, puking frat boy, number one golf team school. I have no say at all. | 10 things i hate about you | 1999 |
| L248 | 119.0 | 1.0 | m0 | u6 | MANDELLA | William would never have gone to a state school. | 10 things i hate about you | 1999 |
| L249 | 119.0 | 2.0 | m0 | u5 | KAT | William didn't even go to high school | 10 things i hate about you | 1999 |
| L250 | 119.0 | 3.0 | m0 | u6 | MANDELLA | That's never been proven | 10 things i hate about you | 1999 |
| L251 | 119.0 | 4.0 | m0 | u5 | KAT | Neither has his heterosexuality. | 10 things i hate about you | 1999 |
| L252 | 120.0 | 0.0 | m0 | u5 | KAT | I appreciate your efforts toward a speedy death, but I'm consuming. Do you mind? | 10 things i hate about you | 1999 |
| L253 | 120.0 | 1.0 | m0 | u6 | MANDELLA | Does it matter? | 10 things i hate about you | 1999 |
| L254 | 120.0 | 2.0 | m0 | u5 | KAT | If I was Bianca, it would be, "Any school you want, precious. Don't forget your tiara." | 10 things i hate about you | 1999 |
| L447 | 121.0 | 0.0 | m0 | u6 | MANDELLA | You think this'll work? | 10 things i hate about you | 1999 |
| L448 | 121.0 | 1.0 | m0 | u5 | KAT | No fear. | 10 things i hate about you | 1999 |
| L490 | 122.0 | 0.0 | m0 | u6 | MANDELLA | What'd he say? | 10 things i hate about you | 1999 |
| L491 | 122.0 | 1.0 | m0 | u5 | KAT | Who cares? | 10 things i hate about you | 1999 |
| L716 | 123.0 | 0.0 | m0 | u6 | MANDELLA | You went to the party? I thought we were officially opposed to suburban social activity. | 10 things i hate about you | 1999 |
| L717 | 123.0 | 1.0 | m0 | u5 | KAT | I didn't have a choice. | 10 things i hate about you | 1999 |
| L718 | 123.0 | 2.0 | m0 | u6 | MANDELLA | You didn't have a choice? Where's Kat and what have you done with her? | 10 things i hate about you | 1999 |
| L719 | 123.0 | 3.0 | m0 | u5 | KAT | I did Bianca a favor and it backfired. | 10 things i hate about you | 1999 |
| L720 | 123.0 | 4.0 | m0 | u6 | MANDELLA | You didn't | 10 things i hate about you | 1999 |
| L721 | 123.0 | 5.0 | m0 | u5 | KAT | I got drunk. I puked. I got rejected. It was big fun. | 10 things i hate about you | 1999 |
| L750 | 124.0 | 0.0 | m0 | u5 | KAT | Can you even imagine? Who the hell would go to this a bastion of commercial excess? | 10 things i hate about you | 1999 |
| L751 | 124.0 | 1.0 | m0 | u6 | MANDELLA | Well, I guess we're not, since we don't have dates . | 10 things i hate about you | 1999 |
| L752 | 124.0 | 2.0 | m0 | u5 | KAT | Listen to you! You sound like Betty, all pissed off because Archie is taking Veronica. | 10 things i hate about you | 1999 |
| L753 | 124.0 | 3.0 | m0 | u6 | MANDELLA | Okay, okay, we won't go. It's not like I have a dress anyway | 10 things i hate about you | 1999 |
| L754 | 124.0 | 4.0 | m0 | u5 | KAT | You ' re looking at this from the wrong perspective. We're making a statement. | 10 things i hate about you | 1999 |
| L755 | 124.0 | 5.0 | m0 | u6 | MANDELLA | Oh, good. Something new and different for us. | 10 things i hate about you | 1999 |
| L946 | 125.0 | 0.0 | m0 | u6 | MANDELLA | Have you seen him? | 10 things i hate about you | 1999 |
| L947 | 125.0 | 1.0 | m0 | u5 | KAT | Who? | 10 things i hate about you | 1999 |
| L948 | 125.0 | 2.0 | m0 | u6 | MANDELLA | William - he asked me to meet him here. | 10 things i hate about you | 1999 |
| L949 | 125.0 | 3.0 | m0 | u5 | KAT | Oh, honey -- tell me we haven't' progressed to full-on hallucinations. | 10 things i hate about you | 1999 |
| L304 | 126.0 | 0.0 | m0 | u9 | PATRICK | I mean Wo-man. How ya doin'? | 10 things i hate about you | 1999 |
| L305 | 126.0 | 1.0 | m0 | u5 | KAT | Sweating like a pig, actually. And yourself? | 10 things i hate about you | 1999 |
| L306 | 126.0 | 2.0 | m0 | u9 | PATRICK | There's a way to get a guy's attention. | 10 things i hate about you | 1999 |
| L307 | 126.0 | 3.0 | m0 | u5 | KAT | My mission in life. | 10 things i hate about you | 1999 |
| L309 | 127.0 | 0.0 | m0 | u9 | PATRICK | Pick you up Friday, then | 10 things i hate about you | 1999 |
| L310 | 127.0 | 1.0 | m0 | u5 | KAT | Oh, right. Friday. | 10 things i hate about you | 1999 |
| L311 | 128.0 | 0.0 | m0 | u9 | PATRICK | The night I take you to places you've never been before. And back. | 10 things i hate about you | 1999 |
| L312 | 128.0 | 1.0 | m0 | u5 | KAT | Like where? The 7-Eleven on Burnside? Do you even know my name, screwboy? | 10 things i hate about you | 1999 |
| L313 | 128.0 | 2.0 | m0 | u9 | PATRICK | I know a lot more than that | 10 things i hate about you | 1999 |
| L322 | 129.0 | 0.0 | m0 | u9 | PATRICK | You hate me don't you? | 10 things i hate about you | 1999 |
| L323 | 129.0 | 1.0 | m0 | u5 | KAT | I don't really think you warrant that strong an emotion. | 10 things i hate about you | 1999 |
| L324 | 129.0 | 2.0 | m0 | u9 | PATRICK | Then say you'll spend Dollar Night at the track with me. | 10 things i hate about you | 1999 |
| L325 | 129.0 | 3.0 | m0 | u5 | KAT | And why would I do that? | 10 things i hate about you | 1999 |
| L326 | 129.0 | 4.0 | m0 | u9 | PATRICK | Come on -- the ponies, the flat beer, you with money in your eyes, me with my hand on your ass... | 10 things i hate about you | 1999 |
| L327 | 129.0 | 5.0 | m0 | u5 | KAT | You -- covered in my vomit. | 10 things i hate about you | 1999 |
| L328 | 129.0 | 6.0 | m0 | u9 | PATRICK | Seven-thirty? | 10 things i hate about you | 1999 |
| L330 | 130.0 | 0.0 | m0 | u5 | KAT | Are you following me? | 10 things i hate about you | 1999 |
| L331 | 130.0 | 1.0 | m0 | u9 | PATRICK | I was in the laundromat. I saw your car. Thought I'd say hi. | 10 things i hate about you | 1999 |
| L332 | 130.0 | 2.0 | m0 | u5 | KAT | Hi | 10 things i hate about you | 1999 |
| L333 | 131.0 | 0.0 | m0 | u9 | PATRICK | You're not a big talker, are you? | 10 things i hate about you | 1999 |
| L334 | 131.0 | 1.0 | m0 | u5 | KAT | Depends on the topic. My fenders don't really whip me into a verbal frenzy. | 10 things i hate about you | 1999 |
| L473 | 132.0 | 0.0 | m0 | u9 | PATRICK | hey. Great show, huh? | 10 things i hate about you | 1999 |
| L474 | 132.0 | 1.0 | m0 | u5 | KAT | 10 things i hate about you | 1999 | |
| L475 | 133.0 | 0.0 | m0 | u9 | PATRICK | Excuse me? | 10 things i hate about you | 1999 |
| L476 | 133.0 | 1.0 | m0 | u5 | KAT | That's what you want, isn't it? | 10 things i hate about you | 1999 |
| L477 | 133.0 | 2.0 | m0 | u9 | PATRICK | Do you mind? You're sort of ruining it for me. | 10 things i hate about you | 1999 |
| L481 | 134.0 | 0.0 | m0 | u9 | PATRICK | You know, these guys are no Bikini Kill or The Raincoats, but they're right up there. | 10 things i hate about you | 1999 |
| L482 | 134.0 | 1.0 | m0 | u5 | KAT | You know who The Raincoats are? | 10 things i hate about you | 1999 |
| L483 | 134.0 | 2.0 | m0 | u9 | PATRICK | Why, don't you? | 10 things i hate about you | 1999 |
| L564 | 135.0 | 0.0 | m0 | u9 | PATRICK | What's this? | 10 things i hate about you | 1999 |
| L565 | 135.0 | 1.0 | m0 | u5 | KAT | "I'm getting trashed, man." Isn't that what you're supposed to do at a party? | 10 things i hate about you | 1999 |
| L566 | 135.0 | 2.0 | m0 | u9 | PATRICK | I say, do what you wanna do. | 10 things i hate about you | 1999 |
| L567 | 135.0 | 3.0 | m0 | u5 | KAT | Funny, you're the only one | 10 things i hate about you | 1999 |
| L610 | 136.0 | 0.0 | m0 | u9 | PATRICK | Okay? | 10 things i hate about you | 1999 |
| L611 | 136.0 | 1.0 | m0 | u5 | KAT | I'm fine. I'm | 10 things i hate about you | 1999 |
| L612 | 137.0 | 0.0 | m0 | u9 | PATRICK | You're not okay. | 10 things i hate about you | 1999 |
| L613 | 137.0 | 1.0 | m0 | u5 | KAT | I just need to lie down for awhile | 10 things i hate about you | 1999 |
| L614 | 137.0 | 2.0 | m0 | u9 | PATRICK | Uh, uh. You lie down and you'll go to sleep | 10 things i hate about you | 1999 |
| L615 | 137.0 | 3.0 | m0 | u5 | KAT | I know, just let me sleep | 10 things i hate about you | 1999 |
| L616 | 137.0 | 4.0 | m0 | u9 | PATRICK | What if you have a concussion? My dog went to sleep with a concussion and woke up a vegetable. Not that I could tell the difference... | 10 things i hate about you | 1999 |
| L626 | 138.0 | 0.0 | m0 | u5 | KAT | This is so patronizing. | 10 things i hate about you | 1999 |
| L627 | 138.0 | 1.0 | m0 | u9 | PATRICK | Leave it to you to use big words when you're shitfaced. | 10 things i hate about you | 1999 |
| L628 | 138.0 | 2.0 | m0 | u5 | KAT | Why 're you doing this? | 10 things i hate about you | 1999 |
| L629 | 138.0 | 3.0 | m0 | u9 | PATRICK | I told you | 10 things i hate about you | 1999 |
| L630 | 138.0 | 4.0 | m0 | u5 | KAT | You don't care if I die | 10 things i hate about you | 1999 |
| L631 | 138.0 | 5.0 | m0 | u9 | PATRICK | Sure, I do | 10 things i hate about you | 1999 |
| L632 | 138.0 | 6.0 | m0 | u5 | KAT | Why? | 10 things i hate about you | 1999 |
| L633 | 138.0 | 7.0 | m0 | u9 | PATRICK | Because then I'd have to start taking out girls who like me. | 10 things i hate about you | 1999 |
| L634 | 138.0 | 8.0 | m0 | u5 | KAT | Like you could find one | 10 things i hate about you | 1999 |
| L635 | 138.0 | 9.0 | m0 | u9 | PATRICK | See that? Who needs affection when I've got blind hatred? | 10 things i hate about you | 1999 |
| L636 | 138.0 | 10.0 | m0 | u5 | KAT | Just let me sit down. | 10 things i hate about you | 1999 |
| L639 | 139.0 | 0.0 | m0 | u9 | PATRICK | Why'd you let him get to you? | 10 things i hate about you | 1999 |
| L640 | 139.0 | 1.0 | m0 | u5 | KAT | Who? | 10 things i hate about you | 1999 |
| L641 | 139.0 | 2.0 | m0 | u9 | PATRICK | Dorsey. | 10 things i hate about you | 1999 |
| L642 | 139.0 | 3.0 | m0 | u5 | KAT | I hate him. | 10 things i hate about you | 1999 |
| L643 | 139.0 | 4.0 | m0 | u9 | PATRICK | I know. It'd have to be a pretty big deal to get you to mainline tequila. You don't seem like the type. | 10 things i hate about you | 1999 |
| L644 | 139.0 | 5.0 | m0 | u5 | KAT | Hey man. . . You don ' t think I can be "cool"? You don't think I can be "laid back" like everyone else? | 10 things i hate about you | 1999 |
| L645 | 139.0 | 6.0 | m0 | u9 | PATRICK | I thought you were above all that | 10 things i hate about you | 1999 |
| L646 | 139.0 | 7.0 | m0 | u5 | KAT | You know what they say | 10 things i hate about you | 1999 |
| L650 | 140.0 | 0.0 | m0 | u9 | PATRICK | Kat! Wake up! | 10 things i hate about you | 1999 |
| L651 | 140.0 | 1.0 | m0 | u5 | KAT | What? | 10 things i hate about you | 1999 |
| L668 | 141.0 | 0.0 | m0 | u9 | PATRICK | And I'm in control of it. | 10 things i hate about you | 1999 |
| L669 | 141.0 | 1.0 | m0 | u5 | KAT | But it's Gigglepuss - I know you like them. I saw you there. | 10 things i hate about you | 1999 |
| L670 | 142.0 | 0.0 | m0 | u5 | KAT | When you were gone last year -- where were you? | 10 things i hate about you | 1999 |
| L671 | 142.0 | 1.0 | m0 | u9 | PATRICK | Busy | 10 things i hate about you | 1999 |
| L672 | 142.0 | 2.0 | m0 | u5 | KAT | Were you in jail? | 10 things i hate about you | 1999 |
| L673 | 142.0 | 3.0 | m0 | u9 | PATRICK | Maybe. | 10 things i hate about you | 1999 |
| L674 | 142.0 | 4.0 | m0 | u5 | KAT | No, you weren't | 10 things i hate about you | 1999 |
| L675 | 142.0 | 5.0 | m0 | u9 | PATRICK | Then why'd you ask? | 10 things i hate about you | 1999 |
| L676 | 142.0 | 6.0 | m0 | u5 | KAT | Why'd you lie? | 10 things i hate about you | 1999 |
| L677 | 143.0 | 0.0 | m0 | u5 | KAT | I should do this. | 10 things i hate about you | 1999 |
| L678 | 143.0 | 1.0 | m0 | u9 | PATRICK | Do what? | 10 things i hate about you | 1999 |
| L679 | 143.0 | 2.0 | m0 | u5 | KAT | This. | 10 things i hate about you | 1999 |
| L680 | 144.0 | 0.0 | m0 | u9 | PATRICK | Start a band? | 10 things i hate about you | 1999 |
| L681 | 144.0 | 1.0 | m0 | u5 | KAT | My father wouldn't approve of that that | 10 things i hate about you | 1999 |
| L682 | 144.0 | 2.0 | m0 | u9 | PATRICK | You don't strike me as the type that would ask permission. | 10 things i hate about you | 1999 |
| L683 | 145.0 | 0.0 | m0 | u5 | KAT | Oh, so now you think you know me? | 10 things i hate about you | 1999 |
| L684 | 145.0 | 1.0 | m0 | u9 | PATRICK | I'm gettin' there | 10 things i hate about you | 1999 |
| L686 | 146.0 | 0.0 | m0 | u9 | PATRICK | So what ' s up with your dad? He a pain in the ass? | 10 things i hate about you | 1999 |
| L687 | 146.0 | 1.0 | m0 | u5 | KAT | He just wants me to be someone I'm not. | 10 things i hate about you | 1999 |
| L688 | 146.0 | 2.0 | m0 | u9 | PATRICK | Who? | 10 things i hate about you | 1999 |
| L689 | 146.0 | 3.0 | m0 | u5 | KAT | BIANCA | 10 things i hate about you | 1999 |
| L690 | 146.0 | 4.0 | m0 | u9 | PATRICK | No offense, but you're sister is without. I know everyone likes her and all, but ... | 10 things i hate about you | 1999 |
| L764 | 147.0 | 0.0 | m0 | u9 | PATRICK | Excuse me, have you seen The Feminine Mystique? I lost my copy. | 10 things i hate about you | 1999 |
| L765 | 147.0 | 1.0 | m0 | u5 | KAT | What are you doing here? | 10 things i hate about you | 1999 |
| L766 | 147.0 | 2.0 | m0 | u9 | PATRICK | I heard there was a poetry reading. | 10 things i hate about you | 1999 |
| L767 | 147.0 | 3.0 | m0 | u5 | KAT | You 're so -- | 10 things i hate about you | 1999 |
| L768 | 147.0 | 4.0 | m0 | u9 | PATRICK | Pleasant? | 10 things i hate about you | 1999 |
| L769 | 148.0 | 0.0 | m0 | u9 | PATRICK | Wholesome. | 10 things i hate about you | 1999 |
| L770 | 148.0 | 1.0 | m0 | u5 | KAT | Unwelcome. | 10 things i hate about you | 1999 |
| L771 | 148.0 | 2.0 | m0 | u9 | PATRICK | Unwelcome? I guess someone still has her panties in a twist. | 10 things i hate about you | 1999 |
| L772 | 148.0 | 3.0 | m0 | u5 | KAT | Don't for one minute think that you had any effect whatsoever on my panties. | 10 things i hate about you | 1999 |
| L773 | 148.0 | 4.0 | m0 | u9 | PATRICK | So what did I have an effect on ? | 10 things i hate about you | 1999 |
| L774 | 148.0 | 5.0 | m0 | u5 | KAT | Other than my upchuck reflex? Nothing. | 10 things i hate about you | 1999 |
| L798 | 149.0 | 0.0 | m0 | u5 | KAT | He left! I sprung the dickhead and he cruised on me. | 10 things i hate about you | 1999 |
| L799 | 149.0 | 1.0 | m0 | u9 | PATRICK | Look up, sunshine | 10 things i hate about you | 1999 |
| L800 | 150.0 | 0.0 | m0 | u9 | PATRICK | I guess I never told you I'm afraid of heights. | 10 things i hate about you | 1999 |
| L801 | 150.0 | 1.0 | m0 | u5 | KAT | C'mon. It's not that bad | 10 things i hate about you | 1999 |
| L802 | 150.0 | 2.0 | m0 | u9 | PATRICK | Try lookin' at it from this angle | 10 things i hate about you | 1999 |
| L803 | 151.0 | 0.0 | m0 | u5 | KAT | Put your right foot there -- | 10 things i hate about you | 1999 |
| L804 | 151.0 | 1.0 | m0 | u9 | PATRICK | Forget it. I'm stayin'. | 10 things i hate about you | 1999 |
| L805 | 151.0 | 2.0 | m0 | u5 | KAT | You want me to climb up and show you how to get down? | 10 things i hate about you | 1999 |
| L806 | 151.0 | 3.0 | m0 | u9 | PATRICK | Maybe. | 10 things i hate about you | 1999 |
| L808 | 152.0 | 0.0 | m0 | u5 | KAT | The Partridge Family? | 10 things i hate about you | 1999 |
| L809 | 152.0 | 1.0 | m0 | u9 | PATRICK | I figured it had to be something ridiculous to win your respect. And piss you off. | 10 things i hate about you | 1999 |
| L810 | 152.0 | 2.0 | m0 | u5 | KAT | Good call. | 10 things i hate about you | 1999 |
| L811 | 152.0 | 3.0 | m0 | u9 | PATRICK | So how'd you get Chapin to look the other way? | 10 things i hate about you | 1999 |
| L812 | 152.0 | 4.0 | m0 | u5 | KAT | I dazzled him with my wit | 10 things i hate about you | 1999 |
| L813 | 153.0 | 0.0 | m0 | u9 | PATRICK | A soft side? Who knew? | 10 things i hate about you | 1999 |
| L814 | 153.0 | 1.0 | m0 | u5 | KAT | Yeah, well, don't let it get out | 10 things i hate about you | 1999 |
| L815 | 153.0 | 2.0 | m0 | u9 | PATRICK | So what's your excuse? | 10 things i hate about you | 1999 |
| L816 | 153.0 | 3.0 | m0 | u5 | KAT | Acting the way we do. | 10 things i hate about you | 1999 |
| L817 | 153.0 | 4.0 | m0 | u9 | PATRICK | Yes | 10 things i hate about you | 1999 |
| L818 | 153.0 | 5.0 | m0 | u5 | KAT | I don't like to do what people expect. Then they expect it all the time and they get disappointed when you change. | 10 things i hate about you | 1999 |
| L819 | 153.0 | 6.0 | m0 | u9 | PATRICK | So if you disappoint them from the start, you're covered? | 10 things i hate about you | 1999 |
| L820 | 153.0 | 7.0 | m0 | u5 | KAT | Something like that | 10 things i hate about you | 1999 |
| L821 | 153.0 | 8.0 | m0 | u9 | PATRICK | Then you screwed up | 10 things i hate about you | 1999 |
| L822 | 153.0 | 9.0 | m0 | u5 | KAT | How? | 10 things i hate about you | 1999 |
| L823 | 153.0 | 10.0 | m0 | u9 | PATRICK | You never disappointed me. | 10 things i hate about you | 1999 |
| L824 | 154.0 | 0.0 | m0 | u9 | PATRICK | You up for it? | 10 things i hate about you | 1999 |
| L825 | 154.0 | 1.0 | m0 | u5 | KAT | For. . . ? | 10 things i hate about you | 1999 |
| L828 | 155.0 | 0.0 | m0 | u5 | KAT | State trooper? | 10 things i hate about you | 1999 |
| L829 | 155.0 | 1.0 | m0 | u9 | PATRICK | Fallacy. | 10 things i hate about you | 1999 |
| L830 | 155.0 | 2.0 | m0 | u5 | KAT | The duck? | 10 things i hate about you | 1999 |
| L831 | 155.0 | 3.0 | m0 | u9 | PATRICK | Hearsay. | 10 things i hate about you | 1999 |
| L832 | 155.0 | 4.0 | m0 | u5 | KAT | I know the porn career's a lie. | 10 things i hate about you | 1999 |
| L834 | 156.0 | 0.0 | m0 | u5 | KAT | Tell me something true. | 10 things i hate about you | 1999 |
| L835 | 156.0 | 1.0 | m0 | u9 | PATRICK | I hate peas. | 10 things i hate about you | 1999 |
| L836 | 156.0 | 2.0 | m0 | u5 | KAT | No -- something real. Something no one else knows. | 10 things i hate about you | 1999 |
| L837 | 156.0 | 3.0 | m0 | u9 | PATRICK | You're sweet. And sexy. And completely hot for me. | 10 things i hate about you | 1999 |
| L838 | 156.0 | 4.0 | m0 | u5 | KAT | What? | 10 things i hate about you | 1999 |
| L839 | 156.0 | 5.0 | m0 | u9 | PATRICK | No one else knows | 10 things i hate about you | 1999 |
| L840 | 156.0 | 6.0 | m0 | u5 | KAT | You're amazingly self-assured. Has anyone ever told you that? | 10 things i hate about you | 1999 |
| L841 | 156.0 | 7.0 | m0 | u9 | PATRICK | Go to the prom with me | 10 things i hate about you | 1999 |
| L842 | 157.0 | 0.0 | m0 | u5 | KAT | Is that a request or a command? | 10 things i hate about you | 1999 |
| L843 | 157.0 | 1.0 | m0 | u9 | PATRICK | You know what I mean | 10 things i hate about you | 1999 |
| L844 | 157.0 | 2.0 | m0 | u5 | KAT | No. | 10 things i hate about you | 1999 |
| L845 | 157.0 | 3.0 | m0 | u9 | PATRICK | No what? | 10 things i hate about you | 1999 |
| L846 | 157.0 | 4.0 | m0 | u5 | KAT | No, I won't go with you | 10 things i hate about you | 1999 |
| L847 | 157.0 | 5.0 | m0 | u9 | PATRICK | Why not? | 10 things i hate about you | 1999 |
| L848 | 157.0 | 6.0 | m0 | u5 | KAT | Because I don't want to. It's a stupid tradition. | 10 things i hate about you | 1999 |
| L852 | 158.0 | 0.0 | m0 | u5 | KAT | Create a little drama? Start a new rumor? What? | 10 things i hate about you | 1999 |
| L853 | 158.0 | 1.0 | m0 | u9 | PATRICK | So I have to have a motive to be with you? | 10 things i hate about you | 1999 |
| L854 | 158.0 | 2.0 | m0 | u5 | KAT | You tell me. | 10 things i hate about you | 1999 |
| L855 | 158.0 | 3.0 | m0 | u9 | PATRICK | You need therapy. Has anyone ever told you that? | 10 things i hate about you | 1999 |
| L856 | 158.0 | 4.0 | m0 | u5 | KAT | Answer the question, Patrick | 10 things i hate about you | 1999 |
| L857 | 158.0 | 5.0 | m0 | u9 | PATRICK | Nothing! There's nothing in it for me. Just the pleasure of your company. | 10 things i hate about you | 1999 |
| L936 | 159.0 | 0.0 | m0 | u5 | KAT | How'd you get a tux at the last minute? | 10 things i hate about you | 1999 |
| L937 | 159.0 | 1.0 | m0 | u9 | PATRICK | It's Scurvy's. His date got convicted. Where'd you get the dress? | 10 things i hate about you | 1999 |
| L938 | 159.0 | 2.0 | m0 | u5 | KAT | It's just something I had. You know | 10 things i hate about you | 1999 |
| L939 | 159.0 | 3.0 | m0 | u9 | PATRICK | Oh huh | 10 things i hate about you | 1999 |
| L940 | 159.0 | 4.0 | m0 | u5 | KAT | Look, I'm -- sorry -- that I questioned your motives. I was wrong. | 10 things i hate about you | 1999 |
| L957 | 160.0 | 0.0 | m0 | u9 | PATRICK | My grandmother's . | 10 things i hate about you | 1999 |
| L958 | 160.0 | 1.0 | m0 | u5 | KAT | What? | 10 things i hate about you | 1999 |
| L959 | 160.0 | 2.0 | m0 | u9 | PATRICK | That's where I was last year. She'd never lived alone -- my grandfather died -- I stayed with her. I wasn't in jail, I don't know Marilyn Manson, and I've never slept with a Spice Girl. I spent a year sitting next to my grandma on the couch watching Wheel of Fortune. End of story. | 10 things i hate about you | 1999 |
| L960 | 161.0 | 0.0 | m0 | u5 | KAT | That ' s completely adorable! | 10 things i hate about you | 1999 |
| L961 | 161.0 | 1.0 | m0 | u9 | PATRICK | It gets worse -- you still have your freshman yearbook? | 10 things i hate about you | 1999 |
| L973 | 162.0 | 0.0 | m0 | u9 | PATRICK | Wait I... | 10 things i hate about you | 1999 |
| L974 | 162.0 | 1.0 | m0 | u5 | KAT | You were paid to take me out! By -- the one person I truly hate. I knew it was a set-up! | 10 things i hate about you | 1999 |
| L975 | 162.0 | 2.0 | m0 | u9 | PATRICK | It wasn't like that. | 10 things i hate about you | 1999 |
| L976 | 162.0 | 3.0 | m0 | u5 | KAT | Really? What was it like? A down payment now, then a bonus for sleeping with me? | 10 things i hate about you | 1999 |
| L977 | 162.0 | 4.0 | m0 | u9 | PATRICK | I didn't care about the money. | 10 things i hate about you | 1999 |
| L1031 | 163.0 | 0.0 | m0 | u5 | KAT | A Fender Strat. You bought this? | 10 things i hate about you | 1999 |
| L1032 | 163.0 | 1.0 | m0 | u9 | PATRICK | I thought you could use it. When you start your band. | 10 things i hate about you | 1999 |
| L1033 | 164.0 | 0.0 | m0 | u9 | PATRICK | Besides, I had some extra cash. Some asshole paid me to take out a really great girl. | 10 things i hate about you | 1999 |
| L1034 | 164.0 | 1.0 | m0 | u5 | KAT | Is that right? | 10 things i hate about you | 1999 |
| L1035 | 164.0 | 2.0 | m0 | u9 | PATRICK | Yeah, but then I fucked up. I fell for her. | 10 things i hate about you | 1999 |
| L1040 | 165.0 | 0.0 | m0 | u5 | KAT | Why is my veggie burger the only burnt object on this grill? | 10 things i hate about you | 1999 |
| L1041 | 165.0 | 1.0 | m0 | u9 | PATRICK | Because I like to torture you. | 10 things i hate about you | 1999 |
| L1042 | 165.0 | 2.0 | m0 | u5 | KAT | Oh, Bianca? Can you get me my freshman yearbook? | 10 things i hate about you | 1999 |
| L1043 | 165.0 | 3.0 | m0 | u9 | PATRICK | Don ' t you even dare. . . | 10 things i hate about you | 1999 |
| L172 | 166.0 | 0.0 | m0 | u5 | KAT | I know. | 10 things i hate about you | 1999 |
| L173 | 166.0 | 1.0 | m0 | u11 | WALTER | I thought we decided you were going to school here. At U of 0. | 10 things i hate about you | 1999 |
| L174 | 166.0 | 2.0 | m0 | u5 | KAT | You decided. | 10 things i hate about you | 1999 |
| L184 | 167.0 | 0.0 | m0 | u5 | KAT | This from someone whose diary is devoted to favorite grooming tips? | 10 things i hate about you | 1999 |
| L185 | 167.0 | 1.0 | m0 | u11 | WALTER | Enough! | 10 things i hate about you | 1999 |
| L338 | 168.0 | 0.0 | m0 | u11 | WALTER | My insurance does not cover PMS | 10 things i hate about you | 1999 |
| L339 | 168.0 | 1.0 | m0 | u5 | KAT | Then tell them I had a seizure. | 10 things i hate about you | 1999 |
| L340 | 168.0 | 2.0 | m0 | u11 | WALTER | Is this about Sarah Lawrence? You punishing me? | 10 things i hate about you | 1999 |
| L341 | 168.0 | 3.0 | m0 | u5 | KAT | I thought you were punishing me. | 10 things i hate about you | 1999 |
| L342 | 168.0 | 4.0 | m0 | u11 | WALTER | Why can't we agree on this? | 10 things i hate about you | 1999 |
| L343 | 168.0 | 5.0 | m0 | u5 | KAT | Because you're making decisions for me. | 10 things i hate about you | 1999 |
| L344 | 168.0 | 6.0 | m0 | u11 | WALTER | As a parent, that's my right | 10 things i hate about you | 1999 |
| L345 | 168.0 | 7.0 | m0 | u5 | KAT | So what I want doesn't matter? | 10 things i hate about you | 1999 |
| L346 | 168.0 | 8.0 | m0 | u11 | WALTER | You're eighteen. You don't know what you want. You won't know until you're forty-five and you don't have it. | 10 things i hate about you | 1999 |
| L347 | 168.0 | 9.0 | m0 | u5 | KAT | I want to go to an East Coast school! I want you to trust me to make my own choices. I want -- | 10 things i hate about you | 1999 |
| L986 | 169.0 | 0.0 | m0 | u11 | WALTER | Was that your sister? | 10 things i hate about you | 1999 |
| L987 | 169.0 | 1.0 | m0 | u5 | KAT | Yeah. She left with some bikers Big ones. Full of sperm. | 10 things i hate about you | 1999 |
| L988 | 169.0 | 2.0 | m0 | u11 | WALTER | Funny. | 10 things i hate about you | 1999 |
| L989 | 170.0 | 0.0 | m0 | u11 | WALTER | I don't understand the allure of dehydrated food. Is this something I should be hip to? | 10 things i hate about you | 1999 |
| L990 | 170.0 | 1.0 | m0 | u5 | KAT | No, Daddy. | 10 things i hate about you | 1999 |
| L991 | 170.0 | 2.0 | m0 | u11 | WALTER | So tell me about this dance. Was it fun? | 10 things i hate about you | 1999 |
| L992 | 170.0 | 3.0 | m0 | u5 | KAT | Parts of it. | 10 things i hate about you | 1999 |
| L993 | 170.0 | 4.0 | m0 | u11 | WALTER | Which parts? | 10 things i hate about you | 1999 |
| L994 | 170.0 | 5.0 | m0 | u5 | KAT | The part where Bianca beat the hell out of some guy. | 10 things i hate about you | 1999 |
| L995 | 170.0 | 6.0 | m0 | u11 | WALTER | Bianca did what? | 10 things i hate about you | 1999 |
| L996 | 170.0 | 7.0 | m0 | u5 | KAT | What's the matter? Upset that I rubbed off on her? | 10 things i hate about you | 1999 |
| L997 | 170.0 | 8.0 | m0 | u11 | WALTER | No -- impressed. | 10 things i hate about you | 1999 |
| L998 | 171.0 | 0.0 | m0 | u11 | WALTER | You know, fathers don't like to admit that their daughters are capable of running their own lives. It means we've become spectators. Bianca still lets me play a few innings. You've had me on the bleachers for years. When you go to Sarah Lawrence, I won't even be able to watch the game. | 10 things i hate about you | 1999 |
| L999 | 171.0 | 1.0 | m0 | u5 | KAT | When I go? | 10 things i hate about you | 1999 |
| L1000 | 171.0 | 2.0 | m0 | u11 | WALTER | Oh, Christ. Don't tell me you've changed your mind. I already sent 'em a check. | 10 things i hate about you | 1999 |
| L103 | 172.0 | 0.0 | m0 | u8 | MISS PERKY | Katarina Stratford. My, my. You've been terrorizing Ms. Blaise again. | 10 things i hate about you | 1999 |
| L104 | 172.0 | 1.0 | m0 | u5 | KAT | Expressing my opinion is not a terrorist action. | 10 things i hate about you | 1999 |
| L105 | 172.0 | 2.0 | m0 | u8 | MISS PERKY | Well, yes, compared to your other choices of expression this year, today's events are quite mild. By the way, Bobby Rictor's gonad retrieval operation went quite well, in case you're interested. | 10 things i hate about you | 1999 |
| L106 | 172.0 | 3.0 | m0 | u5 | KAT | I still maintain that he kicked himself in the balls. I was merely a spectator. | 10 things i hate about you | 1999 |
| L107 | 172.0 | 4.0 | m0 | u8 | MISS PERKY | The point is Kat -- people perceive you as somewhat ... | 10 things i hate about you | 1999 |
| L108 | 173.0 | 0.0 | m0 | u5 | KAT | Tempestuous? | 10 things i hate about you | 1999 |
| L109 | 173.0 | 1.0 | m0 | u8 | MISS PERKY | No ... I believe "heinous bitch" is the term used most often. | 10 things i hate about you | 1999 |
| L1017 | 174.0 | 0.0 | m0 | u5 | KAT | Am I supposed to feel better? Like, right now? Or do I have some time to think about it? | 10 things i hate about you | 1999 |
| L1018 | 174.0 | 1.0 | m0 | u8 | MISS PERKY | Just smack her now. | 10 things i hate about you | 1999 |
| L728 | 175.0 | 0.0 | m0 | u7 | MICHAEL | Hey there. Tired of breathing? | 10 things i hate about you | 1999 |
| L729 | 175.0 | 1.0 | m0 | u6 | MANDELLA | Hi. | 10 things i hate about you | 1999 |
| L730 | 175.0 | 2.0 | m0 | u7 | MICHAEL | Cool pictures. You a fan? | 10 things i hate about you | 1999 |
| L731 | 175.0 | 3.0 | m0 | u6 | MANDELLA | Yeah. I guess. | 10 things i hate about you | 1999 |
| L732 | 176.0 | 0.0 | m0 | u6 | MANDELLA | You think? | 10 things i hate about you | 1999 |
| L733 | 176.0 | 1.0 | m0 | u7 | MICHAEL | Oh yeah. | 10 things i hate about you | 1999 |
| L735 | 177.0 | 0.0 | m0 | u7 | MICHAEL | Macbeth, right? | 10 things i hate about you | 1999 |
| L736 | 177.0 | 1.0 | m0 | u6 | MANDELLA | Right. | 10 things i hate about you | 1999 |
| L737 | 177.0 | 2.0 | m0 | u7 | MICHAEL | Kat a fan, too? | 10 things i hate about you | 1999 |
| L738 | 177.0 | 3.0 | m0 | u6 | MANDELLA | Yeah... | 10 things i hate about you | 1999 |
| L371 | 178.0 | 0.0 | m0 | u9 | PATRICK | Say it | 10 things i hate about you | 1999 |
| L372 | 178.0 | 1.0 | m0 | u7 | MICHAEL | What? | 10 things i hate about you | 1999 |
| L373 | 178.0 | 2.0 | m0 | u9 | PATRICK | Whatever the hell it is you're standin' there waitin' to say. | 10 things i hate about you | 1999 |
| L375 | 179.0 | 0.0 | m0 | u9 | PATRICK | What plan? | 10 things i hate about you | 1999 |
| L376 | 179.0 | 1.0 | m0 | u7 | MICHAEL | The situation is, my man Cameron here has a major jones for Bianca Stratford. | 10 things i hate about you | 1999 |
| L377 | 179.0 | 2.0 | m0 | u9 | PATRICK | What is it with this chick? She have three tits? | 10 things i hate about you | 1999 |
| L378 | 180.0 | 0.0 | m0 | u7 | MICHAEL | I think I speak correctly when I say that Cameron's love is pure. Purer than say -- Joey Dorsey's. | 10 things i hate about you | 1999 |
| L379 | 180.0 | 1.0 | m0 | u9 | PATRICK | Dorsey can plow whoever he wants. I'm just in this for the cash. | 10 things i hate about you | 1999 |
| L380 | 181.0 | 0.0 | m0 | u7 | MICHAEL | That's where we can help you. With Kat. | 10 things i hate about you | 1999 |
| L381 | 181.0 | 1.0 | m0 | u9 | PATRICK | So Dorsey can get the girl? | 10 things i hate about you | 1999 |
| L382 | 181.0 | 2.0 | m0 | u7 | MICHAEL | Patrick, Pat, you're not looking at the big picture. Joey's just a pawn. We set this whole thing up so Cameron can get the girl. | 10 things i hate about you | 1999 |
| L383 | 182.0 | 0.0 | m0 | u9 | PATRICK | You two are gonna help me tame the wild beast? | 10 things i hate about you | 1999 |
| L384 | 182.0 | 1.0 | m0 | u7 | MICHAEL | We're your guys. | 10 things i hate about you | 1999 |
| L414 | 183.0 | 0.0 | m0 | u9 | PATRICK | What?! | 10 things i hate about you | 1999 |
| L415 | 183.0 | 1.0 | m0 | u7 | MICHAEL | Good enough. | 10 things i hate about you | 1999 |
| L420 | 184.0 | 0.0 | m0 | u9 | PATRICK | Are you telling me I'm a - "non-smoker"? | 10 things i hate about you | 1999 |
| L421 | 184.0 | 1.0 | m0 | u7 | MICHAEL | Just for now. | 10 things i hate about you | 1999 |
| L428 | 185.0 | 0.0 | m0 | u7 | MICHAEL | Ever been to Club Skunk? | 10 things i hate about you | 1999 |
| L429 | 185.0 | 1.0 | m0 | u9 | PATRICK | Yeah. | 10 things i hate about you | 1999 |
| L436 | 186.0 | 0.0 | m0 | u7 | MICHAEL | I prefer to think of it simply as an alternative to what the law allows. | 10 things i hate about you | 1999 |
| L437 | 186.0 | 1.0 | m0 | u9 | PATRICK | I'm likin' you guys better | 10 things i hate about you | 1999 |
| L723 | 187.0 | 0.0 | m0 | u7 | MICHAEL | So you got cozy with she who stings? | 10 things i hate about you | 1999 |
| L724 | 187.0 | 1.0 | m0 | u9 | PATRICK | No - I've got a sweet-payin' job that I'm about to lose. | 10 things i hate about you | 1999 |
| L775 | 188.0 | 0.0 | m0 | u9 | PATRICK | You were right. She's still pissed. | 10 things i hate about you | 1999 |
| L776 | 188.0 | 1.0 | m0 | u7 | MICHAEL | Sweet love, renew thy force! | 10 things i hate about you | 1999 |
| L777 | 188.0 | 2.0 | m0 | u9 | PATRICK | Man -- don't say shit like that to me. People can hear you. | 10 things i hate about you | 1999 |
| L59 | 189.0 | 0.0 | m0 | u9 | PATRICK | I missed you. | 10 things i hate about you | 1999 |
| L60 | 189.0 | 1.0 | m0 | u8 | MISS PERKY | It says here you exposed yourself to a group of freshmen girls. | 10 things i hate about you | 1999 |
| L61 | 189.0 | 2.0 | m0 | u9 | PATRICK | It was a bratwurst. I was eating lunch. | 10 things i hate about you | 1999 |
| L62 | 189.0 | 3.0 | m0 | u8 | MISS PERKY | With the teeth of your zipper? | 10 things i hate about you | 1999 |
| L257 | 190.0 | 0.0 | m0 | u8 | MISS PERKY | I don't understand, Patrick. You haven't done anything asinine this week. Are you not feeling well? | 10 things i hate about you | 1999 |
| L258 | 190.0 | 1.0 | m0 | u9 | PATRICK | Touch of the flu. | 10 things i hate about you | 1999 |
| L259 | 190.0 | 2.0 | m0 | u8 | MISS PERKY | I'm at a loss, then. What should we talk about? Your year of absence? | 10 things i hate about you | 1999 |
| L261 | 191.0 | 0.0 | m0 | u8 | MISS PERKY | Why don't we discuss your driving need to be a hemorrhoid? | 10 things i hate about you | 1999 |
| L262 | 191.0 | 1.0 | m0 | u9 | PATRICK | What's to discuss? | 10 things i hate about you | 1999 |
| L263 | 191.0 | 2.0 | m0 | u8 | MISS PERKY | You weren't abused, you aren't stupid, and as far as I can tell, you're only slightly psychotic -- so why is it that you're such a fuck-up? | 10 things i hate about you | 1999 |
| L264 | 191.0 | 3.0 | m0 | u9 | PATRICK | Well, you know -- there's the prestige of the job title... and the benefits package is pretty good... | 10 things i hate about you | 1999 |
| L511 | 192.0 | 0.0 | m0 | u8 | MISS PERKY | You're completely demented. | 10 things i hate about you | 1999 |
| L512 | 192.0 | 1.0 | m0 | u9 | PATRICK | See you next week! | 10 things i hate about you | 1999 |
| L161 | 193.0 | 0.0 | m0 | u10 | SHARON | In the microwave. | 10 things i hate about you | 1999 |
| L162 | 193.0 | 1.0 | m0 | u11 | WALTER | Make anyone cry today? | 10 things i hate about you | 1999 |
| L170 | 194.0 | 0.0 | m0 | u10 | SHARON | What's a synonym for throbbing? | 10 things i hate about you | 1999 |
| L171 | 194.0 | 1.0 | m0 | u11 | WALTER | Sarah Lawrence is on the other side of the country. | 10 things i hate about you | 1999 |
| L191 | 195.0 | 0.0 | m0 | u11 | WALTER | Jesus! Can a man even grab a sandwich before you women start dilating? | 10 things i hate about you | 1999 |
| L192 | 195.0 | 1.0 | m0 | u10 | SHARON | Tumescent! | 10 things i hate about you | 1999 |
| L193 | 195.0 | 2.0 | m0 | u11 | WALTER | You're not helping. | 10 things i hate about you | 1999 |
| L876 | 196.0 | 0.0 | m0 | u10 | SHARON | Would you rather be ravished by a pirate or a British rear admiral? | 10 things i hate about you | 1999 |
| L877 | 196.0 | 1.0 | m0 | u11 | WALTER | Pirate -- no question. | 10 things i hate about you | 1999 |
| L886 | 197.0 | 0.0 | m0 | u10 | SHARON | They'll dance, they'll kiss, they'll come home. Let her go. | 10 things i hate about you | 1999 |
| L887 | 197.0 | 1.0 | m0 | u11 | WALTER | Kissing? Is that what you think happens? Kissing isn't what keeps me up to my elbows in placenta all day. | 10 things i hate about you | 1999 |
| L917 | 198.0 | 0.0 | m0 | u11 | WALTER | What do you wanna watch? We've got crap, crap, crap or crap | 10 things i hate about you | 1999 |
| L918 | 198.0 | 1.0 | m0 | u10 | SHARON | Dr. Ruth? | 10 things i hate about you | 1999 |
| L926 | 199.0 | 0.0 | m0 | u10 | SHARON | Have a great time, honey! | 10 things i hate about you | 1999 |
| L927 | 199.0 | 1.0 | m0 | u11 | WALTER | But -- who -- what --? | 10 things i hate about you | 1999 |
| L929 | 200.0 | 0.0 | m0 | u11 | WALTER | What just happened? | 10 things i hate about you | 1999 |
| L930 | 200.0 | 1.0 | m0 | u10 | SHARON | Your daughters went to the prom. | 10 things i hate about you | 1999 |
| L931 | 200.0 | 2.0 | m0 | u11 | WALTER | Did I have anything to say about it? | 10 things i hate about you | 1999 |
| L932 | 200.0 | 3.0 | m0 | u10 | SHARON | Absolutely not. | 10 things i hate about you | 1999 |
| L933 | 200.0 | 4.0 | m0 | u11 | WALTER | That ' s what I thought | 10 things i hate about you | 1999 |
| L2170 | 201.0 | 0.0 | m1 | u12 | ALONSO | I never seen heat like this! Not even in Las Minas! | 1492: conquest of paradise | 1992 |
| L2171 | 201.0 | 1.0 | m1 | u23 | SAILOR | The water's going putrid in the barrels. | 1492: conquest of paradise | 1992 |
| L2172 | 201.0 | 2.0 | m1 | u12 | ALONSO | You'll be drinking your own piss... For the glory of Spain... and Admiral Colon...! Bastard! | 1492: conquest of paradise | 1992 |
| L2173 | 202.0 | 0.0 | m1 | u12 | ALONSO | What are you listening to, chicken ass? | 1492: conquest of paradise | 1992 |
| L2174 | 202.0 | 1.0 | m1 | u23 | SAILOR | Ah, leave him alone. He's doing no harm. | 1492: conquest of paradise | 1992 |
| L2175 | 202.0 | 2.0 | m1 | u12 | ALONSO | With a face like that? I don't want you looking at me. You hear? | 1492: conquest of paradise | 1992 |
| L2176 | 203.0 | 0.0 | m1 | u12 | ALONSO | He's the devil's child... | 1492: conquest of paradise | 1992 |
| L2177 | 203.0 | 1.0 | m1 | u23 | SAILOR | We'll all go crazy... | 1492: conquest of paradise | 1992 |
| L2179 | 204.0 | 0.0 | m1 | u12 | ALONSO | We should have seen land. | 1492: conquest of paradise | 1992 |
| L2180 | 204.0 | 1.0 | m1 | u23 | SAILOR | We left three weeks ago, Alonso. Can't be that near. | 1492: conquest of paradise | 1992 |
| L2181 | 204.0 | 2.0 | m1 | u12 | ALONSO | Can't be that far, I say. Also, I don't like the smell of the sea around here. Smells like a cunt. Bad sign... | 1492: conquest of paradise | 1992 |
| L1989 | 205.0 | 0.0 | m1 | u13 | AROJAZ | You say Asia can be found by sailing west? | 1492: conquest of paradise | 1992 |
| L1990 | 205.0 | 1.0 | m1 | u16 | COLUMBUS | Yes, your Eminence. The voyage should not take more than six or seven weeks. | 1492: conquest of paradise | 1992 |
| L1991 | 205.0 | 2.0 | m1 | u13 | AROJAZ | Unfortunately, Don Colon, that is precisely where our opinions differ... Are you familiar with the work of Aristotle? Erathostene? Ptolemeus? | 1492: conquest of paradise | 1992 |
| L1992 | 205.0 | 3.0 | m1 | u16 | COLUMBUS | I am, Your Eminence | 1492: conquest of paradise | 1992 |
| L1993 | 205.0 | 4.0 | m1 | u13 | AROJAZ | Then you cannot ignore that according to their calculations, the circumference of the Earth is approximately... 22,000 leagues or more. Which makes the ocean... uncrossable. | 1492: conquest of paradise | 1992 |
| L2005 | 206.0 | 0.0 | m1 | u13 | AROJAZ | Senor Colon, an experienced captain such as yourself will understand our concern with the crew. I am not willing to have on my conscience the loss of men who would have relied upon our judgment. | 1492: conquest of paradise | 1992 |
| L2006 | 206.0 | 1.0 | m1 | u16 | COLUMBUS | Excellency, you are right. | 1492: conquest of paradise | 1992 |
| L2010 | 207.0 | 0.0 | m1 | u16 | COLUMBUS | Your Eminence, there is only one way to settle the matter. And that is to make the journey. I am ready to risk my life to prove it possible. | 1492: conquest of paradise | 1992 |
| L2011 | 207.0 | 1.0 | m1 | u13 | AROJAZ | Your life, and that of others! | 1492: conquest of paradise | 1992 |
| L2012 | 207.0 | 2.0 | m1 | u16 | COLUMBUS | If they agree to follow me, yes. | 1492: conquest of paradise | 1992 |
| L2014 | 208.0 | 0.0 | m1 | u16 | COLUMBUS | Trade, Your Excellency. According to Marco Polo, the Kingdom of China is one of the richest of the world. Even the meanest buildings are roofed with gold. | 1492: conquest of paradise | 1992 |
| L2015 | 208.0 | 1.0 | m1 | u13 | AROJAZ | Is that all that interests you? Gold? | 1492: conquest of paradise | 1992 |
| L2016 | 208.0 | 2.0 | m1 | u16 | COLUMBUS | No. The Portuguese have already discovered black-skinned people. I, too, will find other populations -- and bring them to the word of God. | 1492: conquest of paradise | 1992 |
| L2019 | 209.0 | 0.0 | m1 | u13 | AROJAZ | If God intended our proximity to Asia, do you believe he would have waited for you to show it to the world? | 1492: conquest of paradise | 1992 |
| L2020 | 209.0 | 1.0 | m1 | u16 | COLUMBUS | Did He not choose a carpenter's son to reveal Himself to the world? | 1492: conquest of paradise | 1992 |
| L2022 | 210.0 | 0.0 | m1 | u13 | AROJAZ | Don't you realize your words could be considered heretical? | 1492: conquest of paradise | 1992 |
| L2023 | 210.0 | 1.0 | m1 | u16 | COLUMBUS | Blind faith is what I consider heresy! | 1492: conquest of paradise | 1992 |
| L2024 | 211.0 | 0.0 | m1 | u16 | COLUMBUS | Asia can be found to the west -- and I will prove it. | 1492: conquest of paradise | 1992 |
| L2025 | 211.0 | 1.0 | m1 | u13 | AROJAZ | IF-GOD-WILLS-IT! | 1492: conquest of paradise | 1992 |
| L2027 | 212.0 | 0.0 | m1 | u24 | SANCHEZ | The State has some reason to be interested in this man's proposition, Your Eminence... | 1492: conquest of paradise | 1992 |
| L2028 | 212.0 | 1.0 | m1 | u13 | AROJAZ | The Judgment is ours! | 1492: conquest of paradise | 1992 |
| L2029 | 212.0 | 2.0 | m1 | u24 | SANCHEZ | Naturally. But I would really deplore the loss of such a potential opportunity for Spain for a... dispute over a point of geography. | 1492: conquest of paradise | 1992 |
| L2030 | 213.0 | 0.0 | m1 | u13 | AROJAZ | He is a mercenary! Did he not already try to convince the King of Portugal of his absurd notions? | 1492: conquest of paradise | 1992 |
| L2031 | 213.0 | 1.0 | m1 | u24 | SANCHEZ | Indeed. The world is full of mercenaries -- and states often make use of them, when it benefits them. My only concern is the welfare and prosperity of Spain. | 1492: conquest of paradise | 1992 |
| L2274 | 214.0 | 0.0 | m1 | u13 | AROJAZ | It won't be easy to get rid of your prophet now, Don Sanchez. | 1492: conquest of paradise | 1992 |
| L2275 | 214.0 | 1.0 | m1 | u24 | SANCHEZ | On the contrary, Your Eminence. It seems to me the man is preparing his own cross. | 1492: conquest of paradise | 1992 |
| L2522 | 215.0 | 0.0 | m1 | u24 | SANCHEZ | You can see for yourself. | 1492: conquest of paradise | 1992 |
| L2523 | 215.0 | 1.0 | m1 | u13 | AROJAZ | What a tragedy... what a waste of a life... | 1492: conquest of paradise | 1992 |
| L2524 | 215.0 | 2.0 | m1 | u24 | SANCHEZ | A waste...? Let me tell you something, Arojaz. If your name, or mine, is ever remembered -- it will only be because of his. | 1492: conquest of paradise | 1992 |
| L1979 | 216.0 | 0.0 | m1 | u16 | COLUMBUS | I could be gone for years. | 1492: conquest of paradise | 1992 |
| L1980 | 216.0 | 1.0 | m1 | u14 | BEATRIX | I know. | 1492: conquest of paradise | 1992 |
| L1981 | 216.0 | 2.0 | m1 | u16 | COLUMBUS | I haven't given you much of a life. | 1492: conquest of paradise | 1992 |
| L1982 | 216.0 | 3.0 | m1 | u14 | BEATRIX | Well... that's true. I have a child by a man who won't marry me! Who's always leaving... | 1492: conquest of paradise | 1992 |
| L1983 | 216.0 | 4.0 | m1 | u16 | COLUMBUS | Are we going to argue? | 1492: conquest of paradise | 1992 |
| L1984 | 216.0 | 5.0 | m1 | u14 | BEATRIX | I'd love to argue with you sometimes. But you're never here! | 1492: conquest of paradise | 1992 |
| L1985 | 217.0 | 0.0 | m1 | u16 | COLUMBUS | Perhaps I was never meant to live with a woman... | 1492: conquest of paradise | 1992 |
| L1986 | 217.0 | 1.0 | m1 | u14 | BEATRIX | I find that hard to believe. | 1492: conquest of paradise | 1992 |
| L2118 | 218.0 | 0.0 | m1 | u16 | COLUMBUS | She said yes. | 1492: conquest of paradise | 1992 |
| L2119 | 218.0 | 1.0 | m1 | u14 | BEATRIX | Thank God... | 1492: conquest of paradise | 1992 |
| L2121 | 219.0 | 0.0 | m1 | u14 | BEATRIX | I'm not asking you to swear to anything. | 1492: conquest of paradise | 1992 |
| L2122 | 219.0 | 1.0 | m1 | u16 | COLUMBUS | I don't want you to wait for me. | 1492: conquest of paradise | 1992 |
| L2123 | 219.0 | 2.0 | m1 | u14 | BEATRIX | That's something you can't decide. | 1492: conquest of paradise | 1992 |
| L2318 | 220.0 | 0.0 | m1 | u16 | COLUMBUS | Beatrix, I want to ask you something. | 1492: conquest of paradise | 1992 |
| L2319 | 220.0 | 1.0 | m1 | u14 | BEATRIX | You don't usually ask. | 1492: conquest of paradise | 1992 |
| L2320 | 220.0 | 2.0 | m1 | u16 | COLUMBUS | I can arrange for the Queen to take Fernando and Diego into her service. | 1492: conquest of paradise | 1992 |
| L2471 | 221.0 | 0.0 | m1 | u16 | COLUMBUS | God... you're so beautiful! I can't believe no other man has ever taken you away from me... | 1492: conquest of paradise | 1992 |
| L2472 | 221.0 | 1.0 | m1 | u14 | BEATRIX | They tried... but I didn't let them. | 1492: conquest of paradise | 1992 |
| L2473 | 222.0 | 0.0 | m1 | u14 | BEATRIX | They took everything... | 1492: conquest of paradise | 1992 |
| L2474 | 222.0 | 1.0 | m1 | u16 | COLUMBUS | Not everything... Do you think I care? I'm a free man again. Riches don't make a man rich, they only make him busier... | 1492: conquest of paradise | 1992 |
| L2537 | 223.0 | 0.0 | m1 | u16 | COLUMBUS | Can't you stay with us a little? | 1492: conquest of paradise | 1992 |
| L2538 | 223.0 | 1.0 | m1 | u14 | BEATRIX | I am busy inside. | 1492: conquest of paradise | 1992 |
| L2539 | 224.0 | 0.0 | m1 | u14 | BEATRIX | What is it, now? Tell me... | 1492: conquest of paradise | 1992 |
| L2540 | 224.0 | 1.0 | m1 | u16 | COLUMBUS | I can't keep my eyes off you. I would like to catch up with all the moments I didn't spend with you. | 1492: conquest of paradise | 1992 |
| L2290 | 225.0 | 0.0 | m1 | u15 | BOBADILLA | I understand that you will soon be appointing Governors for the islands? Is it not so? | 1492: conquest of paradise | 1992 |
| L2291 | 225.0 | 1.0 | m1 | u16 | COLUMBUS | Forgive me, Don Bobadilla -- those positions have already been taken. | 1492: conquest of paradise | 1992 |
| L2292 | 225.0 | 2.0 | m1 | u15 | BOBADILLA | May I ask by whom? | 1492: conquest of paradise | 1992 |
| L2293 | 225.0 | 3.0 | m1 | u16 | COLUMBUS | Bartolome and Giacomo Colon. | 1492: conquest of paradise | 1992 |
| L2421 | 226.0 | 0.0 | m1 | u15 | BOBADILLA | Don Alonso de Bobadilla. | 1492: conquest of paradise | 1992 |
| L2422 | 226.0 | 1.0 | m1 | u16 | COLUMBUS | Yes... I remember... | 1492: conquest of paradise | 1992 |
| L2423 | 227.0 | 0.0 | m1 | u15 | BOBADILLA | My letters of appointment. | 1492: conquest of paradise | 1992 |
| L2424 | 227.0 | 1.0 | m1 | u16 | COLUMBUS | Appointment to what? | 1492: conquest of paradise | 1992 |
| L2425 | 227.0 | 2.0 | m1 | u15 | BOBADILLA | Viceroy of the West Indies. | 1492: conquest of paradise | 1992 |
| L2426 | 227.0 | 3.0 | m1 | u16 | COLUMBUS | Congratulations. Then I am free to search for the mainland. | 1492: conquest of paradise | 1992 |
| L2429 | 228.0 | 0.0 | m1 | u16 | COLUMBUS | How far from here? | 1492: conquest of paradise | 1992 |
| L2430 | 228.0 | 1.0 | m1 | u15 | BOBADILLA | I am not a seaman. But I heard it is no more than a week at sea. I hope you are not too disappointed. | 1492: conquest of paradise | 1992 |
| L2431 | 228.0 | 2.0 | m1 | u16 | COLUMBUS | How could I be? The mainland has been found. Exactly as I said it would. | 1492: conquest of paradise | 1992 |
| L2432 | 228.0 | 3.0 | m1 | u15 | BOBADILLA | I am afraid this is not the worst news. | 1492: conquest of paradise | 1992 |
| L2127 | 229.0 | 0.0 | m1 | u17 | FERNANDO | I want to go with you! | 1492: conquest of paradise | 1992 |
| L2128 | 229.0 | 1.0 | m1 | u16 | COLUMBUS | There'll be a time. | 1492: conquest of paradise | 1992 |
| L2129 | 229.0 | 2.0 | m1 | u17 | FERNANDO | You promise? Do you swear on St. Christopher...? | 1492: conquest of paradise | 1992 |
| L2130 | 230.0 | 0.0 | m1 | u17 | FERNANDO | Do you swear on all the Holy Saints in heaven? | 1492: conquest of paradise | 1992 |
| L2131 | 230.0 | 1.0 | m1 | u16 | COLUMBUS | Yes... Yes, I do... On all of them! | 1492: conquest of paradise | 1992 |
| L2439 | 231.0 | 0.0 | m1 | u16 | COLUMBUS | I have to explore the mainland. | 1492: conquest of paradise | 1992 |
| L2440 | 231.0 | 1.0 | m1 | u17 | FERNANDO | This time with me! | 1492: conquest of paradise | 1992 |
| L2484 | 232.0 | 0.0 | m1 | u16 | COLUMBUS | How are you feeling, Fernando? | 1492: conquest of paradise | 1992 |
| L2485 | 232.0 | 1.0 | m1 | u17 | FERNANDO | Not bad. | 1492: conquest of paradise | 1992 |
| L2500 | 233.0 | 0.0 | m1 | u17 | FERNANDO | Father... | 1492: conquest of paradise | 1992 |
| L2501 | 233.0 | 1.0 | m1 | u16 | COLUMBUS | There must be a passage to that other ocean. | 1492: conquest of paradise | 1992 |
| L2541 | 234.0 | 0.0 | m1 | u16 | COLUMBUS | What are you listening to? | 1492: conquest of paradise | 1992 |
| L2542 | 234.0 | 1.0 | m1 | u17 | FERNANDO | I am not listening, Father. But I can't help hearing. | 1492: conquest of paradise | 1992 |
| L2547 | 235.0 | 0.0 | m1 | u16 | COLUMBUS | What does he say? | 1492: conquest of paradise | 1992 |
| L2548 | 235.0 | 1.0 | m1 | u17 | FERNANDO | He asks when he can come to visit you. He left his address. | 1492: conquest of paradise | 1992 |
| L2549 | 235.0 | 2.0 | m1 | u16 | COLUMBUS | He never had one... except aboard my ships! | 1492: conquest of paradise | 1992 |
| L2550 | 236.0 | 0.0 | m1 | u17 | FERNANDO | I want you to tell me everything you remember, Father. From the beginning. Everything. | 1492: conquest of paradise | 1992 |
| L2551 | 236.0 | 1.0 | m1 | u16 | COLUMBUS | Really? God... I wouldn't know where to start... and yet... | 1492: conquest of paradise | 1992 |
| L2552 | 236.0 | 2.0 | m1 | u17 | FERNANDO | Tell me the first thing that comes to your mind. | 1492: conquest of paradise | 1992 |
| L2103 | 237.0 | 0.0 | m1 | u16 | COLUMBUS | No... | 1492: conquest of paradise | 1992 |
| L2104 | 237.0 | 1.0 | m1 | u24 | SANCHEZ | No? | 1492: conquest of paradise | 1992 |
| L2105 | 237.0 | 2.0 | m1 | u16 | COLUMBUS | NO...! I have waited too long, fought too hard. Now you expect me to take all the risks while you take the profit! No... I will not be your servant! | 1492: conquest of paradise | 1992 |
| L2106 | 238.0 | 0.0 | m1 | u24 | SANCHEZ | I remind you, Senor Colon, that you are in no position to bargain with me. | 1492: conquest of paradise | 1992 |
| L2107 | 238.0 | 1.0 | m1 | u16 | COLUMBUS | I'm not bargaining! | 1492: conquest of paradise | 1992 |
| L2108 | 238.0 | 2.0 | m1 | u24 | SANCHEZ | Then you are too ambitious. | 1492: conquest of paradise | 1992 |
| L2109 | 239.0 | 0.0 | m1 | u16 | COLUMBUS | And were you never ambitious, Excellency? Or is ambition only a virtue among the nobles, a fault for the rest of us? | 1492: conquest of paradise | 1992 |
| L2110 | 239.0 | 1.0 | m1 | u24 | SANCHEZ | If you won't accept our proposal, we'll simply find someone who will. | 1492: conquest of paradise | 1992 |
| L2281 | 240.0 | 0.0 | m1 | u16 | COLUMBUS | They don't see sin in their nakedness. They live according to nature, in a never ending summer. The islands are covered with trees, filled with blossoms and fruits. And... | 1492: conquest of paradise | 1992 |
| L2282 | 240.0 | 1.0 | m1 | u24 | SANCHEZ | Forgive me, Don Colon. But what about gold? | 1492: conquest of paradise | 1992 |
| L2286 | 241.0 | 0.0 | m1 | u24 | SANCHEZ | You defend yourself admirably... | 1492: conquest of paradise | 1992 |
| L2287 | 241.0 | 1.0 | m1 | u16 | COLUMBUS | ... for a commoner? | 1492: conquest of paradise | 1992 |
| L2295 | 242.0 | 0.0 | m1 | u16 | COLUMBUS | But we do have a lack of notaries. You should contact my administration. | 1492: conquest of paradise | 1992 |
| L2296 | 242.0 | 1.0 | m1 | u24 | SANCHEZ | Don Bobadilla is already a judge, my Dear Don Cristobal. | 1492: conquest of paradise | 1992 |
| L2297 | 242.0 | 2.0 | m1 | u16 | COLUMBUS | Good! We are also in need of judges. Except there are no thieves! | 1492: conquest of paradise | 1992 |
| L2299 | 243.0 | 0.0 | m1 | u24 | SANCHEZ | You seem to have a special talent for making friends. | 1492: conquest of paradise | 1992 |
| L2300 | 243.0 | 1.0 | m1 | u16 | COLUMBUS | What...? Do I have so many already? | 1492: conquest of paradise | 1992 |
| L2301 | 243.0 | 2.0 | m1 | u24 | SANCHEZ | To rise so high, in so short a time, is a dangerous occupation. A little hypocrisy goes a long way. | 1492: conquest of paradise | 1992 |
| L2463 | 244.0 | 0.0 | m1 | u24 | SANCHEZ | All I have to do is call the guards. | 1492: conquest of paradise | 1992 |
| L2464 | 244.0 | 1.0 | m1 | u16 | COLUMBUS | Call them. | 1492: conquest of paradise | 1992 |
| L2465 | 245.0 | 0.0 | m1 | u24 | SANCHEZ | I am not afraid of you. You are nothing but a dreamer. | 1492: conquest of paradise | 1992 |
| L2466 | 245.0 | 1.0 | m1 | u16 | COLUMBUS | Look out of that window. | 1492: conquest of paradise | 1992 |
| L2467 | 246.0 | 0.0 | m1 | u16 | COLUMBUS | What do you see? | 1492: conquest of paradise | 1992 |
| L2468 | 246.0 | 1.0 | m1 | u24 | SANCHEZ | Roofs... towers, palaces... spires... | 1492: conquest of paradise | 1992 |
| L2469 | 246.0 | 2.0 | m1 | u16 | COLUMBUS | All of them created by people like me. | 1492: conquest of paradise | 1992 |
| L2237 | 247.0 | 0.0 | m1 | u25 | UTAPAN | Say not here! Cuba! | 1492: conquest of paradise | 1992 |
| L2238 | 247.0 | 1.0 | m1 | u16 | COLUMBUS | What is it? A tribe? An island? | 1492: conquest of paradise | 1992 |
| L2239 | 247.0 | 2.0 | m1 | u25 | UTAPAN | Island. Far. | 1492: conquest of paradise | 1992 |
| L2248 | 248.0 | 0.0 | m1 | u25 | UTAPAN | You come! You speak first! | 1492: conquest of paradise | 1992 |
| L2249 | 248.0 | 1.0 | m1 | u16 | COLUMBUS | Tell the Chief we thank him. | 1492: conquest of paradise | 1992 |
| L2250 | 248.0 | 2.0 | m1 | u25 | UTAPAN | Chief knows. | 1492: conquest of paradise | 1992 |
| L2251 | 248.0 | 3.0 | m1 | u16 | COLUMBUS | Tell him his country is very beautiful. Tell him we are leaving men here -- to build a fort. | 1492: conquest of paradise | 1992 |
| L2253 | 249.0 | 0.0 | m1 | u25 | UTAPAN | Chief says -- how many? | 1492: conquest of paradise | 1992 |
| L2254 | 249.0 | 1.0 | m1 | u16 | COLUMBUS | Thousands. | 1492: conquest of paradise | 1992 |
| L2255 | 249.0 | 2.0 | m1 | u25 | UTAPAN | Why? | 1492: conquest of paradise | 1992 |
| L2256 | 250.0 | 0.0 | m1 | u16 | COLUMBUS | To bring the word of God. | 1492: conquest of paradise | 1992 |
| L2257 | 250.0 | 1.0 | m1 | u25 | UTAPAN | Chief says -- he has a God. | 1492: conquest of paradise | 1992 |
| L2258 | 250.0 | 2.0 | m1 | u16 | COLUMBUS | ... and also to bring medicine. | 1492: conquest of paradise | 1992 |
| L2259 | 250.0 | 3.0 | m1 | u25 | UTAPAN | Chief says... | 1492: conquest of paradise | 1992 |
| L2260 | 250.0 | 4.0 | m1 | u16 | COLUMBUS | He has medicine. Tell him we admire his people. | 1492: conquest of paradise | 1992 |
| L2341 | 251.0 | 0.0 | m1 | u16 | COLUMBUS | We will work with his people. We want peace. Ask the Chief if he understands? | 1492: conquest of paradise | 1992 |
| L2342 | 251.0 | 1.0 | m1 | u25 | UTAPAN | He understands. | 1492: conquest of paradise | 1992 |
| L2343 | 251.0 | 2.0 | m1 | u16 | COLUMBUS | Ask him if he will help. | 1492: conquest of paradise | 1992 |
| L2393 | 252.0 | 0.0 | m1 | u16 | COLUMBUS | You have to find them, Utapan. Look what they did! | 1492: conquest of paradise | 1992 |
| L2394 | 252.0 | 1.0 | m1 | u25 | UTAPAN | You did the same to your God! | 1492: conquest of paradise | 1992 |
| L2409 | 253.0 | 0.0 | m1 | u16 | COLUMBUS | Utapan, won't you speak to me? You used to know how to speak to me. | 1492: conquest of paradise | 1992 |
| L2410 | 253.0 | 1.0 | m1 | u25 | UTAPAN | You never learned how to speak my language. | 1492: conquest of paradise | 1992 |
| L1942 | 254.0 | 0.0 | m1 | u19 | MARCHENA | Diego is a bright boy -- a pleasure to teach -- but so serious... Brothers should be raised together, Colon. Even brothers from different mothers... | 1492: conquest of paradise | 1992 |
| L1943 | 254.0 | 1.0 | m1 | u16 | COLUMBUS | Father, I am doing what I think is the best for him. And he has the teacher I would have chosen for myself. | 1492: conquest of paradise | 1992 |
| L1947 | 255.0 | 0.0 | m1 | u16 | COLUMBUS | God... That's in a week! | 1492: conquest of paradise | 1992 |
| L1948 | 255.0 | 1.0 | m1 | u19 | MARCHENA | That's what it says. | 1492: conquest of paradise | 1992 |
| L1949 | 255.0 | 2.0 | m1 | u16 | COLUMBUS | How did you manage it? | 1492: conquest of paradise | 1992 |
| L1950 | 255.0 | 3.0 | m1 | u19 | MARCHENA | With some difficulty. I had to promise them you were not a total fool. | 1492: conquest of paradise | 1992 |
| L1951 | 256.0 | 0.0 | m1 | u19 | MARCHENA | Why do you wish to sail west? | 1492: conquest of paradise | 1992 |
| L1952 | 256.0 | 1.0 | m1 | u16 | COLUMBUS | To open a new route to Asia. At the moment there are only two ways of reaching it... | 1492: conquest of paradise | 1992 |
| L1957 | 257.0 | 0.0 | m1 | u19 | MARCHENA | How can you be so certain? The Ocean is said to be infinite. | 1492: conquest of paradise | 1992 |
| L1958 | 257.0 | 1.0 | m1 | u16 | COLUMBUS | Ignorance! I believe the Indies are no more than 750 leagues west of the Canary Islands. | 1492: conquest of paradise | 1992 |
| L1959 | 257.0 | 2.0 | m1 | u19 | MARCHENA | How can you be so certain? | 1492: conquest of paradise | 1992 |
| L1960 | 257.0 | 3.0 | m1 | u16 | COLUMBUS | The calculations of Toscanelli Marin de Tyr, Esdras... | 1492: conquest of paradise | 1992 |
| L1961 | 257.0 | 4.0 | m1 | u19 | MARCHENA | Esdras is a Jew. | 1492: conquest of paradise | 1992 |
| L1962 | 257.0 | 5.0 | m1 | u16 | COLUMBUS | So was Christ! | 1492: conquest of paradise | 1992 |
| L1963 | 258.0 | 0.0 | m1 | u19 | MARCHENA | Two minutes... and already you're a dead man. Don't let passion overwhelm you, Colon. | 1492: conquest of paradise | 1992 |
| L1964 | 258.0 | 1.0 | m1 | u16 | COLUMBUS | I'll try to remember that, Marchena... | 1492: conquest of paradise | 1992 |
| L1965 | 258.0 | 2.0 | m1 | u19 | MARCHENA | Father Marchena! | 1492: conquest of paradise | 1992 |
| L1966 | 258.0 | 3.0 | m1 | u16 | COLUMBUS | Passion is something one cannot control! | 1492: conquest of paradise | 1992 |
| L1967 | 258.0 | 4.0 | m1 | u19 | MARCHENA | You get so carried away when you are being contradicted! | 1492: conquest of paradise | 1992 |
| L1968 | 258.0 | 5.0 | m1 | u16 | COLUMBUS | I've been contradicted all my life... Eternity! | 1492: conquest of paradise | 1992 |
| L1969 | 258.0 | 6.0 | m1 | u19 | MARCHENA | Only God knows the meaning of such words, my son. | 1492: conquest of paradise | 1992 |
| L2037 | 259.0 | 0.0 | m1 | u19 | MARCHENA | You mustn't give way to despair. You must wait. | 1492: conquest of paradise | 1992 |
| L2038 | 259.0 | 1.0 | m1 | u16 | COLUMBUS | Wait! I've waited seven years already! How much longer do you want me to wait? | 1492: conquest of paradise | 1992 |
| L2039 | 259.0 | 2.0 | m1 | u19 | MARCHENA | If God intends you to go, then you will go. | 1492: conquest of paradise | 1992 |
| L2040 | 259.0 | 3.0 | m1 | u16 | COLUMBUS | Damn God! | 1492: conquest of paradise | 1992 |
| L2041 | 260.0 | 0.0 | m1 | u19 | MARCHENA | Colon! | 1492: conquest of paradise | 1992 |
| L2042 | 260.0 | 1.0 | m1 | u16 | COLUMBUS | Damn all of you! You all set up theories based on what? You never leave the safety of your studies! Go out! Find out what the world is about and then tell me something I can listen to! | 1492: conquest of paradise | 1992 |
| L2045 | 261.0 | 0.0 | m1 | u16 | COLUMBUS | All of them! Just lies! | 1492: conquest of paradise | 1992 |
| L2046 | 261.0 | 1.0 | m1 | u19 | MARCHENA | Colon! Don't! | 1492: conquest of paradise | 1992 |
| L2132 | 262.0 | 0.0 | m1 | u19 | MARCHENA | In Nomine Patris et Filius, et Spiritus Sancti. | 1492: conquest of paradise | 1992 |
| L2133 | 262.0 | 1.0 | m1 | u16 | COLUMBUS | Forgive me, Father. For I have sinned. | 1492: conquest of paradise | 1992 |
| L2134 | 263.0 | 0.0 | m1 | u19 | MARCHENA | I am listening, my son. | 1492: conquest of paradise | 1992 |
| L2135 | 263.0 | 1.0 | m1 | u16 | COLUMBUS | Father, I have betrayed my family. I betrayed my men. And I betrayed you. | 1492: conquest of paradise | 1992 |
| L2136 | 263.0 | 2.0 | m1 | u19 | MARCHENA | What are you saying? | 1492: conquest of paradise | 1992 |
| L2137 | 263.0 | 3.0 | m1 | u16 | COLUMBUS | I lied. The journey will be longer than I said. | 1492: conquest of paradise | 1992 |
| L2138 | 263.0 | 4.0 | m1 | u19 | MARCHENA | How long? | 1492: conquest of paradise | 1992 |
| L2139 | 263.0 | 5.0 | m1 | u16 | COLUMBUS | I am not sure... It could be twice the distance. | 1492: conquest of paradise | 1992 |
| L2140 | 264.0 | 0.0 | m1 | u19 | MARCHENA | May God forgive you...! You must tell them! You must tell your men! | 1492: conquest of paradise | 1992 |
| L2141 | 264.0 | 1.0 | m1 | u16 | COLUMBUS | If I tell them, they won't follow me. You know that I am right, Father. You trust me... | 1492: conquest of paradise | 1992 |
| L2142 | 264.0 | 2.0 | m1 | u19 | MARCHENA | My son, my son... Your certitudes are sometimes frightening... Christopher, you must speak to them. And if you don't I will. | 1492: conquest of paradise | 1992 |
| L2143 | 264.0 | 3.0 | m1 | u16 | COLUMBUS | You are bound by an oath, Father. | 1492: conquest of paradise | 1992 |
| L2144 | 265.0 | 0.0 | m1 | u19 | MARCHENA | I believed in you... | 1492: conquest of paradise | 1992 |
| L2145 | 265.0 | 1.0 | m1 | u16 | COLUMBUS | Give me absolution. | 1492: conquest of paradise | 1992 |
| L2525 | 266.0 | 0.0 | m1 | u19 | MARCHENA | I suppose we're both old men now. | 1492: conquest of paradise | 1992 |
| L2526 | 266.0 | 1.0 | m1 | u16 | COLUMBUS | You'll always be older than me, Father. | 1492: conquest of paradise | 1992 |
| L2531 | 267.0 | 0.0 | m1 | u16 | COLUMBUS | I have to disagree. | 1492: conquest of paradise | 1992 |
| L2532 | 267.0 | 1.0 | m1 | u19 | MARCHENA | I knew you would. | 1492: conquest of paradise | 1992 |
| L2533 | 267.0 | 2.0 | m1 | u16 | COLUMBUS | New worlds create new people. | 1492: conquest of paradise | 1992 |
| L2534 | 267.0 | 3.0 | m1 | u19 | MARCHENA | Oh? So you are a new man? | 1492: conquest of paradise | 1992 |
| L2535 | 267.0 | 4.0 | m1 | u16 | COLUMBUS | I don't know... I have the impression that I didn't change that much. I still can't accept the world as it is! | 1492: conquest of paradise | 1992 |
| L2071 | 268.0 | 0.0 | m1 | u18 | ISABEL | I should not even be listening to you, since my council said no. But Santangel tells me you are a man of honor and sincerity... And Sanchez, that you are not a fool. | 1492: conquest of paradise | 1992 |
| L2072 | 268.0 | 1.0 | m1 | u16 | COLUMBUS | No more than the woman who said she would take Granada from the Moors. | 1492: conquest of paradise | 1992 |
| L2073 | 269.0 | 0.0 | m1 | u18 | ISABEL | The ocean is uncrossable? | 1492: conquest of paradise | 1992 |
| L2074 | 269.0 | 1.0 | m1 | u16 | COLUMBUS | What did they say about Granada before today? | 1492: conquest of paradise | 1992 |
| L2075 | 269.0 | 2.0 | m1 | u18 | ISABEL | That she was impregnable. | 1492: conquest of paradise | 1992 |
| L2076 | 270.0 | 0.0 | m1 | u18 | ISABEL | I cannot ignore the verdict of my council. | 1492: conquest of paradise | 1992 |
| L2077 | 270.0 | 1.0 | m1 | u16 | COLUMBUS | Surely you can do anything you want. | 1492: conquest of paradise | 1992 |
| L2079 | 271.0 | 0.0 | m1 | u16 | COLUMBUS | May I speak freely? | 1492: conquest of paradise | 1992 |
| L2080 | 271.0 | 1.0 | m1 | u18 | ISABEL | You show no inclination to speak otherwise! | 1492: conquest of paradise | 1992 |
| L2081 | 271.0 | 2.0 | m1 | u16 | COLUMBUS | I know what I see. I see someone who doesn't accept the world as it is. Who's not afraid. I see a women who thinks... "What if?"... | 1492: conquest of paradise | 1992 |
| L2082 | 271.0 | 3.0 | m1 | u18 | ISABEL | A woman? | 1492: conquest of paradise | 1992 |
| L2085 | 272.0 | 0.0 | m1 | u18 | ISABEL | How old are you, Senor Colon? | 1492: conquest of paradise | 1992 |
| L2086 | 272.0 | 1.0 | m1 | u16 | COLUMBUS | Thirty seven, Your Majesty... And you? | 1492: conquest of paradise | 1992 |
| L2279 | 273.0 | 0.0 | m1 | u18 | ISABEL | Do they have such thoughts? | 1492: conquest of paradise | 1992 |
| L2280 | 273.0 | 1.0 | m1 | u16 | COLUMBUS | They come and go as naked as the day God created them... | 1492: conquest of paradise | 1992 |
| L2456 | 274.0 | 0.0 | m1 | u18 | ISABEL | But without your brothers. Nor are you to return to Santo Domingo or any of the other colonies. You may explore the continent. | 1492: conquest of paradise | 1992 |
| L2457 | 274.0 | 1.0 | m1 | u16 | COLUMBUS | Thank you. | 1492: conquest of paradise | 1992 |
| L2458 | 274.0 | 2.0 | m1 | u18 | ISABEL | There is one thing I'd like to understand... Why do you want to go back, after all this? | 1492: conquest of paradise | 1992 |
| L2459 | 274.0 | 3.0 | m1 | u16 | COLUMBUS | Your Majesty -- some men are content to read about things. I must see them with my own eyes. I cannot be other than I am. | 1492: conquest of paradise | 1992 |
| L2277 | 275.0 | 0.0 | m1 | u21 | MOXICA | And you say this is an Indian vice? By God! I don't see any kind of pleasure that would make this a sin. | 1492: conquest of paradise | 1992 |
| L2278 | 275.0 | 1.0 | m1 | u16 | COLUMBUS | The Indians have no such word, Don Moxica. | 1492: conquest of paradise | 1992 |
| L2328 | 276.0 | 0.0 | m1 | u21 | MOXICA | We lost cousins, friends. We will wash this in blood. | 1492: conquest of paradise | 1992 |
| L2329 | 276.0 | 1.0 | m1 | u16 | COLUMBUS | If you want to keep your head on your shoulders, you'll do as I say. | 1492: conquest of paradise | 1992 |
| L2331 | 277.0 | 0.0 | m1 | u16 | COLUMBUS | You want a war? Fine. We are a thousand. They outnumber us by ten! Who will you kill? Which tribe? | 1492: conquest of paradise | 1992 |
| L2332 | 277.0 | 1.0 | m1 | u21 | MOXICA | We don't need to know. | 1492: conquest of paradise | 1992 |
| L2333 | 277.0 | 2.0 | m1 | u16 | COLUMBUS | We came here to stay! To build! Not to start a crusade. In this forest, there is enough danger to sweep us away in days! So we will be brave and swallow our grief. And in the name of those who died, we will accomplish what we came for. | 1492: conquest of paradise | 1992 |
| L2345 | 278.0 | 0.0 | m1 | u16 | COLUMBUS | We can't raise the wheel without it. | 1492: conquest of paradise | 1992 |
| L2346 | 278.0 | 1.0 | m1 | u21 | MOXICA | My horse doesn't work. | 1492: conquest of paradise | 1992 |
| L2347 | 279.0 | 0.0 | m1 | u16 | COLUMBUS | Don Moxica -- we all have to work. | 1492: conquest of paradise | 1992 |
| L2348 | 279.0 | 1.0 | m1 | u21 | MOXICA | You did not hear me, Don Colon. Not my horse. | 1492: conquest of paradise | 1992 |
| L2386 | 280.0 | 0.0 | m1 | u16 | COLUMBUS | In one act of brutality, you have created chaos. Tribes who were fighting each other are now joining forces against us! All that because of your criminal savagery! | 1492: conquest of paradise | 1992 |
| L2387 | 280.0 | 1.0 | m1 | u21 | MOXICA | Savagery is what monkeys understand. | 1492: conquest of paradise | 1992 |
| L2388 | 280.0 | 2.0 | m1 | u16 | COLUMBUS | You'll be held in detention, deprived of your privileges and possessions. Until you are returned to Spain where you will be judged. Have you anything to say? | 1492: conquest of paradise | 1992 |
| L2389 | 280.0 | 3.0 | m1 | u21 | MOXICA | You will regret this. | 1492: conquest of paradise | 1992 |
| L2154 | 281.0 | 0.0 | m1 | u16 | COLUMBUS | Due west, Captain Mendez. And may God be with us... | 1492: conquest of paradise | 1992 |
| L2155 | 281.0 | 1.0 | m1 | u20 | MENDEZ | God be with us admiral. | 1492: conquest of paradise | 1992 |
| L2157 | 282.0 | 0.0 | m1 | u20 | MENDEZ | Well... It's the men, Sir. They wonder how you know our position. We've lost sight from land days ago... | 1492: conquest of paradise | 1992 |
| L2158 | 282.0 | 1.0 | m1 | u16 | COLUMBUS | And what do you think Mendez? | 1492: conquest of paradise | 1992 |
| L2159 | 282.0 | 2.0 | m1 | u20 | MENDEZ | Well, I surely know what a quadrant is! But I've never seen it used at night before. | 1492: conquest of paradise | 1992 |
| L2160 | 282.0 | 3.0 | m1 | u16 | COLUMBUS | Come over here. | 1492: conquest of paradise | 1992 |
| L2164 | 283.0 | 0.0 | m1 | u16 | COLUMBUS | What do you read? | 1492: conquest of paradise | 1992 |
| L2165 | 283.0 | 1.0 | m1 | u20 | MENDEZ | Twenty eight. | 1492: conquest of paradise | 1992 |
| L2490 | 284.0 | 0.0 | m1 | u20 | MENDEZ | What's he doing? | 1492: conquest of paradise | 1992 |
| L2491 | 284.0 | 1.0 | m1 | u16 | COLUMBUS | He's drawing an isthmus... He's saying we're on an isthmus. | 1492: conquest of paradise | 1992 |
| L2492 | 284.0 | 2.0 | m1 | u20 | MENDEZ | We can't be. | 1492: conquest of paradise | 1992 |
| L2064 | 285.0 | 0.0 | m1 | u16 | COLUMBUS | Where can I meet this man? | 1492: conquest of paradise | 1992 |
| L2065 | 285.0 | 1.0 | m1 | u22 | PINZON | Immediately. | 1492: conquest of paradise | 1992 |
| L2196 | 286.0 | 0.0 | m1 | u22 | PINZON | You lied! You cheated! We're way past 750 leagues! | 1492: conquest of paradise | 1992 |
| L2197 | 286.0 | 1.0 | m1 | u16 | COLUMBUS | Six days ago, yes. | 1492: conquest of paradise | 1992 |
| L2198 | 286.0 | 2.0 | m1 | u22 | PINZON | You must be mad...! | 1492: conquest of paradise | 1992 |
| L2199 | 286.0 | 3.0 | m1 | u16 | COLUMBUS | We have to keep the hopes of these men alive! | 1492: conquest of paradise | 1992 |
| L2200 | 286.0 | 4.0 | m1 | u22 | PINZON | We're on the verge of a mutiny, Colon! | 1492: conquest of paradise | 1992 |
| L2201 | 286.0 | 5.0 | m1 | u16 | COLUMBUS | You think I don't know that? | 1492: conquest of paradise | 1992 |
| L2202 | 286.0 | 6.0 | m1 | u22 | PINZON | We're lost! | 1492: conquest of paradise | 1992 |
| L2203 | 286.0 | 7.0 | m1 | u16 | COLUMBUS | The land is there. I know it! | 1492: conquest of paradise | 1992 |
| L2204 | 286.0 | 8.0 | m1 | u22 | PINZON | You don't know anything! Listen Colon, these are my ships, right? So I'm telling you we're turning back! | 1492: conquest of paradise | 1992 |
| L2205 | 286.0 | 9.0 | m1 | u16 | COLUMBUS | And then what? Half of the water has gone, the rest is nearly putrid! You know that! | 1492: conquest of paradise | 1992 |
| L2206 | 286.0 | 10.0 | m1 | u22 | PINZON | Jesus Maria! I should have never listened to you! | 1492: conquest of paradise | 1992 |
| L2207 | 286.0 | 11.0 | m1 | u16 | COLUMBUS | You never did. You did all the talking for both of us, remember? | 1492: conquest of paradise | 1992 |
| L2208 | 286.0 | 12.0 | m1 | u22 | PINZON | You bloody... | 1492: conquest of paradise | 1992 |
| L2209 | 286.0 | 13.0 | m1 | u16 | COLUMBUS | Pinzon, Pinzon... All we can do now is go forward! Think about that! | 1492: conquest of paradise | 1992 |
| L2210 | 286.0 | 14.0 | m1 | u22 | PINZON | You tell that to them! | 1492: conquest of paradise | 1992 |
| L2211 | 286.0 | 15.0 | m1 | u16 | COLUMBUS | You're right. Let the men decide. | 1492: conquest of paradise | 1992 |
| L1930 | 287.0 | 0.0 | m1 | u18 | ISABEL | Is that the man I knew, Treasurer Sanchez? | 1492: conquest of paradise | 1992 |
| L1931 | 287.0 | 1.0 | m1 | u24 | SANCHEZ | Yes, Your Majesty. | 1492: conquest of paradise | 1992 |
| L2112 | 288.0 | 0.0 | m1 | u18 | ISABEL | You were right, Don Sanchez... His demands could never be granted. | 1492: conquest of paradise | 1992 |
| L2113 | 288.0 | 1.0 | m1 | u24 | SANCHEZ | Never, Your Majesty. Although... | 1492: conquest of paradise | 1992 |
| L2115 | 289.0 | 0.0 | m1 | u24 | SANCHEZ | ... Into a monk... | 1492: conquest of paradise | 1992 |
| L2116 | 289.0 | 1.0 | m1 | u18 | ISABEL | Yes. It would be a pity, wouldn't it? Call him back! | 1492: conquest of paradise | 1992 |
| L2376 | 290.0 | 0.0 | m1 | u24 | SANCHEZ | Every ship returns with a cargo of sick and dying. But with no gold! The new world proves expensive, Your Majesty. | 1492: conquest of paradise | 1992 |
| L2377 | 290.0 | 1.0 | m1 | u18 | ISABEL | We weren't expecting immediate profits, were we? We must have faith. We must give time for time. | 1492: conquest of paradise | 1992 |
| L2414 | 291.0 | 0.0 | m1 | u24 | SANCHEZ | ... But there is worse. He ordered the execution of five members of the nobility... | 1492: conquest of paradise | 1992 |
| L2415 | 291.0 | 1.0 | m1 | u18 | ISABEL | Is this true, Brother Buyl? | 1492: conquest of paradise | 1992 |
| L2417 | 292.0 | 0.0 | m1 | u18 | ISABEL | Then, what do you suggest, Don Sanchez? | 1492: conquest of paradise | 1992 |
| L2418 | 292.0 | 1.0 | m1 | u24 | SANCHEZ | He must be replaced. | 1492: conquest of paradise | 1992 |
| L2419 | 292.0 | 2.0 | m1 | u18 | ISABEL | And who would you think of, for such a task? | 1492: conquest of paradise | 1992 |
| L2460 | 293.0 | 0.0 | m1 | u18 | ISABEL | I know, I should not tolerate his impertinence. | 1492: conquest of paradise | 1992 |
| L2461 | 293.0 | 1.0 | m1 | u24 | SANCHEZ | Then why? | 1492: conquest of paradise | 1992 |
| L2462 | 293.0 | 2.0 | m1 | u18 | ISABEL | Because he is not afraid of me. | 1492: conquest of paradise | 1992 |
| L3380 | 294.0 | 0.0 | m2 | u30 | EMIL | Are you my attorney? I'm Emil. I'm insane. | 15 minutes | 2001 |
| L3381 | 294.0 | 1.0 | m2 | u26 | CUTLER | I'm not your lawyer until I see the money. | 15 minutes | 2001 |
| L3382 | 294.0 | 2.0 | m2 | u30 | EMIL | Here. I have your money. | 15 minutes | 2001 |
| L3383 | 295.0 | 0.0 | m2 | u30 | EMIL | Oh no! No! Shit! | 15 minutes | 2001 |
| L3384 | 295.0 | 1.0 | m2 | u26 | CUTLER | Emil. Take it easy. Stay with me. Sit down. What do you need? What are you looking for? | 15 minutes | 2001 |
| L3385 | 295.0 | 2.0 | m2 | u30 | EMIL | He has the camera! He took the movie! | 15 minutes | 2001 |
| L3392 | 296.0 | 0.0 | m2 | u26 | CUTLER | Don't say anything. | 15 minutes | 2001 |
| L3393 | 296.0 | 1.0 | m2 | u30 | EMIL | Where are we going? | 15 minutes | 2001 |
| L3394 | 296.0 | 2.0 | m2 | u26 | CUTLER | I'm coming with you. | 15 minutes | 2001 |
| L3395 | 296.0 | 3.0 | m2 | u30 | EMIL | Yes. Yes, come with me! | 15 minutes | 2001 |
| L3396 | 296.0 | 4.0 | m2 | u26 | CUTLER | I'm invoking rights - this man is represented by counsel. I'm coming with him. | 15 minutes | 2001 |
| L3459 | 297.0 | 0.0 | m2 | u26 | CUTLER | I brought you some letters. It's really fan mail. Women mostly. One wants to buy you clothes, another sent a check. Another wants a check. | 15 minutes | 2001 |
| L3460 | 297.0 | 1.0 | m2 | u30 | EMIL | You bring the cigarettes? | 15 minutes | 2001 |
| L3461 | 297.0 | 2.0 | m2 | u26 | CUTLER | Oh, sure. | 15 minutes | 2001 |
| L3464 | 298.0 | 0.0 | m2 | u26 | CUTLER | ...delusions and paranoia. | 15 minutes | 2001 |
| L3465 | 298.0 | 1.0 | m2 | u30 | EMIL | I was all of these. | 15 minutes | 2001 |
| L3466 | 298.0 | 2.0 | m2 | u26 | CUTLER | Well, you didn't appreciate the severity of it until recently. No question about that. | 15 minutes | 2001 |
| L3467 | 298.0 | 3.0 | m2 | u30 | EMIL | What about Oleg? | 15 minutes | 2001 |
| L3468 | 298.0 | 4.0 | m2 | u26 | CUTLER | Disappeared. They're looking everywhere. Maybe he went back to Czechoslovakia. | 15 minutes | 2001 |
| L3469 | 298.0 | 5.0 | m2 | u30 | EMIL | No, he is here. Shit... | 15 minutes | 2001 |
| L3470 | 298.0 | 6.0 | m2 | u26 | CUTLER | Don't worry about him. Think about yourself. | 15 minutes | 2001 |
| L3471 | 298.0 | 7.0 | m2 | u30 | EMIL | What about my movie rights? Book rights? | 15 minutes | 2001 |
| L3472 | 298.0 | 8.0 | m2 | u26 | CUTLER | Look, I haven't really focused on that kind of thing. | 15 minutes | 2001 |
| L3473 | 298.0 | 9.0 | m2 | u30 | EMIL | What's your cut? How much? | 15 minutes | 2001 |
| L3474 | 298.0 | 10.0 | m2 | u26 | CUTLER | I would say...half. Half is fair. | 15 minutes | 2001 |
| L3475 | 298.0 | 11.0 | m2 | u30 | EMIL | No. No way. | 15 minutes | 2001 |
| L3476 | 298.0 | 12.0 | m2 | u26 | CUTLER | But it's... | 15 minutes | 2001 |
| L3477 | 298.0 | 13.0 | m2 | u30 | EMIL | Thirty-percent. No more. Or I call another lawyer. This is the biggest case of your life. Don't try to negotiate. Thirty percent. Say yes or no. | 15 minutes | 2001 |
| L3478 | 298.0 | 14.0 | m2 | u26 | CUTLER | This is not about money, Emil. I need your trust in me. | 15 minutes | 2001 |
| L3479 | 298.0 | 15.0 | m2 | u30 | EMIL | What else do you need? | 15 minutes | 2001 |
| L3480 | 298.0 | 16.0 | m2 | u26 | CUTLER | I need to know about your background. I need to know about your upbringing. Why you're here. | 15 minutes | 2001 |
| L3481 | 298.0 | 17.0 | m2 | u30 | EMIL | Give me another one, please. | 15 minutes | 2001 |
| L3482 | 299.0 | 0.0 | m2 | u26 | CUTLER | Tell me about yourself. What you did as a young boy... what your parents were like. | 15 minutes | 2001 |
| L3483 | 299.0 | 1.0 | m2 | u30 | EMIL | My father always degraded me. Killed my self-esteem. And my mother was blind. | 15 minutes | 2001 |
| L3484 | 299.0 | 2.0 | m2 | u26 | CUTLER | Your mother was blind? | 15 minutes | 2001 |
| L3485 | 299.0 | 3.0 | m2 | u30 | EMIL | Yeah, she went blind giving birth to me. She went to fucking black market doctor to induce me. | 15 minutes | 2001 |
| L3486 | 299.0 | 4.0 | m2 | u26 | CUTLER | Back in the Czech Republic? | 15 minutes | 2001 |
| L3487 | 299.0 | 5.0 | m2 | u30 | EMIL | Yeah, yeah...bad doctor gave her bad drugs which made her go blind. And my father blamed me for her blindness... | 15 minutes | 2001 |
| L3488 | 299.0 | 6.0 | m2 | u26 | CUTLER | Your father blamed you for your mother's blindness? | 15 minutes | 2001 |
| L3489 | 299.0 | 7.0 | m2 | u30 | EMIL | Yeah, he hated me from day when I was born. Put it out. Can you put the cigarette out? | 15 minutes | 2001 |
| L3490 | 300.0 | 0.0 | m2 | u30 | EMIL | That's what he did to me. He put cigarettes out on me. | 15 minutes | 2001 |
| L3491 | 300.0 | 1.0 | m2 | u26 | CUTLER | Your father put cigarettes out on you? | 15 minutes | 2001 |
| L3492 | 300.0 | 2.0 | m2 | u30 | EMIL | Out on my back when I was a small boy. | 15 minutes | 2001 |
| L3493 | 300.0 | 3.0 | m2 | u26 | CUTLER | Can I see your back? | 15 minutes | 2001 |
| L3496 | 301.0 | 0.0 | m2 | u30 | EMIL | I'm abused. Don't you think? | 15 minutes | 2001 |
| L3497 | 301.0 | 1.0 | m2 | u26 | CUTLER | I don't think it's abuse, I think it's torture. | 15 minutes | 2001 |
| L3545 | 302.0 | 0.0 | m2 | u30 | EMIL | ...so we kill someone famous and if we are caught, we are sent to mental hospital... | 15 minutes | 2001 |
| L3546 | 302.0 | 1.0 | m2 | u26 | CUTLER | Officers, there's your killer, do your duty, arrest him! | 15 minutes | 2001 |
| L3059 | 303.0 | 0.0 | m2 | u27 | DAPHNE | ...my little sister and I shared a flat - I came home one night and a man was raping her. His gun was on the chair... He came at me and I shot him. | 15 minutes | 2001 |
| L3060 | 303.0 | 1.0 | m2 | u34 | JORDY | Alright. That's a justifiable homicide. | 15 minutes | 2001 |
| L3061 | 303.0 | 2.0 | m2 | u27 | DAPHNE | Yes, but he was a cop. | 15 minutes | 2001 |
| L3089 | 304.0 | 0.0 | m2 | u27 | DAPHNE | Now I become custody of police department? | 15 minutes | 2001 |
| L3090 | 304.0 | 1.0 | m2 | u34 | JORDY | If you cooperate with the DA - maybe they'll help you with your situation. | 15 minutes | 2001 |
| L3091 | 304.0 | 2.0 | m2 | u27 | DAPHNE | I will if they don't send me back. | 15 minutes | 2001 |
| L3092 | 304.0 | 3.0 | m2 | u34 | JORDY | They won't until this is over. | 15 minutes | 2001 |
| L3093 | 305.0 | 0.0 | m2 | u27 | DAPHNE | Are you married? | 15 minutes | 2001 |
| L3094 | 305.0 | 1.0 | m2 | u34 | JORDY | Divorced. | 15 minutes | 2001 |
| L3095 | 305.0 | 2.0 | m2 | u27 | DAPHNE | Do you live alone? I've been in these clothes since...the killings. Could we stop at your place? I could take a shower...before I go into custody? | 15 minutes | 2001 |
Let's amalgamate the texts utered in the same conversations together.
By doing this we loose all the information in the order of utterance.
But this is fine as we are going to do LDA with just the first-order information of words uttered in each conversation by anyone involved in the dialogue.
import org.apache.spark.sql.functions.{collect_list, udf, lit, concat_ws}
val corpusDF = convLines.groupBy($"conversationID",$"movieID")
.agg(concat_ws(" :-()-: ",collect_list($"text")).alias("corpus"))
.join(moviesMetaData, "movieID") // and join it to get movie meta data
.select($"conversationID".as("id"),$"corpus",$"movieTitle",$"movieYear")
.cache()
import org.apache.spark.sql.functions.{collect_list, udf, lit, concat_ws}
corpusDF: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: bigint, corpus: string ... 2 more fields]
corpusDF.count()
res28: Long = 83097
corpusDF.take(5).foreach(println)
[28762,Your wife and children...you're happy with them? :-()-: Yes. :-()-: Good.,the godfather,1972]
[28815,Michael? :-()-: I'm thinking about it. :-()-: Michael... :-()-: No, I would not like you better if you were Ingrid Bergman.,the godfather,1972]
[28842,What is it? :-()-: Is it all right if I go to the bathroom?,the godfather,1972]
[28766,Things went badly in Palermo? :-()-: The younger men have no respect. Things are changing; I don't know what will happen. Michael, because of the wedding, people now know your name. :-()-: Is that why there are more men on the walls? :-()-: Even so, I don't think it is safe here anymore. I've made plans to move you to a villa near Siracuse. You must go right away. :-()-: What is it? :-()-: Bad news from America. Your brother, Santino. He has been killed.,the godfather,1972]
[28835,We can't wait. No matter what Sollozzo say about a deal, he's figuring out how to kill Pop. You have to get Sollozzo now. :-()-: The kid's right.,the godfather,1972]
display(corpusDF)
| id | corpus | movieTitle | movieYear |
|---|---|---|---|
| 28762.0 | Your wife and children...you're happy with them? :-()-: Yes. :-()-: Good. | the godfather | 1972 |
| 28815.0 | Michael? :-()-: I'm thinking about it. :-()-: Michael... :-()-: No, I would not like you better if you were Ingrid Bergman. | the godfather | 1972 |
| 28842.0 | What is it? :-()-: Is it all right if I go to the bathroom? | the godfather | 1972 |
| 28766.0 | Things went badly in Palermo? :-()-: The younger men have no respect. Things are changing; I don't know what will happen. Michael, because of the wedding, people now know your name. :-()-: Is that why there are more men on the walls? :-()-: Even so, I don't think it is safe here anymore. I've made plans to move you to a villa near Siracuse. You must go right away. :-()-: What is it? :-()-: Bad news from America. Your brother, Santino. He has been killed. | the godfather | 1972 |
| 28835.0 | We can't wait. No matter what Sollozzo say about a deal, he's figuring out how to kill Pop. You have to get Sollozzo now. :-()-: The kid's right. | the godfather | 1972 |
| 28717.0 | Jesus, Connie...Sure, Mike... :-()-: Go back to your house and wait for me... | the godfather | 1972 |
| 28735.0 | I tol' you to stay put, Paulie... :-()-: The guy at the gate's outside...says there's a package... | the godfather | 1972 |
| 28752.0 | What is this nonsense? :-()-: It's from Johnny. It was announced this morning. He's going to play the lead in the new Woltz Brothers film. | the godfather | 1972 |
| 28852.0 | You straightened my brother out? :-()-: Hell, he was banging cocktail waitresses two at a time. Players couldn't get a drink. | the godfather | 1972 |
| 28782.0 | Tom, you're the Consigliere, what do we do if the old man dies? :-()-: Without your father's political contacts and personal influence, the Corleone family loses half its strength. Without your father, the other New York families might wind up supporting Sollozzo, and the Tattaglias just to make sure there isn't a long destructive war. The old days are over, this is 1946; nobody wants bloodshed anymore. If your father dies...make the deal, Sonny. :-()-: That's easy to say; it's not your father. :-()-: I was as good a son to him as you or Mike. :-()-: Oh Christ Tom, I didn't mean it that way. :-()-: We're all tired... :-()-: OK, we sit tight until the old man can give us the lead. But Tom, I want you to stay inside the Mall. You too, Mike, no chances. Tessio, you hold your people in reserve, but have them nosing around the city. The hospital is yours; I want it tight, fool-proof, 24 hours a day. | the godfather | 1972 |
| 28840.0 | We're going to New Jersey? :-()-: Maybe. | the godfather | 1972 |
| 28729.0 | Sollozzo knows Mike's a civilian. :-()-: OK, but be careful. | the godfather | 1972 |
| 28730.0 | I want somebody very good, very safe to plant that gun. I don't want my brother coming out of that toilet with just his dick in his hand. :-()-: The gun will be there. :-()-: You're on, kid...I'll square it with Mom your not seeing her before you left. And I'll get a message to your girl friend when I think the time is right. :-()-: We gotta move... | the godfather | 1972 |
| 28774.0 | Ever seen anything like that before? :-()-: No. | the godfather | 1972 |
| 28724.0 | The food is on the table. :-()-: I'm not hungry yet. :-()-: Eat it, it's on the table. :-()-: Ba Fa Goulle. :-()-: BA FA GOULE YOU! | the godfather | 1972 |
| 28839.0 | I'm glad you came, Mike. I hope we can straighten everything out. All this is terrible, it's not the way I wanted things to happen at all. It should never have happened. :-()-: I want to settle things tonight. I want my father left alone. :-()-: He won't be; I swear to you be my children he won't be. Just keep an open mind when we talk. I hope you're not a hothead like your brother, Sonny. It's impossible to talk business with him. | the godfather | 1972 |
| 28827.0 | You cannot stay here...I'm sorry. :-()-: You and I are going to move my father right now...to another room on another floor...Can you disconnect those tubes so we can wheel the bed out? :-()-: Absolutely not! We have to get permission from the Doctor. :-()-: You've read about my father in the papers. You've seen that no one's here to guard him. Now I've just gotten word that men are coming to this hospital to kill him. Believe me and help me. :-()-: We don't have to disconnect them, we can wheel the stand with the bed. | the godfather | 1972 |
| 28721.0 | What's the matter, Carlo? :-()-: Shut up. | the godfather | 1972 |
| 28820.0 | When will I see you again? :-()-: Goodbye. | the godfather | 1972 |
| 28843.0 | Do you pledge to guide and protect this child if he is left fatherless? Do you promise to shield him against the wickedness of the world? :-()-: Yes, I promise. | the godfather | 1972 |
| 28816.0 | Hello. Kay? :-()-: How is your father? :-()-: He'll be OK. :-()-: I love you. | the godfather | 1972 |
| 28751.0 | When does my daughter leave with her bridegroom? :-()-: They'll cut the cake in a few minutes...leave right after that. Your new son-in-law, do we give him something important? :-()-: No, give him a living. But never let him know the family's business. What else, Tom? :-()-: I've called the hospital; they've notified Consiglere Genco's family to come and wait. He won't last out the night. | the godfather | 1972 |
| 28728.0 | You take care of Paulie? :-()-: You won't see Paulie anymore. He's sick for good this winter. | the godfather | 1972 |
| 28744.0 | All right, Hollywood...Now tell me about this Hollywood Pezzonovanta who won't let you work. :-()-: He owns the studio. Just a month ago he bought the movie rights to this book, a best seller. And the main character is a guy just like me. I wouldn't even have to act, just be myself. | the godfather | 1972 |
| 28780.0 | Is the hospital covered? :-()-: The cops have it locked in and I got my people there visiting Pop all the time. What about the hit list. | the godfather | 1972 |
| 28781.0 | What about Luca? Sollozzo didn't seem worried about Luca. That worries me. :-()-: If Luca sold out we're in real trouble. :-()-: Has anyone been able to get in touch with him? :-()-: No, and I've been calling all night. Maybe he's shacked up. :-()-: Luca never sleeps over with a broad. He always goes home when he's through. Mike, keep ringing Luca's number. | the godfather | 1972 |
| 28785.0 | I found out about this Captain McCluskey who broke Mike's jaw. He's definitely on Sollozzo's payroll, and for big money. McCluskey's agreed to be the Turk's bodyguard. What you have to understand is that while Sollozzo is guarded like this, he's invulnerable. Nobody has ever gunned down a New York Police Captain. Never. It would be disastrous. All the five families would come after you Sonny; the Corleone family would be outcasts; even the old man's political protection would run for cover. So just...take that into consideration. :-()-: McCluskey can't stay with the Turk forever. We'll wait. | the godfather | 1972 |
| 28761.0 | Barzini will move against you first. :-()-: How? :-()-: He will get in touch with you through someone you absolutely trust. That person will arrange a meeting, guarantee your safety... | the godfather | 1972 |
| 28738.0 | Good for ten men... :-()-: OK, go to Arthur Avenue; I'm suppose to call when I found somethin'. | the godfather | 1972 |
| 28803.0 | I've never seen anything like it. :-()-: I told you I had a lot of relatives. | the godfather | 1972 |
| 28836.0 | Go on Mike. :-()-: They want me to go to the conference with Sollozzo. Set up the meeting for two days from now. Sonny, get our informers to find out where the meeting will be held. Insist it has to be a public place: a bar or restaurant at the height of the dinner hour. So I'll feel safe. They'll check me when I meet them so I won't be able to carry a weapon; but Clemenza, figure out a way to have one planted there for me. Then I'll kill them both. | the godfather | 1972 |
| 28720.0 | Don't be frightened. Do you think I'd make my sister a widow? Do you think I'd make your children fatherless? After all, I'm Godfather to your son. No, your punishment is that you're out of the family business. I'm putting you on a plane to Vegas--and I want you to stay there. I'll send Connie an allowance, that's all. But don't keep saying you're innocent; it insults my intelligence and makes me angry. Who approached you, Tattaglia or Barzini? :-()-: Barzini. :-()-: Good, good. Leave now; there's a car waiting to take you to the airport. | the godfather | 1972 |
| 28745.0 | You take care of your family? :-()-: Sure. | the godfather | 1972 |
| 28753.0 | My wife was weeping before she fell asleep, outside my window I saw my caporegimes to the house, and it is midnight. So, Consigliere of mine, I think you should tell your Don what everyone knows. :-()-: I didn't tell Mama anything. I was about to come up and wake you and tell you. Just now. :-()-: But you needed a drink first. :-()-: Yes. :-()-: Now you've had your drink. | the godfather | 1972 |
| 28767.0 | You tell us about America. :-()-: How do you know I come from America? :-()-: We hear. We were told you were a Pezzonovanta...big shot. :-()-: Only the son of a Pezzonovanta. :-()-: Hey America! Is she as rich as they say? :-()-: Yes. :-()-: Take me to America! You need a good lupara in America? You take me, I'll be the best man you got. "Oh say, can you seeee...By da star early light..." | the godfather | 1972 |
| 28797.0 | Hello Kay. Your father's inside, doing some business. He's been asking for you. :-()-: Thanks Tom. | the godfather | 1972 |
| 28814.0 | Would you like me better if I were a nun? :-()-: No. :-()-: Would you like me better if I were Ingrid Bergman? | the godfather | 1972 |
| 28817.0 | I LOVE YOU. :-()-: Yeah Kay, I'm here. :-()-: Can you say it? :-()-: Huh? :-()-: Tell me you love me. | the godfather | 1972 |
| 28845.0 | Do you wish to be baptized? :-()-: I do wish to be baptized. | the godfather | 1972 |
| 28794.0 | Sonny was hot for my deal, right? You know it's the smart thing to do, too. I want you to talk Sonny into it. :-()-: Sonny will come after you with everything he's got. | the godfather | 1972 |
| 28758.0 | Have you thought about a wife? A family? :-()-: No. :-()-: I understand, Michael. But you must make a family, you know. :-()-: I want children, I want a family. But I don't know when. :-()-: Accept what's happened, Michael. :-()-: I could accept everything that's happened; I could accept it, but that I never had a choice. From the time I was born, you had laid this all out for me. :-()-: No, I wanted other things for you. :-()-: You wanted me to be your son. :-()-: Yes, but sons who would be professors, scientists, musicians...and grandchildren who could be, who knows, a Governor, a President even, nothing's impossible here in America. :-()-: Then why have I become a man like you? :-()-: You are like me, we refuse to be fools, to be puppets dancing on a string pulled by other men. I hoped the time for guns and killing and massacres was over. That was my misfortune. That was your misfortune. I was hunted on the streets of Corleone when I was twelve years old because of who my father was. I had no choice. :-()-: A man has to choose what he will be. I believe that. :-()-: What else do you believe in? | the godfather | 1972 |
| 28809.0 | If he's your brother, why does he have a different name? :-()-: My brother Sonny found him living in the streets when he was a kid, so my father took him in. He's a good lawyer. | the godfather | 1972 |
| 28807.0 | I never know when you're telling me the truth. :-()-: I told you you wouldn't like him. :-()-: He's coming over here! | the godfather | 1972 |
| 28719.0 | You fingered Sonny for the Barzini people. That little farce you played out with my sister. Did Barzini kid you that would fool a Corleone? :-()-: I swear I'm innocent. I swear on the head of my children, I'm innocent. Mike, don't do this to me, please Mike, don't do this to me! :-()-: Barzini is dead. So is Philip Tattaglia, so are Strachi, Cuneo and Moe Greene...I want to square all the family accounts tonight. So don't tell me you're innocent; admit what you did. | the godfather | 1972 |
| 28798.0 | Sure. Anything I can do for you. :-()-: No. I guess I'll see you Christmas. Everyone's going to be out at Long Beach, right? :-()-: Right. | the godfather | 1972 |
| 28792.0 | Will you give this to him. :-()-: If I accept that letter and you told a Court of Law I accepted it, they would interpret it as my having knowledge of his whereabouts. Just wait Kay, he'll contact you. | the godfather | 1972 |
| 28822.0 | Your sister wants to ask you something. :-()-: Let HER ask. | the godfather | 1972 |
| 28784.0 | Was there a definite proposal? :-()-: Sure, he wants us to send Mike to meet him to hear his proposition. The promise is the deal will be so good we can't refuse. :-()-: What about that Tattaglias? What will they do about Bruno? :-()-: Part of the deal: Bruno cancels out what they did to my father. :-()-: We should hear what they have to say. :-()-: No, no Consiglere. Not this time. No more meetings, no more discussions, no more Sollozzo tricks. Give them one message: I WANT SOLLOZZO. If not, it's all out war. We go to the mattresses and we put all the button men out on the street. :-()-: The other families won't sit still for all out war. :-()-: Then THEY hand me Sollozzo. :-()-: Come ON Sonny, your father wouldn't want to hear this. This is not a personal thing, this is Business. :-()-: And when they shot me father... :-()-: Yes, even the shooting of your father was business, not personal... :-()-: No no, no more advice on how to patch it up Tom. You just help me win. Understood? | the godfather | 1972 |
| 28846.0 | Barzini's people chisel my territory and we do nothing about it. Pretty soon there won't be one place in Brooklyn I can hang my hat. :-()-: Just be patient. :-()-: I'm not asking you for help, Mike. Just take off the handcuffs. :-()-: Be patient. | the godfather | 1972 |
| 28786.0 | One of Tattaglia's people? :-()-: No. Our informer in McCluskey's precinct. Tonight at 8:00 he signed out for Louis' Restaurant in the Bronx. Anyone know it. | the godfather | 1972 |
| 28754.0 | When I meet with Tattaglia's people; should I insist that all his drug middle-men be clean? :-()-: Mention it, don't insist. Barzini is a man who will know that without being told. :-()-: You mean Tattaglia. :-()-: Barzini. :-()-: He was the one behind Sollozzo? :-()-: Tattaglia is a pimp. He could never have outfought Santino. But I wasn't sure until this day. No, it was Barzini all along. | the godfather | 1972 |
| 28760.0 | I see you have your Luca Brasi. :-()-: I'll need him. :-()-: There are men in this world who demand to be killed. They argue in gambling games; they jump out of their cars in a rage if someone so much as scratches their fender. These people wander through the streets calling out "Kill me, kill me." Luca Brasi was like that. And since he wasn't scared of death, and in fact, looked for it...I made him my weapon. Because I was the only person in the world that he truly hoped would not kill him. I think you have done the same with this man. | the godfather | 1972 |
| 28802.0 | Christ, Tom; I needed more time with him. I really needed him. :-()-: Did he give you his politicians? :-()-: Not all...I needed another four months and I would have had them all. I guess you've figured it all out? :-()-: How will they come at you? :-()-: I know now. I'll make them call me Don. :-()-: Have you agreed on a meeting? :-()-: A week from tonight. In Brooklyn on Tessio's ground, where I'll be safe. | the godfather | 1972 |
| 28771.0 | It's the real Thunderbolt, then. :-()-: Come Sunday morning: My name is Vitelli and my house is up there on the hill, above the village. | the godfather | 1972 |
| 28829.0 | All right, Mikey...who do we have to hit, Clemenza or Paulie? :-()-: What? :-()-: One of them fingered the old man. | the godfather | 1972 |
| 28795.0 | The Don was slipping; in the old days I could never have gotten to him. Now he's dead, nothing can bring him back. Talk to Sonny, talk to the Caporegimes, Clemenza and Tessio...it's good business. :-()-: Even Sonny won't be able to call off Luca Brasi. :-()-: I'll worry about Luca. You take care of Sonny and the other two kids. :-()-: I'll try...It's what the Don would want us to do. :-()-: Good...then you can go... I don't like violence. I'm a businessman, and blood is a big expense. | the godfather | 1972 |
| 28715.0 | What do you wish me to do? :-()-: I want you to use all your powers, all your skill, as you love me. I do not want his mother to see him as he is. | the godfather | 1972 |
| 28757.0 | What was this for? :-()-: For bravery. :-()-: And this? :-()-: For killing a man. :-()-: What miracles you do for strangers. :-()-: I fought for my country. It was my choice. :-()-: And now, what do you choose to do? :-()-: I'm going to finish school. :-()-: Good. When you are finished, come and talk to me. I have hopes for you. | the godfather | 1972 |
| 28741.0 | My compliments. I'll take care of them from my share. :-()-: So. I receive 30 per cent just for finance and legal protection. No worries about operations, is that what you tell me? :-()-: If you think two million dollars in cash is just finance, I congratulate you Don Corleone. | the godfather | 1972 |
| 28851.0 | The Corleone family wants to buy me out. I buy you out. You don't buy me out. :-()-: Your casino loses money. Maybe we can do better. :-()-: You think I scam? :-()-: You're unlucky. :-()-: You goddamn dagos. I do you a favor and take Freddie in when you're having a bad time, and then you try to push me out. :-()-: You took Freddie in because the Corleone family bankrolled your casino. You and the Corleone family are evened out. This is for business; name your price. :-()-: The Corleone family don't have that kind of muscle anymore. The Godfather is sick. You're getting chased out of New York by Barzini and the other families, and you think you can find easier pickings here. I've talked to Barzini; I can make a deal with him and keep my hotel! :-()-: Is that why you thought you could slap Freddie around in public? | the godfather | 1972 |
| 28844.0 | Do you renounce Satan. :-()-: I do renounce him. :-()-: And all his works? :-()-: I do renounce them. | the godfather | 1972 |
| 28825.0 | Michael, it's not true. Please tell me. :-()-: Don't ask me. :-()-: Tell me! :-()-: All right, this one time I'll let you ask about my affairs, one last time. :-()-: Is it true? | the godfather | 1972 |
| 28841.0 | Most important...I want a sure guarantee that no more attempts will be made on my father's life. :-()-: What guarantees can I give you? I am the hunted one. I've missed my chance. You think too highly of me, my friend...I am not so clever...all I want if a truce... | the godfather | 1972 |
| 28789.0 | We'll let the old man take it easy for a couple of weeks. I want to get things going good before he gets better. What's the matter with you? :-()-: You start operating, the five families will start their raids again. We're at a stalemate Sonny, your war is costing us a lot of money. :-()-: No more stalemate Tom, we got the soldiers, we'll match them gun for gun if that's how they want it. They know me for what I am, Tom-- and they're scared of me. :-()-: Yes. That's true, you're getting a hell of a reputation. :-()-: Well it's war! We might not be in this shape if we had a real war- time Consiglere, a Sicilian. Pop had Genco, who do I have? Hey Tom, hey...hey. It's Sunday, we're gonna have dinner. Don't be sore. | the godfather | 1972 |
| 28812.0 | What will your father say? :-()-: As long as I tell him beforehand he won't object. He'll be hurt, but he won't object. :-()-: What time do they expect us? :-()-: For dinner. Unless I call and tell them we're still in New Hampshire. :-()-: Michael. :-()-: Then we can have dinner, see a show, and spend one more night. | the godfather | 1972 |
| 28793.0 | Will you give this letter to Michael. :-()-: Mama, no. | the godfather | 1972 |
| 28726.0 | You heard about your father? :-()-: Yeah. :-()-: The word is out in the streets that he's dead. :-()-: Where the hell was Paulie, why wasn't he with the Don? :-()-: Paulie's been a little sick all winter...he was home. :-()-: How many times did he stay home the last couple of months? :-()-: Maybe three, four times. I always asked Freddie if he wanted another bodyguard, but he said no. Things have been so smooth the last ten years... :-()-: Go get Paulie, I don't care how sick he is. Pick him up yourself, and bring him to my father's house. :-()-: That's all? Don't you want me to send some people over here? :-()-: No, just you and Paulie. | the godfather | 1972 |
| 28788.0 | Pop, they hit us and we hit them back. :-()-: We put out a lot of material through our contacts in the Newspapers...about McCluskey's being tied up with Sollozzo in the Drug Rackets...things are starting to loosen up. | the godfather | 1972 |
| 28716.0 | Understood. I just wish I was doing more to help out. :-()-: I'll come to you when I need you. | the godfather | 1972 |
| 28783.0 | Maybe Mike shouldn't get mixed up in this so directly. You know the old man doesn't want that. :-()-: OK forget it, just stay on the phone. | the godfather | 1972 |
| 28736.0 | Outside. :-()-: Sure. | the godfather | 1972 |
| 28773.0 | You look wonderful, kid; really wonderful. That doctor did some job on your face. :-()-: You look good, too. | the godfather | 1972 |
| 28776.0 | Who are those girls? :-()-: That's for you to find out. :-()-: Give them some money and send them home. :-()-: Mike! :-()-: Get rid of them... | the godfather | 1972 |
| 28810.0 | I didn't know your family knew Johnny Fontane. :-()-: Sure. :-()-: I used to come down to New York whenever he sang at the Capitol and scream my head off. :-()-: He's my father's godson; he owes him his whole career. | the godfather | 1972 |
| 28804.0 | Michael, what are those men doing? :-()-: They're waiting to see my father. :-()-: They're talking to themselves. :-()-: They're going to talk to my father, which means they're going to ask him for something, which means they better get it right. :-()-: Why do they bother him on a day like this? :-()-: Because they know that no Sicilian will refuse a request on his daughter's wedding day. | the godfather | 1972 |
| 28787.0 | Jesus, I don't know... :-()-: Can you do it Mike? | the godfather | 1972 |
| 28833.0 | Sonny...Sonny--Jesus Christ, I'm down at the hospital. I came down late. There's no one here. None of Tessio's people--no detectives, no one. The old man is completely unprotected. :-()-: All right, get him in a different room; lock the door from the inside. I'll have some men there inside of fifteen minutes. Sit tight, and don't panic. :-()-: I won't panic. | the godfather | 1972 |
| 28747.0 | Is it necessary? :-()-: You understand him better than anyone. | the godfather | 1972 |
| 28849.0 | Mike, good to see you. Got everything you want? :-()-: Thanks. :-()-: The chef cooked for you special; the dancers will kick your tongue out and you credit is good! Draw chips for all these people so they can play on the house. :-()-: Is my credit good enough to buy you out? | the godfather | 1972 |
| 28834.0 | Mikey, you look beautiful! :-()-: Cut it out. :-()-: The Turk wants to talk! The nerve of that son of a bitch! After he craps out last night he wants a meet. | the godfather | 1972 |
| 28714.0 | Be my friend. :-()-: Good. From me you'll get Justice. :-()-: Godfather. :-()-: Some day, and that day may never come, I would like to call upon you to do me a service in return. | the godfather | 1972 |
| 28768.0 | Hey, beautiful girls! :-()-: Shhhhh. | the godfather | 1972 |
| 28821.0 | I have to see my father and his people when we get back to the Mall. :-()-: Oh Michael. :-()-: We'll go to the show tomorrow night--we can change the tickets. :-()-: Don't you want dinner first? :-()-: No, you eat...don't wait up for me. :-()-: Wake me up when you come to bed? | the godfather | 1972 |
| 28718.0 | Godfather! :-()-: You have to answer for Santino. | the godfather | 1972 |
| 28750.0 | It is Johnny. He came all the way from California to be at the wedding. :-()-: Should I bring him in. :-()-: No. Let the people enjoy him. You see? He is a good godson. :-()-: It's been two years. He's probably in trouble again. | the godfather | 1972 |
| 28847.0 | Let us fill up our Regimes. :-()-: No. I want things very calm for another six months. :-()-: Forgive me, Godfather, let our years of friendship be my excuse. How can you hope for success there without your strength here to back you up? The two go hand in hand. And with you gone from here the Barzini and the Tattaglias will be too strong for us. | the godfather | 1972 |
| 28791.0 | What was that? :-()-: An accident. No one was hurt. :-()-: Listen Tom, I let my cab go; can I come in to call another one? | the godfather | 1972 |
| 28763.0 | ...a fine boy from Sicily, captured by the American Army, and sent to New Jersey as a prisoner of war... :-()-: Nazorine, my friend, tell me what I can do. :-()-: Now that the war is over, Enzo, this boy is being repatriated to Italy. And you see, Godfather... He...my daughter...they... :-()-: You want him to stay in this country. :-()-: Godfather, you understand everything. :-()-: Tom, what we need is an Act of Congress to allow Enzo to become a citizen. :-()-: An Act of Congress! | the godfather | 1972 |
| 28826.0 | I'm Michael Corleone--this is my father. What happened to the detectives who were guarding him? :-()-: Oh your father just had too many visitors. It interfered with the hospital service. The police came and made them all leave just ten minutes ago. But don't worry. I look in on him. :-()-: You just stand here one minute... | the godfather | 1972 |
| 28853.0 | I have to go back to New York tomorrow. Think of your price. :-()-: You son of a bitch, you think you can brush me off like that? I made my bones when you were going out with cheerleaders. | the godfather | 1972 |
| 28831.0 | Is it going to be all-out war, like last time? :-()-: Until the old man tells me different. :-()-: Then wait, Sonny. Talk to Pop. :-()-: Sollozzo is a dead man, I don't care what it costs. I don't care if we have to fight all the five families in New York. The Tattaglia family's going to eat dirt. I don't care if we all go down together. :-()-: That's not how Pop would have played it. :-()-: I know I'm not the man he was. But I'll tell you this and he'll tell you too. When it comes to real action, I can operate as good as anybody short range. :-()-: All right, Sonny. All right. :-()-: Christ, if I could only contact Luca. :-()-: Is it like they say? Is he that good? | the godfather | 1972 |
| 28737.0 | I'll think about it. :-()-: Drive while you thinking; I wanna get to the City this month! | the godfather | 1972 |
| 28805.0 | No. His name is Luca Brasi. You wouldn't like him. :-()-: Who is he? :-()-: You really want to know? :-()-: Yes. Tell me. :-()-: You like spaghetti? :-()-: You know I love spaghetti. :-()-: Then eat your spaghetti and I'll tell you a Luca Brasi story. | the godfather | 1972 |
| 28734.0 | You look terrif on the floor! :-()-: What are you, a dance judge? Go do your job; take a walk around the neighborhood... see everything is okay. | the godfather | 1972 |
| 28790.0 | Kay, we weren't expecting you. You should call... :-()-: I've tried calling and writing. I want to reach Michael. :-()-: Nobody knows where he is. We know he's all right, but that's all. | the godfather | 1972 |
| 28811.0 | We have something for your mother, for Sonny, we have the tie for Fredo and Tom Hagen gets the Reynolds pen... :-()-: And what do you want for Christmas? :-()-: Just you. | the godfather | 1972 |
| 28850.0 | Buy me out?... :-()-: The hotel, the casino. The Corleone family wants to buy you out. | the godfather | 1972 |
| 28813.0 | Michael, what are you doing? :-()-: Shhh, you be the long distance operator. Here. :-()-: Hello...this is Long Distance. I have a call from New Hampshire. Mr. Michael Corleone. One moment please. | the godfather | 1972 |
| 28725.0 | You filthy guinea spoiled brat. Clean it up or I'll kick your head in. :-()-: Like hell I will. | the godfather | 1972 |
| 28710.0 | This is Tom Hagen; I'm calling for Don Corleone, at his request. :-()-: Yes, I understand I'm listening. :-()-: You owe the Don a service. He has no doubt that you will repay it. | the godfather | 1972 |
| 28854.0 | Yeah. :-()-: Do you recognize my voice? :-()-: I think so. Detective squad? :-()-: Right. Don't say my name, just listen. Somebody shot your father outside his place fifteen minutes ago. :-()-: Is he alive? :-()-: I think so, but I can't get close enough. There's a lot of blood. I'll try to find out more. :-()-: Find out anything you can...you got a Grand coming. | the godfather | 1972 |
| 28731.0 | Mostly it gives witnesses an excuse to change their identification when we make them see the light. Then you take a long vacation and we catch the hell. :-()-: How bad will it be? :-()-: Probably all the other families will line up against us. But, it's alright. These things have to happen once every ten years or so...gets rid of the bad blood. You gotta stop 'em at the beginning. Like they shoulda stopped Hitler at Munich, they shoulda never let him get away with that, they were just asking for big trouble... | the godfather | 1972 |
| 28748.0 | I'm sure it's the most generous gift today. :-()-: The Senator called--apologized for not coming personally, but said you'd understand. Also, some of the Judges...they've all sent gifts. And another call from Virgil Sollozzo. | the godfather | 1972 |
| 28772.0 | Why didn't Moe Green meet us at the airport? :-()-: He had business at the hotel, but he'll drop in for dinner. | the godfather | 1972 |
| 28819.0 | Visiting hour ends at eight thirty. I'll just sit with him; I want to show respect. :-()-: Can I go to the hospital with you? :-()-: I don't think so. You don't want to end up on page 3 of the Daily News. :-()-: My parents don't read the Daily News. All right, if you think I shouldn't. I can't believe the things the papers are printing. I'm sure most of it's not true. :-()-: I don't think so either. I better go. :-()-: When will I see you again? :-()-: I want you to go back to New Hampshire...think things over. | the godfather | 1972 |
| 28828.0 | I was worried when we couldn't get in touch with you in that hick town. :-()-: How's Mom? :-()-: Good. She's been through it before. Me too. You were too young to know about it. You better wait outside; there're some things you shouldn't hear. :-()-: I can help you out... :-()-: Oh no you can't, the old man'd be sore as hell if I let you get mixed up in this. :-()-: Jesus Christ, he's my father, Sonny. :-()-: Theresa. | the godfather | 1972 |
| 28713.0 | You never think to protect yourself with real friends. You think it's enough to be an American. All right, the Police protects you, there are Courts of Law, so you don't need a friend like me. But now you come to me and say Don Corleone, you must give me justice. And you don't ask in respect or friendship. And you don't think to call me Godfather; instead you come to my house on the day my daughter is to be married and you ask me to do murder...for money. :-()-: America has been good to me... :-()-: Then take the justice from the judge, the bitter with the sweet, Bonasera. But if you come to me with your friendship, your loyalty, then your enemies become my enemies, and then, believe me, they would fear you... | the godfather | 1972 |
| 28823.0 | Why are you so cold to her and Carlo? They live with us on the Mall now, but you never get close to them. :-()-: I'm busy. :-()-: Connie and Carlo want you to be godfather to their little boy. | the godfather | 1972 |
| 28739.0 | You think we'll go for that last place? :-()-: Maybe, or you gotta know now. :-()-: Holy cow, I don't gotta know nothing. | the godfather | 1972 |
| 28722.0 | What was that? :-()-: Your girl friend. She says she can't make it tonight. You lousy bastard you have the nerve to give your whores my telephone number. I'll kill you, you bastard! | the godfather | 1972 |
| 28755.0 | Tom, I never thought you were a bad Consigliere, I thought Santino a bad Don, rest in peace. He had a good heart but he wasn't the right man to head the family when I had my misfortune. Michael has all my confidence, as you do. For reasons which you can't know, you must have no part in what will happen. :-()-: Maybe I can help. | the godfather | 1972 |
| 28775.0 | Mike! The party starting! :-()-: Come here a minute, Fredo. | the godfather | 1972 |
| 28818.0 | I can't... :-()-: Please say it. :-()-: Look. I'll see you tonight, OK? :-()-: OK. | the godfather | 1972 |
| 28765.0 | That is your birthright...but Michael, use this car. :-()-: No...I would like to walk to Corleone. | the godfather | 1972 |
| 28799.0 | What about McCluskey? :-()-: Let's say now that we have to kill McCluskey. We'll clear that up through our Newspaper contacts later. | the godfather | 1972 |
| 28837.0 | Why don't you stop living like a bum and get this place cleaned up. :-()-: What are you, inspecting the barracks? You ready? Did Clemenza tell you be sure to drop the gun right away? :-()-: A million times. :-()-: Sollozzo and McCluskey are going to pick you up in an hour and a half on Times Square, under the big Camels sign. | the godfather | 1972 |
| 28796.0 | Mr. Corleone is Johnny's Godfather. That is very close, a very sacred religious relationship. :-()-: Okay, but just tell him this is one favor I can't give. But he should try me again on anything else. :-()-: He never asks a second favor when he has been refused the first. Understood? :-()-: You smooth son of a bitch, let me lay it on the line for you, and your boss. Johnny Fontane never gets that movie. I don't care how many Dago, Guinea, wop Greaseball Goombahs come out of the woodwork! :-()-: I'm German-Irish. :-()-: Okay my Kraut-Mick friend, Johnny will never get that part because I hate that pinko punk and I'm going to run him out of the Movies. And I'll tell you why. He ruined one of Woltz Brothers' most valuable proteges. For five years I had this girl under training; singing lessons! Acting lessons! Dancing lessons! We spent hundreds of thousands of dollars--I was going to make her a star. I'll be even more frank, just to show you that I'm not a hard-hearted man, that it wasn't all dollars and cents. That girl was beautiful and young and innocent and she was the greatest piece of ass I've ever ad and I've had them all over the world. Then Johnny comes along with that olive oil voice and guinea charm and she runs off. She threw it all away to make me look ridiculous. A MAN IN MY POSITION CANNOT AFFORD TO BE MADE TO LOOK RIDICULOUS! | the godfather | 1972 |
| 28709.0 | Anything...Anything the Godfather wishes. :-()-: Good. He never doubted you. :-()-: The Don himself is coming to me tonight? :-()-: Yes. | the godfather | 1972 |
| 28746.0 | You look terrible. I want you to eat well, to rest. And spend time with your family. And then, at the end of the month, this big shot will give you the part you want. :-()-: It's too late. All the contracts have been signed, they're almost ready to shoot. :-()-: I'll make him an offer he can't refuse. | the godfather | 1972 |
| 28800.0 | Mike, why are you cutting me out of the action? :-()-: Tom, we're going to be legitimate all the way, and you're the legal man. What could be more important than that. :-()-: I'm not talking about that. I'm talking about Rocco Lampone building a secret regime. Why does Neri report directly to you, rather than through me or a caporegime? | the godfather | 1972 |
| 28740.0 | My business is heroin, I have poppy fields, laboratories in Narseilles and Sicily, ready to go into production. My importing methods are as safe as these things can be, about five per cent loss. The risk is nothing, the profits enormous. :-()-: Why do you come to me? Why do I deserve your generosity? :-()-: I need two million dollars in cash...more important, I need a friend who has people in high places; a friend who can guarantee that if one of my employees be arrested, they would get only light sentences. Be my friend. :-()-: What percentages for my family? :-()-: Thirty per cent. In the first year your share would be four million dollars; then it would go up. :-()-: And what is the percentage of the Tattaglia family? | the godfather | 1972 |
| 28742.0 | No...how a man makes his living is none of my business. But this proposition of yours is too risky. All the people in my family lived well the last ten years, I won't risk that out of greed. :-()-: Are you worried about security for your million? :-()-: No. :-()-: The Tattaglias will guarantee your investment also. | the godfather | 1972 |
| 28830.0 | Clemenza? No, I don't believe it. :-()-: You're right, kid, Clemenza is okay. It was Paulie. :-()-: How can you be sure? :-()-: On the three days Paulie was sick this month, he got calls from a payphone across from the old man's building. We got people in the phone company. Thank God it was Paulie...we'll need Clemenza bad. | the godfather | 1972 |
| 28711.0 | Bonasera, we know each other for years, but this is the first time you come to me for help. I don't remember the last time you invited me to your house for coffee...even though our wives are friends. :-()-: What do you want of me? I'll give you anything you want, but do what I ask! :-()-: And what is that Bonasera? | the godfather | 1972 |
| 28712.0 | No. You ask for too much. :-()-: I ask for Justice. :-()-: The Court gave you justice. :-()-: An eye for an eye! :-()-: But your daughter is still alive. :-()-: Then make them suffer as she suffers. How much shall I pay you. | the godfather | 1972 |
| 28848.0 | Barzini wants to arrange a meeting. Says we can straighten any of our problems out. :-()-: He talked to you? :-()-: I can arrange security. | the godfather | 1972 |
| 28769.0 | Get the car. I'll be leaving in ten minutes. Where's Calo? :-()-: Calo is having a cup of coffee in the kitchen. Is your wife coming with you? :-()-: No, she's going home to her family. She'll join me in a few weeks... | the godfather | 1972 |
| 28759.0 | I told you that it wouldn't escape his eye. :-()-: How did you find out? | the godfather | 1972 |
| 28723.0 | You're staying home. You're not going out. :-()-: OK, OK. You gonna make me something to eat at least? | the godfather | 1972 |
| 28764.0 | Don Tommassino. :-()-: Michael, why must you do this. We have been lucky so far, all these months you've been here we've kept your name a secret. It is from love for your father that I've asked you never to more than an hour from the Villa. :-()-: Calo and Fabrizzio are with me; nothing will happen. :-()-: You must understand that your Father's enemies have friends in Palermo. :-()-: I know. :-()-: Where are you going? :-()-: Corleone. :-()-: There is nothing there. Not anymore. :-()-: I was told that my Grandfather was murdered on its main street; and his murderers came to kill my father there when he was twelve years old. :-()-: Long ago. Now there is nothing: the men killed each other in family vendettas...the others escaped to America. :-()-: Don Tommassino...I should see this place. | the godfather | 1972 |
| 28732.0 | We gotta fight sometime. Let us at least recruit our regimes to full strength. :-()-: No, I don't want to give Barzini an excuse to start fighting. | the godfather | 1972 |
| 28749.0 | He's his own boss, and very competent. :-()-: And with prison record. :-()-: Two terms; one in Italy, one in the United States. He's known to the Government as a top narcotics man. That could be a plus for us; he could never get immunity to testify. :-()-: When did he call? :-()-: This morning. :-()-: On a day like this. Consiglero, do you also have in your notes the the Turk made his living from Prostitution before the war, like the Tattaglias do now. Write that down before you forget it. The Turk will wait. | the godfather | 1972 |
| 28838.0 | O.K. How long do you think before I can come back? :-()-: Probably a year... | the godfather | 1972 |
| 28779.0 | The old man wants you; Johnny's here...he's got a problem. :-()-: Okay. One minute. | the godfather | 1972 |
| 28808.0 | Tom...Tom, I'd like you to meet Kay Adams. :-()-: How do you do. :-()-: My brother, Tom Hagen. | the godfather | 1972 |
| 28832.0 | Where are you going? :-()-: To the city. :-()-: Send some bodyguards. :-()-: I don't need them, Sonny. I'm just going to see Pop in the hospital. Also, I got other things. | the godfather | 1972 |
| 28708.0 | Yes, I understand. I'm listening. :-()-: You owe the Don a service. In one hour, not before, perhaps later, he will be at your funeral parlor to ask for your help. Be there to greet him. If you have any objections speak now, and I'll inform him. | the godfather | 1972 |
| 28727.0 | Clemenza. You take care of Paulie. I don't ever want to see him again. Understood? :-()-: Understood. :-()-: Okay, now you can move your men into the Mall, replace Tessio's people. Mike, tomorrow you take a couple of Clemenza's people and go to Luca's apartment and wait for him to show. That crazy bastard might be going after Sollozzo right now if he's heard the news. | the godfather | 1972 |
| 28743.0 | I kept trying to call you after my divorce and Tom always said you were busy. When I got the Wedding invitation I knew you weren't sore at me anymore, Godfather. :-()-: Can I do something for you still? You're not too rich, or too famous that I can't help you? :-()-: I'm not rich anymore, Godfather, and...my career, I'm almost washed up... | the godfather | 1972 |
| 28806.0 | Once upon a time, about fifteen years ago some people wanted to take over my father's olive oil business. They had Al Capone send some men in from Chicago to kill my father, and they almost did. :-()-: Al Capone! :-()-: My Father sent Luca Brasi after them. He tied the two Capone men hand and foot, and stuffed small bath towels into their mouths. Then he took an ax, and chopped one man's feet off... :-()-: Michael... :-()-: Then the legs at the knees... :-()-: Michael you're trying to scare me... :-()-: Then the thighs where they joined the torso. :-()-: Michael, I don't want to hear anymore... :-()-: Then Luca turned to the other man... :-()-: Michael, I love you. :-()-: ...who out of sheer terror had swallowed the bath towel in his mouth and suffocated. | the godfather | 1972 |
| 28824.0 | Will you? :-()-: Let me think about it, O.K.? | the godfather | 1972 |
| 28756.0 | Will your girl friend get back to the city all right? :-()-: Tom said he'd take care of it. | the godfather | 1972 |
| 28733.0 | He's going to be our lawyer in Vegas. Nobody goes to him with any other business as of now, this minute. No reflection on Tom; that's the way I want it. Besides, if I ever need any advice, who's a better Consigliere than my father. :-()-: Then in a six month time we're on our own; is that it? :-()-: Maybe less... | the godfather | 1972 |
| 28770.0 | You had better bring a few bottles home with you, my friend; you'll need help sleeping tonight. :-()-: This one could seduce the devil. A body! and eyes as big and black as olives. :-()-: I know about what you mean! :-()-: This was a beauty. Right, Calo? :-()-: Beautiful all over, eh? :-()-: And hair. Black and curly, like a doll. And such a mouth. | the godfather | 1972 |
| 28777.0 | Mike, you sure about Moe selling. He never mentioned it to me and he loves the business. :-()-: I'll make him an offer he can't refuse. | the godfather | 1972 |
| 28778.0 | Tom, you're the Consigliere; you can talk to the Don and advise him. :-()-: The Don has semi-retired. I'm running the Family business now. So anything you have to say, say it to me. | the godfather | 1972 |
| 28801.0 | Bookkeepers know everything. Rocco's men are all a little too good for the jobs they're supposed to be doing. They get a little more money than the job's worth. Lampone's a good man; he's operating perfectly. :-()-: Not so perfectly if you noticed. :-()-: Mike, why am I out? :-()-: You're not a wartime Consigliere. Things may get tough with the move we're trying. :-()-: OK, but then I agree with Tessio. You're going about it all wrong; you're making the move out of weakness... Barzini's a wolf, and if he tears you apart, the other families won't come running to help the Corleones... | the godfather | 1972 |
| 15850.0 | Hello, Peter. :-()-: You sonofabitch! :-()-: Emotional? I expected more from you. :-()-: If you kill tonight and I'm in jail the police will know I'm innocent. :-()-: By that time the game will be over. :-()-: I've figured out the message. :-()-: No you haven't. And even if you had it doesn't matter. Who would you tell? The police don't believe you, and you've just used your only phone call. | knight moves | 1992 |
| 15808.0 | Yes you are. :-()-: Get off me! | knight moves | 1992 |
| 15810.0 | The message! I figured out-- :-()-: Sit down! | knight moves | 1992 |
| 15855.0 | How'd you know I wouldn't be in the same room with her? :-()-: You told me. When you called you said she was in the other room. Drop the knife. | knight moves | 1992 |
| 15806.0 | We're interested in where you were from the time you left the auditorium until you got there. :-()-: I was at the beach. | knight moves | 1992 |
| 15948.0 | --but you enjoy being the stronger one? You like the control. :-()-: If you're asking me if I'm passionate about what I do, the answer is yes. Without passion, nothing moves us. What's your passion? :-()-: That's a very personal question. :-()-: I see. This is going to be a very polite conversation. What shall we discuss? The weather? Movies? :-()-: Are you disappointed that I won't answer you? :-()-: I had just hoped there would be more substance to the conversation. :-()-: I thought opening too quickly was a fatal mistake in chess. :-()-: It is. :-()-: Do you always open quickly? :-()-: Are we talking about me, or chess? :-()-: You. :-()-: Each circumstance requires a different tactic. :-()-: Well, I hope you remember that tomorrow when you play Krikorian. | knight moves | 1992 |
| 15913.0 | Excuse me... you said earlier that Mary Albert just moved in. How long ago was that? :-()-: Ten days ago. :-()-: Do you know how she found the apartment? :-()-: Through a rental agency. :-()-: Didn't you tell me that Debi Rutlege had just moved into her place also? | knight moves | 1992 |
| 15804.0 | He's not talking about himself. :-()-: Then what's he talking about? :-()-: If we knew that we'd know the answer to the riddle, wouldn't we? He's telling us where he's going to kill tonight and we can't see it. | knight moves | 1992 |
| 15958.0 | No. :-()-: You're not? :-()-: No -- I'm not involved. And I plan on keeping it that way. | knight moves | 1992 |
| 15880.0 | What are you doing up? :-()-: I can't sleep. My beds lumpy. :-()-: I see. You forgot to bring you're night-light, didn't you? :-()-: That has nothing to do with it. :-()-: You want to sleep in my bed tonight? :-()-: Okay. | knight moves | 1992 |
| 15800.0 | You don't tell us how to run our investigation. You got that? :-()-: You don't have an investigation without me. You got that? | knight moves | 1992 |
| 15978.0 | Peter... :-()-: No, listen. You have no idea of the kind of pressure I'm under right now. :-()-: That's still no excuse. You treat everything like a game. :-()-: I can't think any more, unless it's about you. I'll be in the middle of a match and instead of thinking about my next move, I think about how you look when you smile. Remember how you said that I hide behind my chessboard? Ever since my wife died I've been... I've been afraid of getting too close to someone again -- afraid of losing them. :-()-: You're wife died. You can't feel responsible for that. :-()-: You don't understand. :-()-: Then help me to understand. I want to understand. :-()-: It's not that easy. :-()-: Of course it isn't. It's always difficult when someone you love dies. But you can't feel responsible because she had a car accident. :-()-: But I do. :-()-: Why? :-()-: Because she killed herself!! | knight moves | 1992 |
| 15866.0 | Computers. That's how you got into Homesearchers records. You can get into anything. But why? Why? :-()-: You still haven't figured it out, have you? You think that I've put you through an ordeal. My scars run so much deeper than yours. :-()-: What scars? :-()-: The scars on your chest. From where I stabbed you with my fountain pen. | knight moves | 1992 |
| 15946.0 | I love this hotel. I stay here every time I visit my parents. :-()-: How come you don't stay with them? :-()-: Because I love them, but they drive me crazy. You know how parents are? :-()-: No. I don't. | knight moves | 1992 |
| 15826.0 | You should be an actor, Frank. You looked like you were really mad. The veins popping out on your neck was a really nice touch. :-()-: You weren't mad at him for picking up the phone? | knight moves | 1992 |
| 15979.0 | This letter. I've never opened it. :-()-: Why not? :-()-: Because I know what it says. :-()-: Maybe you're afraid of what it says. | knight moves | 1992 |
| 15812.0 | Sorry to ruin your trip to the city, but we got a real nut on our hands, Frank. :-()-: Run it down. | knight moves | 1992 |
| 15795.0 | What do you mean, no? :-()-: He says it's a game. All games have a strategy. :-()-: All you gotta do is look at the map. :-()-: His victims aren't random. It only means that they appear to be random. There's a connection, we just have to find it. | knight moves | 1992 |
| 15889.0 | He tapes their mouths shut. We found traces of adhesive around the victims mouths. We're doing a chemical analysis for components, but it's probably a standard brand you can buy in any hardware store. :-()-: There's only two hardware stores on the whole Island. We'll check that out. What about the blood? :-()-: Not a drop. Maybe the guy works for the Red Cross. | knight moves | 1992 |
| 15917.0 | Why didn't you tell us you were there earlier? :-()-: I don't know. I was afraid there'd be hours of questions. I can't afford to miss a game. | knight moves | 1992 |
| 15975.0 | He's replaying the game I played against him move by move, using these girls as the Chess pieces. All the girls have been found in their homes except Christie Eastman, who was found in back of a warehouse. Why? :-()-: Because to follow the game he played he had to move to that grid? :-()-: Right. Here! c-4. | knight moves | 1992 |
| 15953.0 | It's going to be a long night. It could take hours before we know something. You should try to eat. :-()-: You sound like my mother. :-()-: It wasn't intentional. :-()-: Does what's going on bother you at all? Or are you just wearing your game face? | knight moves | 1992 |
| 15911.0 | Did you see his face? :-()-: No. He was wearing a mask... but I saw the cut on his wrist. It was Peter. :-()-: I can't arrest someone for having a cut on their wrist. Do you have someone you can stay with tonight? :-()-: I've got a room at the institute I use when I stay late. :-()-: Okay. Why don't you go out there. We'll check in with you later. | knight moves | 1992 |
| 15935.0 | You opened with the English. Lutz won't use it. He opened his first game with it. :-()-: He knows you're aware of that. :-()-: Then I'll transpose. :-()-: On a variation, yes -- but it must be a variation that is unique. :-()-: I suppose you have something in mind? | knight moves | 1992 |
| 15987.0 | Hello, Peter. You played an interesting game last night. Even though sacrificing your Queen at b-5 is the game I played against Valsney in '82. I'm glad it helped you. :-()-: I'm sure you are. :-()-: No, I want you to do good. That way when I beat you at the end I'll look that much better. :-()-: You're getting sloppy, Yurilivich. You're nervous? :-()-: I'm not nervous. :-()-: Well, you should be, because this time I'm going to win. :-()-: Well, then this time you'll have to stay for the whole match, won't you? | knight moves | 1992 |
| 15793.0 | What's that supposed to mean? :-()-: It means, if I were a killer and I thought the police were closing in on me I might invent someone to try and put them off the scent. :-()-: That's crazy! Why would I draw attention to myself like that? :-()-: You like to play games, don't you, Peter? :-()-: He says on the photo he'll call tomorrow at eleven. Why not come back then and listen for yourself. :-()-: Oh, we'll be back. You can bet on it. | knight moves | 1992 |
| 15799.0 | We didn't ask for your opinion, Doctor. :-()-: Maybe you should. | knight moves | 1992 |
| 15927.0 | As large as castles, you are still light as the air, one hundred men can't move me. It's posed as a question. What am I? :-()-: A building? :-()-: A building isn't as light as air. What's large, but as light as air and can't be moved? | knight moves | 1992 |
| 15883.0 | Don't you have something you want to say to David? :-()-: Thanks, David. | knight moves | 1992 |
| 15928.0 | He's not going to give us direct hints. He's going to skirt around it. :-()-: He uses castles...plural -- then says "can't move me." Singular. Not can't move us. | knight moves | 1992 |
| 15970.0 | Did you call? :-()-: The line was busy. I'll try again. | knight moves | 1992 |
| 15862.0 | D2-d4... b1-c3... and c1-f4. It's the number two variation of the Tarakoss opening. David, can you bring up the tournament records for the last ten years. :-()-: Mister Sanderson, they'll be hundreds of thousands of games. | knight moves | 1992 |
| 15955.0 | I'm just curious. :-()-: I think it's best if we don't ask too many personal questions -- I want to keep things on a professional level. :-()-: You mean like in the steam room? :-()-: That's not fair! :-()-: Are you going to tell me you didn't feel something in there? | knight moves | 1992 |
| 15930.0 | Where were you last night? :-()-: Are we going to go through this again? :-()-: Answer the question. :-()-: I went out. | knight moves | 1992 |
| 15920.0 | What have you come up with on the riddle? :-()-: "Wee Willie Winkie runs though the street." We think he might be making a reference to himself. :-()-: Maybe, but I don't think so. I think it's just a tease. :-()-: Upstairs and downstairs in his night gown -- He could be saying the house he's picked is two stories. | knight moves | 1992 |
| 15859.0 | What's this? :-()-: Oh, I put a few games on for your daughter. I hope you don't mind. :-()-: Of course not. | knight moves | 1992 |
| 15964.0 | Nobody could attack you. You set your life up like one of your chessboards. You're impregnable -- but at the same time you've become trapped behind your own defenses. You're cut off from everyone around you. :-()-: What are you talking about? You don't even know me. :-()-: Does anyone? | knight moves | 1992 |
| 15874.0 | Is your dad here? :-()-: He went down to the lobby for a minute. He should be right back. Would you like come in? | knight moves | 1992 |
| 15915.0 | Could you tell me where you were last night? :-()-: Here. I played Gregory Lutz. :-()-: I know. You won two games against him and left the auditorium at eight forty five. Where did you go after that? :-()-: To my room and then I went for a walk. | knight moves | 1992 |
| 15887.0 | There's no blood. Where's the fuckin' blood? :-()-: Bingo. | knight moves | 1992 |
| 15875.0 | So, are you having a good time on the Island? :-()-: Not really. It's pretty boring. :-()-: That's only because you don't know where to go. You like hiking? Fishing? Sailing? What do you like? :-()-: Boys. | knight moves | 1992 |
| 15877.0 | I know he really likes you. :-()-: How do you know that? Did he say something? :-()-: No... but I can tell. A woman knows these things. | knight moves | 1992 |
| 15905.0 | Each of those grids represents almost a square mile. :-()-: That's a big area to cover. | knight moves | 1992 |
| 15934.0 | Did you send the car? :-()-: It's over, Peter. Let's stop the games. I'm not sending a car. | knight moves | 1992 |
| 15937.0 | We got a problem? :-()-: Our computer went on the fritz again. David came up to fix it. :-()-: Is it serious? | knight moves | 1992 |
| 15856.0 | This isn't going to help, David. You're mother's dead. You can't undo it. :-()-: You don't understand. :-()-: Yes, I do. I found your file. I know what happened. :-()-: They took away the game because of him. My father left and my mother... There was so much blood... It covered everything. :-()-: I know, but this isn't going to bring her back. | knight moves | 1992 |
| 15829.0 | A few people. :-()-: Did anyone stand out? :-()-: What do you mean, stand out? :-()-: Did anyone look suspicious? Think! :-()-: Now that you mention it there was somebody who looked suspicious. :-()-: What was suspicious about him? | knight moves | 1992 |
| 15907.0 | What time did he get there? :-()-: About a quarter to one. | knight moves | 1992 |
| 15792.0 | Casual. :-()-: Casual? You were boning her weren't you? :-()-: It wasn't serious. What's your problem? :-()-: You are! I don't like you. :-()-: Fine, don't ask me out on a date. | knight moves | 1992 |
| 15884.0 | You ready? :-()-: For what? :-()-: You told me you'd take me over to Seattle today. :-()-: I'm sorry, honey, I can't. Not today. :-()-: But you promised. | knight moves | 1992 |
| 15794.0 | Meaning, it looks like his victims are chosen at random. :-()-: No. | knight moves | 1992 |
| 15918.0 | Mister Sanderson, this is Doctor Sheppard. She's a psychologist helping us out. :-()-: We've already met. Haven't we... Doctor. | knight moves | 1992 |
| 15982.0 | You're right. The killer told me. :-()-: He didn't tell you either. | knight moves | 1992 |
| 15816.0 | Take it easy. Both of you. :-()-: I'm sorry, this is just too convenient. | knight moves | 1992 |
| 15843.0 | And you want these girls to feel your pain? :-()-: Please, I don't want to get into the psychological aspects of my actions. It would detract from the game. :-()-: How? :-()-: I couldn't say it any better than Huxley. | knight moves | 1992 |
| 15921.0 | That's it? :-()-: We think he might be making a reference to drugs? Miss Emma is a street term used by junkies for Morphine. :-()-: Could Emma be the name of the girl he's going to kill? | knight moves | 1992 |
| 15981.0 | How did you know it was "carefully"? :-()-: Frank told me. :-()-: No he didn't. | knight moves | 1992 |
| 15822.0 | How is she? :-()-: She's unconscious, but they think she's going to make it. :-()-: You alright? :-()-: I've seen a lot of things in my time on the job, but nothing like this. Yurilivich? :-()-: I was with him the whole time until I got the call at the hotel. :-()-: Sanderson? | knight moves | 1992 |
| 15873.0 | You must be Erica. :-()-: Uh huh. :-()-: I'm Kathy. | knight moves | 1992 |
| 15891.0 | Same as the others? :-()-: Yeah. :-()-: How long she been dead? :-()-: I'd say at least eighteen hours. :-()-: That means she was dead before we even finished figuring out the message. | knight moves | 1992 |
| 15959.0 | Relax. :-()-: How the hell can I relax after seeing what I just saw. :-()-: I know it was bad. :-()-: How could you? Unless you were there. | knight moves | 1992 |
| 15916.0 | This is a printout from the hotel computer for all the messages logged to your room. Here's one at 9:04 pm. It says: From Debi. Please call me at home. :-()-: She called to give me my schedule for tomorrow. :-()-: What's interesting about it is you say you only knew her in passing. Yet, she says on her message for you to call her at home and she doesn't leave a number. That would imply you already knew the number. :-()-: I got it from the tournament directory. | knight moves | 1992 |
| 15963.0 | What? :-()-: Have you noticed that every time we start to talk about something serious you start to play games. :-()-: I'm not playing a game now. :-()-: Yes you are. You're playing word games. :-()-: What is this? :-()-: I'm just trying to get to know you, Peter. :-()-: What? By attacking me? | knight moves | 1992 |
| 15852.0 | That wouldn't be very sporting. Remember Huxley? "His play is always fair and just." :-()-: You're groping. I have been fair. It's my move now. :-()-: I'll give you anything you want. Anything! Please! :-()-: Don't beg, Peter. She has to die. I can't win unless she dies. | knight moves | 1992 |
| 15878.0 | Okay. Nice meeting you. :-()-: Nice meeting you. | knight moves | 1992 |
| 15910.0 | And what if he's wrong. If you were one hundred percent sure it was Peter you would've arrested him. If it is someone else then he's going to kill again tonight and you're sitting here ignoring the message. :-()-: We're going to work on the message. :-()-: This stinks! You want to know what I think? I think there have been five murders and you've got shit to go on. You need to blame someone and he's the easiest choice. :-()-: The most logical choice. :-()-: You don't have a shred of evidence! | knight moves | 1992 |
| 15789.0 | I've talked to a few people who say you and her were... friendly? :-()-: Chess tournaments can be boring sometimes. People have a lot of free time. They like to gossip. | knight moves | 1992 |
| 15823.0 | I spoke to Jeremy. He's watching Sanderson's kid. Sanderson went out after the match and hasn't come back since. :-()-: Find him! I want to talk to him. | knight moves | 1992 |
| 15820.0 | You still having a problem with this? :-()-: Yeah, I am. I think he's playing us. If I was a killer and the police were trying to make a case against me, what better why to draw them off than to put their attention on someone else? | knight moves | 1992 |
| 15857.0 | Don't you see? I had to make it right. I ignored my mother's crossing. I sat with them all. I held their hands. I stroked their hair. I was with them to the end. I took away the blood. I washed them. Their crossing was peaceful. :-()-: I think your mother knows that now. Why don't you put the knife down. Put it down, David. | knight moves | 1992 |
| 15942.0 | Bainbridge Books. :-()-: Hi, Sara. This is Doctor Sheppard. I was wondering if you could tell me if you have a book on chess called "Principals and Tactics" by Anton Berger. :-()-: I can check and call you back. :-()-: Thank you. I'm at 639-7393. :-()-: We'll call you back. | knight moves | 1992 |
| 15865.0 | What is it? :-()-: New York. 1986. Viktor Yurilivich. | knight moves | 1992 |
| 15954.0 | I think it's interesting. :-()-: Another kind of game? :-()-: In a way. :-()-: This isn't a game. :-()-: Oh, but it is. He's killing a person everyday and challenging us to catch him. That's a game. It has rules and objectives. :-()-: How can you look at it so clinically? :-()-: Take your average cop. They deal with death everyday. If they let emotions get in the way it would cloud their judgement. :-()-: That's true -- but the emotion is still there. They just learn to control it. :-()-: What about you? Aren't there times when a young child is telling you a story so sad you just want to cry? :-()-: Of course. :-()-: Do you? :-()-: Let's change the subject. :-()-: Okay. So, tell me about yourself. Are you married? | knight moves | 1992 |
| 15864.0 | There's three. The first is 1983. Lionel Baines. The Boston... :-()-: Never mind. He died two years ago. :-()-: 1985. Hans Korshaud. :-()-: He's in his seventies and lives in Holland. | knight moves | 1992 |
| 15870.0 | Feeling better? :-()-: I just can't believe it. :-()-: You don't want to believe it. It's a normal reaction. :-()-: How come the police never had a record of Sanderson before. This doesn't come out of nowhere, there has to be a history. | knight moves | 1992 |
| 15903.0 | Well, I think you have to play to his ego. He thinks he's superior. The more secure he feels, the more chances he'll take. :-()-: What did he mean by Huxley? | knight moves | 1992 |
| 15869.0 | It worked out alright, didn't it? :-()-: Fuck off! | knight moves | 1992 |
| 15895.0 | I see you're still having problems with your openings. :-()-: And this Jeremy-- :-()-: --Edmonds. I know. You lost to Karpov in '82, didn't you? | knight moves | 1992 |
| 15968.0 | I'm hungry. :-()-: Call room service. | knight moves | 1992 |
| 15815.0 | We wouldn't want someone's death to interfere with your games. :-()-: What was your relationship with her? | knight moves | 1992 |
| 15821.0 | We've checked. There's no one with the last name of Emma on the Island. :-()-: Maybe he's going to drug her? | knight moves | 1992 |
| 15811.0 | You want me to what? :-()-: I want you to feed every street in this grid into your computer. :-()-: It'll take hours. You can't make me do this. :-()-: You're right, I can't make you do it. Besides, you're probably gonna be too busy with the Franchise Tax Board anyway. :-()-: What are you talking about? :-()-: I gotta friend over there. He was telling me things are kind of slow. So, I figured I'd give him a call, have him come down here and look through your records. You know, give him something to do. :-()-: What's the first street? | knight moves | 1992 |
| 15888.0 | Anything? :-()-: Not much. I don't think she was raped. There's no bruising, or any signs of trauma to the pelvic region -- and no trace of sperm which makes sense because she took a bath. :-()-: What about prints? :-()-: No prints. :-()-: You mean, no prints but hers? :-()-: No Frank. No prints. | knight moves | 1992 |
| 15834.0 | Hello, Peter. :-()-: Who is this? :-()-: Someone who's going to become an important part of your life. I want to play a game with you. :-()-: I haven't got time for this. | knight moves | 1992 |
| 15861.0 | Nope. Nothing. :-()-: Set up some pieces on the big board? | knight moves | 1992 |
| 15876.0 | Do you like my dad? :-()-: Of course. | knight moves | 1992 |
| 15835.0 | Hello, Peter. :-()-: Just a second. Hello? | knight moves | 1992 |
| 15798.0 | What in the hell do you think you're doing? Slamming down the phone in the middle of the trace. :-()-: You think you're going to catch him on a trace? Everything he does is planned out well in advance. The only way we're going to get him is to rattle him -- make him slip up. | knight moves | 1992 |
| 15797.0 | Are you crazy? :-()-: Let's see how much he wants to play. | knight moves | 1992 |
| 15813.0 | Debi Rutlege. Female. Caucasian. Twenty four. Worked over at the Four Oaks Hotel. :-()-: Local? :-()-: No. She just moved here last month from Portland. | knight moves | 1992 |
| 15844.0 | Huxley's quote also says, "his play is always fair and just." :-()-: So is mine, within the framework of my rules. | knight moves | 1992 |
| 15961.0 | Can you go fifteen minutes without thinking about it? :-()-: No. But I'm open to distractions. :-()-: I'm sure you are. | knight moves | 1992 |
| 15831.0 | Don't fuck with us! Where did you go! :-()-: He was with me? | knight moves | 1992 |
| 15851.0 | Yes? :-()-: Congratulations on your daring escape. You just missed me by a few seconds. It's check, Peter. :-()-: Let me speak to my daughter. :-()-: She's in the other room. I just wanted you to know she wasn't dead... yet. But it's time for her to die now. :-()-: Please... wait. :-()-: The game's over. You lost. :-()-: It's me you want. Not her. :-()-: No. As usual you're wrong. It is her I want. Killing you would be easy. Living with the consequences of losing will be much more of a defeat. | knight moves | 1992 |
| 15814.0 | What do you think? :-()-: He's got an answer for everything, but he doesn't have an alibi. :-()-: You think he's dirty? :-()-: I don't know, but I think you're right. He's lying. | knight moves | 1992 |
| 15962.0 | You know, you're not the easiest person in the world to get close to. :-()-: You always want to talk about me. What about you? :-()-: Wasn't I just talking about me? :-()-: No. You were talking about chess. :-()-: Alright. What about me? :-()-: I dunno. Where's Erica mother? :-()-: She died in a car accident. :-()-: I'm sorry. :-()-: It's alright. It was a long time ago. :-()-: It must've been very hard on Erica? :-()-: It was. :-()-: And you? | knight moves | 1992 |
| 15790.0 | By yourself? :-()-: Yes. | knight moves | 1992 |
| 15983.0 | This isn't going to be like the phone book, is it? :-()-: Of course not. | knight moves | 1992 |
| 15899.0 | I don't think it matters. Last nights victim, Christie Eastman was found in a warehouse on the outskirts of town. The night before, Debi Rutlege was found in the center. :-()-: Meaning? | knight moves | 1992 |
| 15846.0 | What? :-()-: You heard me. Why did you go out to the institute looking for her? :-()-: What makes you think I was there? :-()-: I couldn't tell you that. It would ruin the game. :-()-: The game's almost over, Peter, and you're running out of time. | knight moves | 1992 |
| 15796.0 | Morpheus. :-()-: What? :-()-: Not what -- who. Morpheus. The Greek God of dreams. Look at the next line. "Her God has gone and left his home." | knight moves | 1992 |
| 15929.0 | A shadow. What's as big a castle? As light as air , but one hundred men can't move it? The shadow of the castle. Are there any buildings that have the name Castle in it that cast a shadow long enough to fall across another building? :-()-: Wait a minute. There's an apartment in that area called the Castle Arms. :-()-: That's got to be it. | knight moves | 1992 |
| 15965.0 | I just don't want to fight with you. :-()-: Then show me who you are. | knight moves | 1992 |
| 15906.0 | As large as castles. :-()-: Yes. Why not just say a building if he meant any general type of structure? | knight moves | 1992 |
| 15881.0 | Who told you that? :-()-: Mrs. Lutz. She also told me that Mr. Lutz goes to a medium to try and contact great Grandmasters in the spirit world. :-()-: Has he gotten through to any? :-()-: No, but he did contact a dead parcheesi champion from Ohio. | knight moves | 1992 |
| 15938.0 | Peter... :-()-: I've got be here for the police when they come. Then I've got to practise. In case you forgot I'm in the middle of a match right now. :-()-: You're always in the middle of a match. :-()-: I want to be the best I can. | knight moves | 1992 |
| 15809.0 | You set yourself up tonight when you attacked Kathy, you crazy fuck! :-()-: What? | knight moves | 1992 |
| 15830.0 | It doesn't make sense because we don't understand it. :-()-: But a hundred men could move him. | knight moves | 1992 |
| 15838.0 | I did another one last night. You might have saved her, but you didn't want to play. :-()-: Where is she? :-()-: You'll find her. :-()-: If you got something to say to me, just come out and say it! | knight moves | 1992 |
| 15890.0 | This is where he forced his way in. We found some fibers on the windowsill, probably particles of clothing that rubbed off when he climbed through. I'll know more once I get it under the microscope. :-()-: That's great. :-()-: Wait, I've saved the best for last. Andy, hit the lights, will you? | knight moves | 1992 |
| 15984.0 | You're going to be late for your match. :-()-: Are you going to come tonight? :-()-: Yeah. I'll be there later. | knight moves | 1992 |
| 15801.0 | You know, I figure that's pretty much how these girl's feel just before they get it. You think I'm right? :-()-: I wouldn't know. :-()-: That's right. I guess only the killer would know that. :-()-: How'd you get in here? :-()-: The door was open. :-()-: No it wasn't. :-()-: Of course it was. Otherwise I'd be breaking and entering. That's a felony. :-()-: What do you want? :-()-: You ever do any hunting, Peter? | knight moves | 1992 |
| 15904.0 | Maybe we should back up a minute and take a look at what we do have. All the girls that have been killed have been killed at night. They're all the same type. All moved into their homes within the last three months -- and all of them found their homes through Homesearchers. :-()-: I thought that Homesearchers was a dead end? :-()-: It can't be a coincidence. The woman that owns it has a son. She says he's been on vacation in Montana for the last ten days. We're trying to locate him. There's also a cleaning service that comes in once a week. We're checking that out too. :-()-: He moves around a lot. Why? | knight moves | 1992 |
| 15828.0 | Sending yourself anonymous notes in the mail is one thing -- but who called him today? :-()-: I don't know. Maybe there's two of them. Maybe he hired some Wino to make the calls. :-()-: Maybe you're reaching a bit? I think Sanderson should be in on this. There's a reason why the killer's calling him. | knight moves | 1992 |
| 15966.0 | This frightens me -- because I'm starting to feel things I haven't felt in a long time. :-()-: You've got to face the things you feel. :-()-: I thought you were the woman who didn't want to get involved. :-()-: I said I didn't plan on getting involved. | knight moves | 1992 |
| 15868.0 | The police came to me for help. What could I do? :-()-: You could have been honest with me for starters. We work together. I have to be able to trust you. :-()-: I admit I handled it badly. Sanderson wasn't going to talk to me... but you're young, attractive-- :-()-: --the same type as the girl who got killed. Jesus, Alan, the guy could be a psychopath. :-()-: They asked who would be best suited for this and you-- :-()-: Don't patronize me! You sent me into a potentially dangerous situation and didn't warn me! | knight moves | 1992 |
| 15936.0 | It's a good play, but risky. :-()-: You don't win by playing it safe, Peter. | knight moves | 1992 |
| 15898.0 | That would indicate he's playing out a fantasy. Power-control killers usually fantasize about their actions long before they commit them. Once they become a reality though, they reach a sense of euphoria and need to repeat the act to sustain it. But, in all the research I've read on Serial Killers, I've never heard of one moving so fast. It's as if the game is the catalyst for the murders -- not the other way around. :-()-: Anything else? :-()-: Yes. Why disguise your voice if no one knows it? :-()-: I was thinking the same thing. He must be local. It's logical for him to assume that the police would be there, but he recognized Andy's voice and called him by name. What about this? :-()-: I would think it's some type of clue as to where he's going to kill next? | knight moves | 1992 |
| 15886.0 | Whaddaya got, Nolan? :-()-: Not much. There's no sign of a break in. I think she let him in. :-()-: How long has she been dead? :-()-: Six, eight hours tops. :-()-: Any sign of rape? :-()-: Not that I can see. We'll know more once I get the lab reports back from Seattle. | knight moves | 1992 |
| 15848.0 | Hello. :-()-: Last night was very exciting, wasn't it? Have you figured out what I'm doing? :-()-: You're playing the Tarakoss opening. :-()-: Very good. :-()-: You're next move should have been e2- e3. :-()-: I used a variation. You should have anticipated that. Have you figured out the message? :-()-: What word did you leave last night? :-()-: The police haven't told you? :-()-: They think that you and I are doing this together. | knight moves | 1992 |
| 15893.0 | Only that you wanted someone from the institute to talk to Sanderson for some case you're working on. What'd he do? :-()-: We're not sure. :-()-: What do you think he did? :-()-: We think he could be a suspect in a murder investigation. | knight moves | 1992 |
| 15943.0 | Bainbridge Books. :-()-: Sara, this is Doctor Sheppard. I called you earlier about a book. :-()-: Yes, Doctor Sheppard. We called you back but you weren't home. We have the book. :-()-: Could you please do me a big favor? In the first chapter the author mentions his three rules of chess. Could you look and tell me what they are? :-()-: Of course. Yes, here it is. Why, they're all the same? :-()-: What are they, please? :-()-: "Carefully". "Carefully". "Carefully". | knight moves | 1992 |
| 15956.0 | You're projecting your own desires and reading more into what happened than what actually did. :-()-: Very good. Spoken like a true psychologist. When confronted with the prospect of your own reality, hide behind quotations. What is that Masters and Johnson? | knight moves | 1992 |
| 15908.0 | He's right, Kathy. :-()-: What are you saying? That I'm seeing what I want to see? That I'm protecting a murderer? What? | knight moves | 1992 |
| 15960.0 | I know you didn't :-()-: You know, Andy thinks you're doing this. :-()-: Doesn't that scare you? :-()-: No. :-()-: Why? :-()-: Because I think he's wrong. | knight moves | 1992 |
| 15973.0 | Peter... :-()-: How could you think that, Kathy? How could you even consider it? | knight moves | 1992 |
| 15842.0 | Just for that no hint today. :-()-: Why are you doing this? You must have some idea of the pain you're causing people. :-()-: Pain? Pain is just a state of mind. It's something you learn to live with. I have. | knight moves | 1992 |
| 15952.0 | You sure you don't want to eat something? :-()-: I don't think I could. | knight moves | 1992 |
| 15980.0 | If we could just figure out what the next word is going to be. He said the game's going to end tonight so there's only going to be one more word. :-()-: It could be anything. :-()-: It's got to be grammatically correct. | knight moves | 1992 |
| 15827.0 | Why does he call Sanderson? :-()-: He's one of the best Chess players in the world. Who better to play a game with? | knight moves | 1992 |
| 15802.0 | No. :-()-: I used to. With my old man. He taught me how to hunt and trap. Trapping's a lot harder than most people think. We used to go after Raccoons mostly. They'd get into our garbage, our fields. When an animal can't live peacefully with those around it, it has to be destroyed. But they're crafty little devils. You see the trick is, you can put your trap down, but no Raccoon is going come near it unless you lay down the scent. You ever smell Raccoon scent? Smells like shit, but to a male Raccoon it smells just like pussy. He'll walk right up to that trap, even though it don't look nothing like a Raccoon and stick his Goddam head right in it. You know why? Cause he can't help himself. The scent drives him. So, if you want to catch a Raccoon all you gotta do is figure out where he is -- lay down the scent -- and sooner or later he'll walk right into the trap. :-()-: What if he doesn't bite? What if he's an exceptionally bright Raccoon? :-()-: Well then, you just gotta find out where he is -- and once you're sure where he is -- you shoot the fucker. | knight moves | 1992 |
| 15945.0 | Is this your first time here? :-()-: Yes. | knight moves | 1992 |
| 15985.0 | You beat him. :-()-: I got lucky. :-()-: There's a girl in the hospital who is going to live because of you. You made a difference. | knight moves | 1992 |
| 15971.0 | They're not delivering. I'm going to go pick up the pizza. :-()-: I thought you said the line was busy? :-()-: I tried again and got through. :-()-: What's wrong? :-()-: Nothing. :-()-: Then why are you backing up? | knight moves | 1992 |
| 15986.0 | It's over now. :-()-: I just wanted you to know in case you thought about it in the future. :-()-: Do we have a future? | knight moves | 1992 |
| 15836.0 | Very amateurish, Peter. I'm surprised you would use such an obvious tactic. I'm not an idiot! Don't treat me as one! I'll call you everyday... You get one minute, whether you put me on hold or talk is up to you! Are you ready to play? :-()-: Yes. Why did you kill Debi Rutlege? :-()-: To get your attention. :-()-: Did you know her? :-()-: No, only the path she chose to travel. | knight moves | 1992 |
| 15845.0 | Have you any idea what the message is? :-()-: If it's so important, why don't you just tell me? :-()-: I couldn't tell you that. It would ruin the game. Not that you're playing it very well. :-()-: You like to brag, don't you? | knight moves | 1992 |
| 15860.0 | Sure. We've got a modem line hooked up with the data base in New York. :-()-: Can we correlate data? Look for specific things, like player's ages... stuff like that? :-()-: No problem. We're in. :-()-: Okay. Run a search to see if there are any players with a rating above two thousand that live on the Island. | knight moves | 1992 |
| 15901.0 | Before we go in I gotta tell you this isn't going to be pretty. :-()-: I know that. I've seen the photo's. :-()-: This ain't no photo. | knight moves | 1992 |
| 15925.0 | I still don't see how he could be playing chess against you. There's only enough grids on the map to be half a board. :-()-: That's all he needs. He's making the moves. It's an opening. I just can't remember the name. Okay, it's a game. He's starting it, so he moves first. He's white. e2-e4. | knight moves | 1992 |
| 15944.0 | Sorry. I thought it was empty. :-()-: It's alright. | knight moves | 1992 |
| 15803.0 | You got it on the board. :-()-: No, I need the original note. :-()-: There's a photostat of the original on the wall. | knight moves | 1992 |
| 15885.0 | It isn't Kathy. :-()-: Who's Kathy? | knight moves | 1992 |
| 15892.0 | Well? :-()-: Well what? I told you this was a stupid idea. You can't learn anything from someone in a few minutes. :-()-: You didn't pick up any vibes from the guy? :-()-: I'm a psychologist, Frank... not a psychic. What's this all about anyway? :-()-: What did Dr. Fulton tell you? | knight moves | 1992 |
| 15819.0 | The F.B.I. has nothing remotely similar to this guy. I think he's a first- timer. :-()-: Check with the State. If he's never killed outside of Washington the F.B.I. wouldn't have it. Nolan? | knight moves | 1992 |
| 15967.0 | You can turn everything around so easily. This is not just another game, is it? :-()-: No. | knight moves | 1992 |
| 15926.0 | It's a commercial area. No one lives there. :-()-: But he had to dump the body there to make the move. | knight moves | 1992 |
| 15949.0 | Well, I think I've had enough. :-()-: Getting too hot in here for you? | knight moves | 1992 |
| 15923.0 | As large as castles. You are still light as air, one hundred men can't move me. :-()-: It doesn't make sense. | knight moves | 1992 |
| 15900.0 | His home would be... Mount Olympus. :-()-: Call dispatch. Double the patrols. I want that area blanketed. | knight moves | 1992 |
| 15932.0 | Don't you understand what I'm telling you? That's why he wants me out of the way. :-()-: I understand that this is just another one of your games. :-()-: Look, I'll do anything you want! I'll sign anything you want! Just send a car out to the hotel. | knight moves | 1992 |
| 15837.0 | And -- what path is that? :-()-: We all have paths to follow. You hope yours will lead you to me. :-()-: Why did you write remember on the wall? :-()-: That's something you'll have to figure out for yourself. Really, Peter, you can't expect me to answer such direct questions. :-()-: Why not? :-()-: You don't want to think and that's why I'll win! I'm already two points ahead. :-()-: What? | knight moves | 1992 |
| 15924.0 | He's using the map as a chessboard! The sonofabitch is playing chess with me!! :-()-: How can he be playing Chess with you? You're not making any moves against him. :-()-: Maybe I already have. | knight moves | 1992 |
| 15914.0 | I know you're busy so I'll get right to it. Did you know her? :-()-: Only in passing. To say hello to. | knight moves | 1992 |
| 15840.0 | Hello? :-()-: Do you know what I'm doing right now, Peter? I'm looking at the name of the girl I'm going to kill tonight. :-()-: You know her? :-()-: Not really. :-()-: Why her? :-()-: Because she's the type. :-()-: But you said you didn't know her. :-()-: I know what I said! She looks just like... :-()-: Just like who? :-()-: I really wish you'd stop trying to maneuver me. I find it irritating, not to mention insulting. :-()-: I'm just trying to play the game. :-()-: You're not playing very well. There are clues all around you and you keep missing them. | knight moves | 1992 |
| 15912.0 | If you can think of anything else give us a call. :-()-: You know, I hate to say I told you so, but I warned her. Coming home at all hours of the night. A young girl new to the city. This neighborhood ain't what it used to be. | knight moves | 1992 |
| 15931.0 | Jeremy would have never killed himself. :-()-: Maybe he just got tired of covering for you. :-()-: This is insane! Can't you see what's going on. The killer wants me out of the way. He's setting me up. | knight moves | 1992 |
| 15825.0 | Ready for round two? :-()-: I've got a feeling we're going to go the distance with him. Let him sweat for a little while. | knight moves | 1992 |
| 15957.0 | You have to win every point, don't you? :-()-: I just wanted to know if you're involved with anyone? Let me ask you something? Do your colleagues know if you're involved or not? :-()-: Of course. :-()-: Why? :-()-: Because I work with them. | knight moves | 1992 |
| 15969.0 | Fine. :-()-: What do you want on it? :-()-: You. :-()-: Would you settle for pepperoni? :-()-: If I have to. I'm going to take a shower. | knight moves | 1992 |
| 15871.0 | Kathy, please. You're going to wear a hole in the carpet. :-()-: I'm just nervous. Sorry. :-()-: You're safe. He wouldn't come here. :-()-: He already did once. | knight moves | 1992 |
| 15872.0 | Why did he? :-()-: Why did he what? :-()-: Come here. :-()-: He was watching you. :-()-: Yeah -- that's what we've always thought -- but what if he wasn't? What if I had nothing to do with the reason he came here? :-()-: You're losing me. | knight moves | 1992 |
| 15922.0 | Another one? What word did he leave? :-()-: "Is". Did you tell him about the institute? | knight moves | 1992 |
| 15833.0 | We'll find the evidence. :-()-: You couldn't find your dick in a wind storm! | knight moves | 1992 |
| 15853.0 | I was thinking about leaving the phone line open so you could hear her die -- but I've decided it's better if you don't know exactly when I kill her. Good-bye Peter. :-()-: No! Wait!! | knight moves | 1992 |
| 15879.0 | Congratulations, dad. :-()-: My cheering section. | knight moves | 1992 |
| 15817.0 | No shit! :-()-: Could someone this disturbed give the appearance of being normal? | knight moves | 1992 |
| 15933.0 | Alright, c'mon Peter. We're moving you to a cell. :-()-: I signed the confession. What about the car? | knight moves | 1992 |
| 15972.0 | Peter, you have to admit-- :-()-: --admit what? That I was right about you in the steam room? That you're willing to do anything to find out what you want? Would you like me to leave so you can search the rest of the room? | knight moves | 1992 |
| 15841.0 | I'm sure there are other people who would be interested in what I have to say. :-()-: Then call them! | knight moves | 1992 |
| 15791.0 | We seem to have a little problem here? :-()-: What kind of problem? :-()-: I think you're lying. That's what kind of problem. :-()-: What are you saying? | knight moves | 1992 |
| 15807.0 | You sure you weren't over on Pine Road? :-()-: I'm positive. :-()-: You're a lying bastard! :-()-: Fuck you! I'm tired of your goddam accusations! If you want to arrest me go ahead! Otherwise back off. :-()-: It's all a big game isn't it? A sick, fuckin' game. I don't know if there's two of you involved, or what -- but we're going to find out. Last night you gave us just enough to send us to the wrong place. It must've shocked the hell out to you when Frank walked in on Pine Road and you couldn't finish. | knight moves | 1992 |
| 15974.0 | In a fierce magazine you'll find a hint of my actions to come... Why does he set this line apart? :-()-: For emphasis? :-()-: Exactly. It's the key. :-()-: But what does he mean by a fierce magazine? Violent? :-()-: A fierce magazine... brutal... An angry magazine... A war magazine... a mercenary magazine... fierce... Mad. :-()-: What? :-()-: Where's the note? | knight moves | 1992 |
| 15909.0 | We can't rely on your judgement anymore. :-()-: What does that mean? | knight moves | 1992 |
| 15863.0 | Now what? :-()-: Have computer search for anyone that's used that opening against me. There can't be more than a handful. | knight moves | 1992 |
| 15902.0 | You never know how you're going to catch a suspect. :-()-: Peter's right, Captain. He's got to be forced into making a mistake. | knight moves | 1992 |
| 15854.0 | I know where you are. :-()-: Very good. How did you know? :-()-: The pipes. | knight moves | 1992 |
| 15805.0 | Then let's narrow it down. :-()-: Homesearchers. | knight moves | 1992 |
| 15818.0 | Sorry. :-()-: Anything? | knight moves | 1992 |
| 15950.0 | What are you looking for? :-()-: Nothing. I just came in for a steam. :-()-: No you didn't. | knight moves | 1992 |
| 15858.0 | Naw, it's all fixed. I also loaded up a program that'll analyze your games three hundred percent faster. :-()-: Thanks. | knight moves | 1992 |
| 15919.0 | You want me to go with you? Maybe I can help. :-()-: That's alright. We'll take it from here. | knight moves | 1992 |
| 15882.0 | What is it, dad? :-()-: Nothing. | knight moves | 1992 |
| 15824.0 | What is it? :-()-: It's a voice modulator. | knight moves | 1992 |
| 15832.0 | Remember eventually revenge is carefully... Have you tired juxtaposing the words? :-()-: Oh, c'mon. We're not going to spend any more time on this crap, are we? It doesn't mean anything. It's Sanderson! :-()-: It isn't him. Frank, you brought me in on this in the beginning because you wanted my opinion if he was capable of doing this. :-()-: Jesus, you're sleeping with the guy. You've lost your perspective. You can't possibly be unbiased. | knight moves | 1992 |
| 15941.0 | She went into town with Mrs. Lutz. Did they get him? :-()-: No. :-()-: Feel like practicing? :-()-: Yeah. Just let me grab a shower. | knight moves | 1992 |
| 15867.0 | You might not have done it had you known. :-()-: You're damn right! I'm a child psychologist and you send me into a room with someone who could be a murderer! :-()-: Would you mind keeping your voice down? I have guests. :-()-: Oh, well we wouldn't want to disturb your guests, would we? | knight moves | 1992 |
| 15897.0 | Kathy? :-()-: Well, the fact that he views this as a game suggests that he is trying to prove some sense of superiority -- and the way he murders confirms a need to be in complete control of his victim. :-()-: What about the way he arranged the body? | knight moves | 1992 |
| 15976.0 | Hi. :-()-: Can I come in? | knight moves | 1992 |
| 15977.0 | The other night -- I said some things that maybe I shouldn't have. I mean, you haven't known me very long and I can see how you thought what you did. :-()-: I'm not looking for an apology, Peter. :-()-: I was thinking that... maybe when this is all over we could... :-()-: I want someone I can get close to. I don't know if that's possible with you. :-()-: You really haven't seen my best side. | knight moves | 1992 |
| 15847.0 | It's you who's running out of time. You're starting to make mistakes now. You're wondering just how much I really know. Just how close I'm getting? Well, I'm closer than you think, pal -- and I'm gonna nail your ass to the wall! :-()-: Very nice speech, Peter. Did you rehearse that, or was it impromptu? There's an old wooden bench in the garden. Next to it is a rock. You'll find a message for you under it. Let's see if you're as clever as you think you are. | knight moves | 1992 |
| 15896.0 | No. I told him to. I wanted to make sure there was someone on the other end. :-()-: But we heard him. :-()-: It could have been a tape. A prerecorded conversation between Sanderson and himself. | knight moves | 1992 |
| 15849.0 | Interesting concept. I hadn't thought of that. If you think about how Anton Berger plays chess you might get it. :-()-: I'm beginning to think it doesn't mean anything. Remember eventually revenge- - :-()-: --you're hopeless! You can't even read a sentence! Didn't they teach you punctuation in school? The game ends tonight! | knight moves | 1992 |
| 15894.0 | I'm busy right now. :-()-: It's important. | knight moves | 1992 |
| 15939.0 | You have something you want to say? :-()-: Just thought you might want to talk. :-()-: About what? :-()-: Whatever's on your mind. :-()-: Who says something's on my mind. | knight moves | 1992 |
| 15839.0 | I suppose you want to know where I'm going to kill tonight, Peter? :-()-: You're not going to tell me that. :-()-: "Wee Willie Winkie runs through the town. Upstairs and downstairs in his nightgown. Crawling through the window. At the end of Miss Emma's street. Her God has gone and left his home. So her and I can meet." | knight moves | 1992 |
| 15947.0 | Are you with the tournament? :-()-: Uh huh. :-()-: Are you one of the players? :-()-: Yes. :-()-: I've always wanted to learn how to play chess. I don't have the patience for it. When did you start playing? :-()-: When I was very young. :-()-: It seems like such a complicated game. :-()-: Not really. You see your goal and you go after it. Anything that gets in the way is an obstacle and must be destroyed. :-()-: Sounds very violent. :-()-: Chess is a reflection of life. Life is violent. The strong win. The weak perish. | knight moves | 1992 |
| 15951.0 | They couldn't break the riddle. :-()-: Did you think it was going to be easy? You think he was going to lay it out at your feet? :-()-: We need your help. :-()-: I offered my help this morning and Sedman turned me down. :-()-: But you're the key. The one he wants to play the game with. :-()-: I can't right now. I've got a game. :-()-: She could be dead after the game. :-()-: She could be dead now. :-()-: What if she isn't? Where are your priorities? You have to think between someone's life and a chess game? The girl he killed last night was only twenty one years old! He dumped her body behind a warehouse like a sack of garbage! | knight moves | 1992 |
| 15940.0 | What do you see here? :-()-: Mate in five. :-()-: Exactly... and you did this. | knight moves | 1992 |
| 17313.0 | Had enough? :-()-: Even the alarm didn't wake him. | logan's run | 1976 |
| 17358.0 | I hate outside! I hate it! :-()-: We'll be all right... We will... | logan's run | 1976 |
| 17304.0 | Answer the question! :-()-: Do you know how long all this will last? Not thirty years... or thirty thousand years... but thirty thousand years... and you'll be part of it. Ages will roll... Ages. And you'll be here... the two of you... eternally frozen... frozen... beautiful. :-()-: There must be somebody else up here. I can't believe that he's -- :-()-: Let me sculpt you and I will show you where the others have gone. :-()-: That's better. How do you want us? :-()-: Nude. Imagine, a pair. :-()-: It'll be all right... | logan's run | 1976 |
| 17325.0 | I just might look in on New You 483 myself. :-()-: You? Why? You're already beautiful. :-()-: No -- it's that last Runner -- someone in 483 was trying to help him. | logan's run | 1976 |
| 17374.0 | "Beloved son"... So people stayed together for that feeling of love... They would live and raise children together and be remembered. I think I feel that way, Logan. Can we be that way? :-()-: Yes. You and I, Jessica. :-()-: And Sanctuary? :-()-: Sanctuary is the right to live... nothing more. But nothing less, either... | logan's run | 1976 |
| 17340.0 | Why show me? :-()-: I'm going to run. :-()-: Why tell me? :-()-: You know something. :-()-: About running, dying what? :-()-: Both... running's what I'm interested in. :-()-: I know what everyone knows. Try like hell for Renewal. You have the same chance everyone else has. :-()-: It's different now. Help me. :-()-: How can I? | logan's run | 1976 |
| 17365.0 | What does it mean? :-()-: The Lifeclocks have no power outside. | logan's run | 1976 |
| 17341.0 | Where did you get that? :-()-: A Runner gave it to me. :-()-: And then you killed him, right? :-()-: I let him go... believe me. :-()-: I don't.. :-()-: Speak to your friends for me, Jessica... please... :-()-: Please? What friends? :-()-: I don't have much time. :-()-: I never heard of a Sandman running... ever... :-()-: And I never heard of Sanctuary. | logan's run | 1976 |
| 17352.0 | Will you take me with you? :-()-: Why, Jessica? You're still a green. | logan's run | 1976 |
| 17345.0 | A Runner... Cathedral. A woman. :-()-: You're not going, are you? :-()-: Why not? Maybe she'll help me. You won't. You'd better stay here. | logan's run | 1976 |
| 17301.0 | Where do you think we came from? :-()-: From? From? From? :-()-: We were sent here and you know it. Others have been sent here. Where are they? Hiding? | logan's run | 1976 |
| 17359.0 | Don't! :-()-: Sooner or later, we'll have to try something. | logan's run | 1976 |
| 17333.0 | Please... no? You mean "not here" -- that's it? You're a private Available but particular. Don't worry. There's no one here but me. And you. :-()-: No. Just no. :-()-: You prefer women? :-()-: No. :-()-: Well then...? :-()-: Nothing. I felt sad, I put myself on the circuit. It was a mistake. :-()-: Sad? What made you sad? :-()-: A friend of mine went on Carousel tonight. Now he's gone. :-()-: Yes... probably he was renewed? :-()-: He was killed. :-()-: Killed? Why do you use that word? :-()-: Isn't it right? Isn't that what you do? Kill. :-()-: I never 'killed' anybody in my life. Sandmen terminate Runners. Who brought you? :-()-: Nobody. I felt sad... I put myself on the circuit. :-()-: You felt sad. What's your name? :-()-: Jessica. :-()-: You're beautiful. Let's have sex. :-()-: No. :-()-: Later. :-()-: No. :-()-: But you put yourself on the circuit! :-()-: I thought I had to do something. :-()-: And? :-()-: I changed my mind. :-()-: And now? :-()-: Curious. :-()-: About what? :-()-: How a Sandman lives. | logan's run | 1976 |
| 17296.0 | For me? Better feel sorry for yourself, Sandman! :-()-: No, for you! How old are you, Billy? | logan's run | 1976 |
| 17334.0 | Let's have sex. I thought you were curious. :-()-: Not about that. :-()-: I'm listening. :-()-: I'm afraid to tell you. :-()-: I'm not armed. Well? :-()-: Why is it wrong to run? :-()-: You shouldn't even think such things... And you picked a strange person to say them to -- :-()-: I suppose. But what if you want to live? :-()-: So? Do what everyone does. Try like hell for renewal. | logan's run | 1976 |
| 17379.0 | What kind of jewel is this? :-()-: I don't know. :-()-: You're both full of secrets like Macavity. Did you steal this? :-()-: No. :-()-: "Macavity, Macavity, there's no one like Macavity, There never was a cat of such deceitfulness and suavity." | logan's run | 1976 |
| 17405.0 | Well? How do you like it? :-()-: I don't know. The cheeks maybe... look a little -- :-()-: Cheeks? Cheeks? Right. Too much, you think? :-()-: Too little. :-()-: Too little? Too little. Okay, wait for me. | logan's run | 1976 |
| 17361.0 | Do you think everything's going to turn to ice? :-()-: I doubt it. :-()-: Don't ever let go. :-()-: I won't. | logan's run | 1976 |
| 17397.0 | To a city with thousands and thousands of people. :-()-: Alive? | logan's run | 1976 |
| 17369.0 | Look at his face... and his hair... Is that what it is to grow old? :-()-: It could be... | logan's run | 1976 |
| 17354.0 | What do you suppose this was...? :-()-: Some kind of breeding pens... I suppose... They say people used to breed animals, fish, anything... ...to eat, of course. :-()-: Ycch. To kill things and then eat them. It must have been a savage world. | logan's run | 1976 |
| 17370.0 | That sweet madman -- how could he come to exist? :-()-: He had a mother and father -- and he knew them. :-()-: One in a million, I suppose | logan's run | 1976 |
| 17322.0 | Now there's a few who could have been his seed-mother. :-()-: Only a few? You're just not trying. | logan's run | 1976 |
| 17344.0 | If you did know, you'd tell me. :-()-: Of course -- :-()-: If you trusted me, you'd know. :-()-: We're coming to Arcade. Shall we Relive together? | logan's run | 1976 |
| 17363.0 | Maybe we're the first ones to get through... Maybe Sanctuary is near, now... another protected place. It couldn't be outside. :-()-: How would anyone know? Even if we find it -- we can never go back. | logan's run | 1976 |
| 17367.0 | I have never seen a face like that before. It must be the look of great age. Whoever he was he was terribly old. :-()-: Yes, do you think that's why he looks so sad? | logan's run | 1976 |
| 17401.0 | I know... We're going to try and get in this way. I don't think you can make it. :-()-: Oh... I did so look forward to seeing all those people. :-()-: I'm sorry. :-()-: Yes... :-()-: Can you make it back? :-()-: Oh my... I'll try. | logan's run | 1976 |
| 17337.0 | You could have called me yourself. :-()-: But I wasn't sure you'd come. :-()-: Here I am. Shall I come in? | logan's run | 1976 |
| 17387.0 | We don't get many Sandmen. I think we've only had one other since I've been here. :-()-: A Sandman can get as sick of his face as anyone else. Where's the doctor? :-()-: I like your face. Would you mind if Doc took a picture? I'd like him to give your face to somebody else. :-()-: It's all right with me. Is he here? :-()-: My name's Holly... Holly 13. In ancient times they said my number was unlucky. Do you believe in luck? :-()-: No -- Look, I'm in a hurry. | logan's run | 1976 |
| 17349.0 | I'm ashamed. I was bringing you to be killed. :-()-: Where? Sanctuary? Can you take me there? :-()-: Logan, I don't know where Sanctuary is. But if I take you to them, they'll kill you. :-()-: All right. But why? I didn't kill the Runner. :-()-: Yes, but they won't know that... or care. They're hunting you, Logan. Maybe me too, now... :-()-: That's nothing... there's a Sandman behind us, too and there'll be more soon. Take me to them. :-()-: I -- I can't. :-()-: Then -- why don't you leave me -- go to them -- explain :-()-: No. Not that either. | logan's run | 1976 |
| 17338.0 | I couldn't get you out of my mind. :-()-: I'm the most beautiful woman you've ever seen, I suppose? :-()-: Maybe... sure... :-()-: Thanks... but I have the choice. :-()-: Of course. :-()-: Then it's still no. | logan's run | 1976 |
| 17329.0 | It's him! The first Sandman. He killed... Doc. :-()-: No, Holly -- wait! He's running. Tell them the rest! :-()-: He's the one. You too. I remember. He was in a hurry. Just a face job. Dark hair, I said. Then he killed Doc and you grabbed me -- and the machine blew up and I ran... I ran. :-()-: Holly. Holly! Please... The other Sandman. Remember the one who came after -- | logan's run | 1976 |
| 17402.0 | Break-in scanners report intrusion, identify. :-()-: Logan-5... Francis-7, authorized duty quadrant. Intrusion accidental. :-()-: Clear Logan-5 and Francis-7. | logan's run | 1976 |
| 17308.0 | It's a real privilege, Sandman. :-()-: Thanks. I thought you'd be older. I expected a Red. :-()-: I am. :-()-: Your own work? :-()-: And I did it myself right on there. | logan's run | 1976 |
| 17396.0 | We're leaving. :-()-: What a pity. I was hoping you'd be here to bury me. | logan's run | 1976 |
| 17406.0 | No -- just there -- on the first level. Don't look for us. We'll see you. :-()-: You don't seem quite sure, Jessica. Can you do it? Will you? | logan's run | 1976 |
| 17295.0 | I'm a Sandman! :-()-: I cut up a Sandman yesterday. They said I'd never get him... but I cut him up good, I did. :-()-: I feel sorry for you, boy! | logan's run | 1976 |
| 17302.0 | Hiding? Yes! Hiding, hiding. :-()-: Where do we go?! Where do we go from here??!! | logan's run | 1976 |
| 17399.0 | Is that really it? It doesn't seem very far. Will we be there soon? :-()-: I promise. We'll go on as soon as it's light. | logan's run | 1976 |
| 17357.0 | What's that? :-()-: It feels like breath. It makes everything move. Your hair is moving. :-()-: And yours. | logan's run | 1976 |
| 17377.0 | Yes, cats, of course. What else could they be? Cats. Of course each one has his own name too. :-()-: But there are so many of them. Do you know each one separately. :-()-: Yes indeed, everyone. Actually, they all have three. "The naming of cats is a difficult matter. It isn't just one of your holiday games. You may think at first I'm mad as a hatter when I tell you a cat must have THREE DIFFERENT NAMES." An ordinary name and a fancy name. That's two. Do you want to guess what the third one is? | logan's run | 1976 |
| 17381.0 | We'll have to bury him. :-()-: What's that? :-()-: They're put into the ground so they can be visited by the living... | logan's run | 1976 |
| 17373.0 | Jessica... listen to me... listen to me... The Lifeclocks made me kill Francis. They make people die or be killed every day. If I didn't try and destroy that... I couldn't live here or anywhere. Do you understand? :-()-: I want to be alive and with you, that's all I want. | logan's run | 1976 |
| 17393.0 | What's beyond this place -- do you know? :-()-: No, no, no :-()-: Did your Mother or Father ever mention another place? :-()-: Never, never, ever. Nothing. | logan's run | 1976 |
| 17366.0 | You can have any woman in the city. What do you really want? :-()-: You know, Jessica. :-()-: ...But I still have the choice...? :-()-: Of course. :-()-: Then the answer's yes... | logan's run | 1976 |
| 17330.0 | That's right. The other one came after. The older one. Smashing, killing, burning! :-()-: ...and he was hunting the first one, this one. Wasn't he? Wasn't he? This one was running, the other one was hunting him... :-()-: Yes. Oh yes. He was after you. I remember. You're running! | logan's run | 1976 |
| 17360.0 | It's getting dark and cold. I'm tired. :-()-: Why don't we rest here? We know we can eat these. | logan's run | 1976 |
| 17368.0 | They all have names and numbers on them. I wonder what they are? :-()-: "Beloved Husband". "Beloved Wife". What can all that mean? | logan's run | 1976 |
| 17351.0 | Exactly four steps now. Let me lead you. Now to the right. It's narrow here, you'll have to get behind me. :-()-: How will they know we're coming? :-()-: They're watching us now. They'll let us in when they're sure. | logan's run | 1976 |
| 17324.0 | That was a great shot you made. :-()-: Yes. But you look a little rusty to me -- what were you doing, wondering? | logan's run | 1976 |
| 17376.0 | What does that water do? :-()-: It's part of the hydrogalvanic system. The ocean tides are changed into energy somehow. :-()-: Is it inside the city? :-()-: Of course. I don't know where... I just took them for granted. It's our only chance. | logan's run | 1976 |
| 17400.0 | That's better than gold when it's cold. :-()-: Thank you. Tell me -- what do those words mean? "Beloved husband"... "Beloved son"... "Beloved wife"... :-()-: My father was the husband and my mother was the wife. "Beloved" is a word they used -- to stay together. :-()-: Stay? They lived together all their years? :-()-: Oh, yes... I think... | logan's run | 1976 |
| 17316.0 | But you don't know, you just say what everyone says. "One for one. One for one." :-()-: Well, why not!? That's exactly how everything works. How else could the city stay in balance -- You have a better idea? :-()-: No, but at least I wonder sometimes -- instead of doing that "one for one" song of yours. You sound like a sleepteacher with a stuck tape. :-()-: Well the minute you get a better idea you can stop wondering. You know, Logan -- you wonder a lot. Too much for a Sandman. | logan's run | 1976 |
| 17353.0 | How do we know this is the right way? :-()-: It's the only way. | logan's run | 1976 |
| 17384.0 | Come with us. :-()-: Where are you going? | logan's run | 1976 |
| 17382.0 | I'll make the arrangements. :-()-: At least it's over... | logan's run | 1976 |
| 17342.0 | Are you here to help me? :-()-: What do you need? | logan's run | 1976 |
| 17364.0 | You're right... it must be near now. We'll find it. :-()-: Thirty thousand years didn't last very long, did they? | logan's run | 1976 |
| 17311.0 | You are here. I couldn't believe it when they told me. What are you doing? :-()-: Turn this way. No, no... not you... YOU! | logan's run | 1976 |
| 17372.0 | What are we promising him? What can we possibly give him? :-()-: He asked if we would bury him when his time comes. :-()-: We can't. We're going back. :-()-: To what? :-()-: I'm going to try and tell people what we've seen and -- :-()-: You're lying! You'll never have the chance to tell anybody anything! You'll be killed the moment you're seen! :-()-: Do you expect me to let things go on without trying to change them?! :-()-: Things won't change... you know that! We can live here together, Logan... have a life as long as his... together! :-()-: Things change! :-()-: You want to go back to kill, is that it?! Now, you'll want to kill your own!!! Kill Sandmen!!! Killing's all you ever...!!! | logan's run | 1976 |
| 17303.0 | Is that the wind? Not yet... You must hear my birds sing. :-()-: You know about Sanctuary! I know you do! You have to help us! You don't have a choice! It isn't your decision!! Tell us. :-()-: Never a pair. I have never had a pair. :-()-: Where do you send them? :-()-: You're a beautiful pair. | logan's run | 1976 |
| 17315.0 | Why? :-()-: Just wondered... what happens? :-()-: Dunno... flameout maybe. Whatever happens, you can bet it's final. But who would want to find out? | logan's run | 1976 |
| 17385.0 | Goodbye. :-()-: Oh, my... | logan's run | 1976 |
| 17299.0 | We're hungry do you have anything to eat? :-()-: Anything to eat? | logan's run | 1976 |
| 17380.0 | What belongs to the people? :-()-: All this. All of it. :-()-: What people? :-()-: I don't know... but it does. | logan's run | 1976 |
| 17403.0 | I don't know who you are. I'd like to thank someone. :-()-: It doesn't matter who we are. Follow the tunnel to the end. :-()-: Will there be someone to tell us where to go from there...? | logan's run | 1976 |
| 17312.0 | You should've seen me take my last Runner... perfect. I backed him up against a residence pool and when he terminated... his hand... So now you've seen him... what's the difference awake or asleep? :-()-: Open your eyes once, idiot. It's not every day that a Sandman son is born. I'm telling you, Francis -- that's him! :-()-: Maybe, maybe not. What's the difference? Come on, Logan, let's get out of here before everybody finds out. | logan's run | 1976 |
| 17355.0 | I'm afraid. :-()-: It's brighter there... besides, we can't go back. | logan's run | 1976 |
| 17375.0 | Beloved husband... :-()-: Beloved wife... | logan's run | 1976 |
| 17310.0 | Do you have anything special in mind? :-()-: I don't care... Just get it over with. :-()-: Hurry... hurry... hurry. | logan's run | 1976 |
| 17297.0 | Fourteen? Fifteen? Your days are running out. How long can you last? A year. Six months? What happens when you're sixteen and you go green? :-()-: Nothing will happen! I make the rules as I go!! Cubs do what I say! Always have! Always will! I got Cathedral and I'll never let go! :-()-: No cubs over fifteen, Billy! Ever heard of a cub with a green flower? You'll leave Cathedral then, Billy, when you're on green, because they won't let a green stay here. If you try to stay the young ones will gut-rip you apart! :-()-: Shut up! Shut up your damn mouth! | logan's run | 1976 |
| 17390.0 | But there may be a few around somewhere. :-()-: What makes you think so? :-()-: My parents thought so. Mother and Father. You know? :-()-: Mother and -- ? You knew your mother and father? | logan's run | 1976 |
| 17306.0 | All right. Now you keep your bargain. :-()-: Wait for the wind! Wait and hear the birds sing over you! :-()-: We're ready. | logan's run | 1976 |
| 17348.0 | Just follow -- no matter how it seems... :-()-: But what is this -- why? :-()-: The Cubs. When they're flying on muscle there's no way to catch up. Without the dazzle, they'd just go past us -- -- too fast :-()-: Muscle? I don't know that one. | logan's run | 1976 |
| 17305.0 | How do you want us? :-()-: Up there. | logan's run | 1976 |
| 17388.0 | How long have you been living here? :-()-: For as long as I can remember. :-()-: What kind of place is this? :-()-: Just a place, I suppose... who knows? | logan's run | 1976 |
| 17332.0 | What's wrong, Available? :-()-: Please... No. | logan's run | 1976 |
| 17347.0 | They're like beasts. Wild. :-()-: Maybe they're angry because they're grown in meccano-breeders. :-()-: Instead of what? Nine months inside a woman: We're all raised the same but most of us don't become cubs in Cathedral. :-()-: Some people say children need human mothering. :-()-: Insane. Nurseries are better than any mother could be. :-()-: I'm only telling you what I've heard... Haven't you ever wondered what your seed-mother was Like...? :-()-: Uh-uh. :-()-: I have. :-()-: When did you begin to question Lastday? :-()-: I don't remember exactly... except I was a Green. What would you like to relive, Logan? :-()-: Let's see -- how long has it been? | logan's run | 1976 |
| 17378.0 | And... and how were you grown? Inside your mother? :-()-: Yes... :-()-: Are you sure? :-()-: Mother and Father said so... you know? | logan's run | 1976 |
| 17319.0 | You missed something special. :-()-: Well... you can't have them all. | logan's run | 1976 |
| 17331.0 | What's your name? :-()-: I'm Mary 2. :-()-: Where do you live, Mary? :-()-: Here. :-()-: Why aren't you in Nursery? :-()-: I'm very smart. :-()-: When do you go up? :-()-: I never go upstairs. You're a nice old lady. | logan's run | 1976 |
| 17362.0 | It all seemed to make sense until Box. :-()-: Do you think he was telling the Truth? | logan's run | 1976 |
| 17389.0 | How did you get here? :-()-: I have always been here... :-()-: Are there any other humans? :-()-: Gracious... no. :-()-: Have any other people ever passed through? | logan's run | 1976 |
| 17346.0 | I'd rather be with you. :-()-: That's nice. | logan's run | 1976 |
| 17309.0 | I designed it myself. What'll it be... a face job or a full-body job? :-()-: Just the face. :-()-: Fine... Holly will get you ready. You're in good hands, believe me. | logan's run | 1976 |
| 17383.0 | Of course... that's settled then. But just you remember your promise... :-()-: We'll remember. But that's a long time off... | logan's run | 1976 |
| 17300.0 | How do you think we got here??!! :-()-: You walked in. I saw you. Don't you remember? | logan's run | 1976 |
| 17314.0 | You need a lift. Let's go to Arcade and celebrate... your alert successor... Logan-6. :-()-: Has anyone ever broken in to where the babies are? :-()-: Not in my time... | logan's run | 1976 |
| 17394.0 | May we stay here for a while? We'd like to rest. :-()-: Of course you can stay. This belongs to the people. | logan's run | 1976 |
| 17298.0 | Are you too startled? Am I too removed from your ken? I'm neither machine nor man... but a perfect fusion of the two... and better than either. No human sculptor could match this greatness... don't you agree? :-()-: All right -- what are you? :-()-: Your turn. | logan's run | 1976 |
| 17317.0 | Did you ever see Francis-8? :-()-: I never even visited Nursery before tonight. When you wonder, it slows you up -- you know? | logan's run | 1976 |
| 17336.0 | What Quad do you live in? :-()-: K. :-()-: You're sure you don't want to try? | logan's run | 1976 |
| 17339.0 | You can have any woman in the city. What do you really want? :-()-: You know. :-()-: I don't believe you. There has to be more. :-()-: All right. | logan's run | 1976 |
| 17327.0 | What's going on, Logan?! :-()-: It has nothing to do with you. :-()-: What are you talking about?! I saw you let a Runner go? I saw you, Logan?? Tell me!! | logan's run | 1976 |
| 17371.0 | There's a Sanctuary... there is! :-()-: You want there to be one... that doesn't... :-()-: There has to be! I know it exists! It has to!! :-()-: No, there doesn't. Not really -- just so many want it to exist... so many who don't want to die... want it so much that a place called Sanctuary becomes "real". But it doesn't exist. It never existed. Just the hope. :-()-: You're wrong!! It has to be!! It Just has to be!! | logan's run | 1976 |
| 17386.0 | Hello, Sandman. :-()-: Hello. :-()-: Do you want to see Doc? | logan's run | 1976 |
| 17391.0 | Where are they? :-()-: Dead... they're dead... and buried. | logan's run | 1976 |
| 17356.0 | I don't know what's going to happen to us Logan but -- Are you glad you didn't kill him? :-()-: It doesn't make any difference anymore. :-()-: You're really one of us now, aren't you? :-()-: You knew that I wasn't before, didn't you? Why did you stay with me? :-()-: I wanted to... ...And you... what made you kill Sandmen? :-()-: I had to. I did kill... for the first time in my life I killed. :-()-: Because you felt like a Runner, didn't you. :-()-: I guess so... I know I felt something I never felt before... and I didn't like it... not a bit. I'll tell you one thing... Sanctuary better be worth it. That's the last place for me to live now. :-()-: For us. | logan's run | 1976 |
| 17395.0 | Are you ready to put him in? :-()-: Not yet. :-()-: All right. | logan's run | 1976 |
| 17392.0 | They're beautiful. May I have one too please? :-()-: No -- I'm sorry. It's not possible. :-()-: It isn't fair. I'll give you one of my favorite cats... a Jellicle cat. "Jellicle cats have cheerful faces, Jellicle cats have bright black eyes; They like to practice their airs and graces And wait for the Jellicle Moon to rise." :-()-: I'm sorry but I don't have anything to give you. | logan's run | 1976 |
| 17335.0 | But if you're one of the misfits... that's where I come in. :-()-: I didn't say that I would run... I just... :-()-: Are you a 5 or a 6? :-()-: Six. I go red next year. :-()-: You're years away... I don't know why you're thinking of these things, much less talking about them. Want to try? | logan's run | 1976 |
| 17350.0 | Are you taking me to them? :-()-: Yes. I don't know what else to do -- with him following us. Why do you keep running from your -- :-()-: Because he's my friend -- and I don't want to be killed by him -- or anyone. :-()-: He's good, isn't he? :-()-: Will he find us and kill us? Yes... or one of the others. You know there's only one place to go now... :-()-: They won't believe us. :-()-: I'd rather take my chances with them... than with Francis. :-()-: They won't listen. :-()-: You think Sandmen will? There's no other way for me, :-()-: We'll convince them. | logan's run | 1976 |
| 17398.0 | Thousands and thousands... as many as my cats? :-()-: More... many more. :-()-: And all alive you say? | logan's run | 1976 |
| 17307.0 | How did they get in here? :-()-: Regular storage procedure... the same as the other food... The other food stopped coming and they started. :-()-: What other food? :-()-: Fish and plankton, sea greens and protein from the sea. It's all here -- ready -- fresh as harvest day. Fish and plankton, sea greens and protein from the sea... And then it stopped coming and they... ...came instead. So I store them here. I'm ready. And you're ready. It's my Job -- protein, plankton, grass from the sea. | logan's run | 1976 |
| 17326.0 | What the hell took you so long? :-()-: Did you ever see anybody renew? :-()-: I think you've been skulling out too much. First Nursery and now stupid questions. :-()-: Did you? :-()-: Of course. :-()-: Anybody we know? :-()-: Look... why don't you get into the water... you need it... more than I do. :-()-: I'm fine... See you... :-()-: At Carousel tonight? | logan's run | 1976 |
| 17323.0 | Who invited you? :-()-: I'm in my party mood. | logan's run | 1976 |
| 17343.0 | What're you going to do? :-()-: That's tomorrow. :-()-: I wish I could help you. :-()-: Maybe you'll think of something... :-()-: I wish I knew what you think I know. | logan's run | 1976 |
| 17320.0 | The damned Yellows are getting out of hand. Those three ought to be in Cathedral. No business scrambling in Arcade... :-()-: What an old, old man you're getting to be, Francis. Weren't you ever a Yellow? I bet you were even wilder than -- come on, Sandman. | logan's run | 1976 |
| 17328.0 | Why did you do that??!! :-()-: I didn't do anything, Francis! They've made us believe that... :-()-: Why did you do that???!!! | logan's run | 1976 |
| 17404.0 | Someone will follow. When you come to the lock, he will tell you how to go on the other side. Jessica may go with you as far as the lock. :-()-: No. Jessica goes back now. Take her back. Now! Go on back. Back outside, Jessica. | logan's run | 1976 |
| 17318.0 | I don't know what makes you so curious. You have any idea who his seed-mother was? :-()-: Of course not! I'm curious, not sick. | logan's run | 1976 |
| 17321.0 | You should have been with us in Nursery, Daniel. I'm positive I recognized him -- :-()-: Come on. I don't want to miss the filing-in. There'll be some I know tonight, I think... | logan's run | 1976 |
| 24229.0 | Seymour Scagnetti. :-()-: Scagnetti? Oh shit, I hear he's a motherfucker. :-()-: He is a motherfucker. He won't let me leave the halfway house till I get some piece of shit job. :-()-: You're coming back to work for us, right? :-()-: I wanna. But I gotta show this asshole I got an honest-to-goodness job before he'll let me move out on my own. I can't work for you guys and be worried about gettin' back before ten o'clock curfew. | reservoir dogs | 1992 |
| 24241.0 | Didn't I tell ya not to worry? Vic was worried. :-()-: Me and you'll drive down to Long Beach tomorrow. I'll introduce you to Matthews, tell him what's going on. | reservoir dogs | 1992 |
| 24291.0 | That fucking bastard! That fucking sick fucking bastard! :-()-: Marvin, I need you to hold on. There's officers positioned and waiting to move in a block away. :-()-: What the fuck are they waiting for? That motherfucker cut off my ear! He slashed my face! I'm deformed! :-()-: And I'm dying. They don't know that. All they know is they're not to make a move until Joe Cabot shows up. I was sent undercover to get Cabot. You heard 'em, they said he's on his way. Don't pussy out on me now, Marvin. We're just gonna sit here and bleed until Joe Cabot sticks his fuckin' head through that door. | reservoir dogs | 1992 |
| 24295.0 | Follow you where? :-()-: Down to my car. :-()-: Why? :-()-: It's a surprise. | reservoir dogs | 1992 |
| 24249.0 | The black Beverly Hills. I knew this lady from Ladora Heights once. "Hi, I'm from Ladora Heights, it's the black Beverly Hills." :-()-: It's not the black Beverly Hills, it's the black Palos Verdes. Anyway, this chick, Elois, was a man-eater- upper. I bet every guy who's ever met her has jacked off to her at least once. You know who she looked like? Christie Love. 'Member that TV show "Get Christie Love"? She was a black female cop. She always used to say "You're under arrest, sugar." :-()-: I was in the sixth grade when that show was on. I totally dug it. What the fuck was the name of the chick who played Christie Love? :-()-: Pam Grier. :-()-: No, it wasn't Pam Grier, Pam Grier was the other one. Pam Grier made the movies. Christie Love was like a Pam Grier TV show, without Pam Grier. :-()-: What the fuck was that chick's name? Oh this is just great, I'm totally fuckin' tortured now. :-()-: Well, whoever she was, Elois looked like her. So one night I walk into the club, and no Elois. Now the bartender was a wetback, he was a friend of mine, his name was Carlos. So I asked him "Hey, Carlos, where's Lady E tonight?" Well apparently Lady E was married to this real piece of dog shit. I mean a real animal. And apparently he would so things to her. | reservoir dogs | 1992 |
| 24263.0 | How's freedom kid, pretty fuckin' good, ain't it? :-()-: It's a change. :-()-: Ain't that a sad truth. Remy Martin? :-()-: Sure. :-()-: Take a seat. | reservoir dogs | 1992 |
| 24303.0 | I ain't going anywhere. I'm right here. I'm not gonna leave ya. :-()-: Larry, I'm so scared, would you please hold me. | reservoir dogs | 1992 |
| 24245.0 | Brown's dead, we don't know about Blue. :-()-: Nobody saw what happened to Mr. Blue? | reservoir dogs | 1992 |
| 24297.0 | You talked to Nice Guy Eddie? Why the fuck didn't you say that in the first place? :-()-: You didn't ask. :-()-: Hardy-fuckin-har. What did he say? :-()-: Stay put. Okay, fellas, take a look at the little surprise I brought you. | reservoir dogs | 1992 |
| 24285.0 | He was the only one I wasn't a hundred percent on. I should have my fucking head examined for goin' forward when I wasn't a hundred percent. But he seemed like a good kid, and I was impatient and greedy and all the things that fuck you up. :-()-: That's your proof? :-()-: You don't need proof when you got instinct. I ignored it before, but not no more. | reservoir dogs | 1992 |
| 24282.0 | What's the cut, poppa? :-()-: Juicy, junior, real juicy. | reservoir dogs | 1992 |
| 24259.0 | Let's go over it. Where are you? :-()-: I stand outside and guard the door. I don't let anybody come in or go out. :-()-: Mr. Brown? :-()-: Mr. Brown stays in the car. He's parked across the street till I give him the signal, then he pulls up in front of the store. :-()-: Mr. Blonde and Mr. Blue? :-()-: Crowd control. They handle customers and employees in the display area. | reservoir dogs | 1992 |
| 24264.0 | Who's your parole officer? :-()-: A guy named Scagnetti. Seymour Scagnetti. :-()-: How is he? :-()-: Fuckin' asshole, won't let me leave the halfway house. :-()-: Never ceases to amaze me. Fuckin' jungle bunny goes out there, slits some old woman's throat for twenty- five cents. Fuckin' nigger gets Doris Day as a parole officer. But a good fella like you gets stuck with a ball-bustin' prick. | reservoir dogs | 1992 |
| 24237.0 | Joe, you're making a terrible mistake I can't let you make. :-()-: Stop pointing your fuckin' gun at daddy! | reservoir dogs | 1992 |
| 24247.0 | What does it matter who stays with the cop? We ain't lettin' him go. Not after he's seen everybody. You should've never took him outta your trunk in the first place. :-()-: We were trying to find out what he knew about the set up. :-()-: There is no fuckin' set up! Look, this is the news. Blondie, you stay here and take care of them two. White and Pink come with me, 'cuz if Joe gets here and sees all those fucking cars parked out front, he's going to be as mad at me as he is at you. | reservoir dogs | 1992 |
| 24314.0 | Gun shot. :-()-: Oh that's just fucking great! Where's Brown? :-()-: Dead. :-()-: Goddamn, goddamn! How did he die? :-()-: How the fuck do you think? The cops shot him. :-()-: Oh this is bad, this is so bad. Is it bad? :-()-: As opposed to good? :-()-: This is so fucked up. Somebody fucked us big time. :-()-: You really think we were set up? :-()-: You even doubt it? I don't think we got set up, I know we got set up! I mean really, seriously, where did all those cops come from, huh? One minute they're not there, the next minute they're there. I didn't hear any sirens. The alarm went off, okay. Okay, when an alarm goes off, you got an average of four minutes response time. Unless a patrol car is cruising that street, at that particular moment, you got four minutes before they can realistically respond. In one minute there were seventeen blue boys out there. All loaded for bear, all knowing exactly what the fuck they were doing, and they were all just there! Remember that second wave that showed up in the cars? Those were the ones responding to the alarm. But those other motherfuckers were already there, they were waiting for us. You haven't thought about this? :-()-: I haven't had a chance to think. First I was just trying to get the fuck outta there. And after we got away, I've just been dealin' with him. :-()-: Well, you better start thinking about it. Cause I, sure as fuck, am thinking about it. In fact, that's all I'm thinking about. I came this close to just driving off. Whoever set us up, knows about this place. There could've been cops sitting here waiting for me. For all we know, there's cops, driving fast, on their way here now. :-()-: Let's go in the other room... | reservoir dogs | 1992 |
| 24319.0 | I gotta take a squirt, where's the commode in this dungeon? :-()-: Go down the hall, turn left, up those stairs, then turn right. | reservoir dogs | 1992 |
| 24286.0 | Don't worry, Eddie. Me and Larry have been friends a long time, he ain't gonna shoot. We like each other too much. :-()-: Joe, if you kill that man, you die next. Repeat, if you kill that man, you die next! | reservoir dogs | 1992 |
| 24254.0 | Didja use the commode story? :-()-: Fuckin' A. I tell it real good, too. | reservoir dogs | 1992 |
| 24274.0 | Yeah, Mr. Pink sounds like Mr. Pussy. Tell you what, let me be Mr. Purple. That sounds good to me, I'm Mr. Purple. :-()-: You're not Mr. Purple, somebody from another job's Mr. Purple. You're Mr. Pink. | reservoir dogs | 1992 |
| 24334.0 | Who cares what your name is? Who cares if you're Mr. Pink, Mr. Purple, Mr. Pussy, Mr. Piss... :-()-: Oh that's really easy for you to say, you're Mr. White. You gotta cool-sounding name. So tell me, Mr. White, if you think "Mr. Pink" is no big deal, you wanna trade? | reservoir dogs | 1992 |
| 24308.0 | C'mon, throw in a buck. :-()-: Uh-uh. I don't tip. :-()-: Whaddaya mean you don't tip? :-()-: I don't believe in it. :-()-: You don't believe in tipping? | reservoir dogs | 1992 |
| 24246.0 | I take it this is the bastard you told me about. Why the hell are you beating on him? :-()-: So he'll tell us who the fuck set us up. :-()-: Would you stop it with that shit! You beat on this prick enough, he'll tell ya he started the Chicago fire. That don't necessarily make it so. Okay, first things fucking last, where's the shit? Please tell me somebody brought something with them. :-()-: I got a bag. I stashed it till I could be sure this place wasn't a police station. :-()-: Well, let's go get it. We also gotta get rid of all those cars. It looks like Sam's hot car lot outside. You stay here and babysit Orange and the cop. You two take a car each, I'll follow ya. You ditch it, I'll pick you up, then we'll pick up the stones. And while I'm following you, I'll arrange for some sort of a doctor for our friend. | reservoir dogs | 1992 |
| 24265.0 | I just want you to know, Joe, how much I appreciate your care packages on the inside. :-()-: What the hell did you expect me to do? Just forget about you? :-()-: I just wanted you to know, they meant a lot. :-()-: It's the least I could do Vic. I wish I coulda done more. Vic. Toothpick Vic. Tell me a story? What're your plans? :-()-: Well, what I wanna do is go back to work. But I got this Scagnetti prick deep up my ass. He won't let me leave the halfway house till I get some piece of shit job. My plans have always been to be part of the team again. | reservoir dogs | 1992 |
| 24320.0 | So, is he dead or what? :-()-: He ain't dead. :-()-: So what is it? :-()-: I think he's just passed out. :-()-: He scared the fuckin' shit outta me. I thought he was dead fer sure. | reservoir dogs | 1992 |
| 24283.0 | What the fuck are you talking about? :-()-: That piece of shit. Workin' with the cops. | reservoir dogs | 1992 |
| 24242.0 | Nuts. We got a big meeting in Vegas coming up. And we're kinda just gettin ready for that right now. :-()-: Let Nice Guy set you up at Long Beach. Give ya some cash, get that Scagnetti fuck off your back, and we'll be talking to ya. :-()-: Daddy, I got an idea. Now just hear it out. I know you don't like to use any of the boys on these jobs, but technically, Vic ain't one of the boys. He's been gone for four years. He ain't on no one's list. Ya know he can handle himself, ya know you can trust him. | reservoir dogs | 1992 |
| 24315.0 | What the fuck am I doing here? I felt funny about this job right off. As soon as I felt it I should said "No thank you", and walked. But I never fucking listen. Every time I ever got burned buying weed, I always knew the guy wasn't right. I just felt it. But I wanted to believe him. If he's not lyin' to me, and it really is Thai stick, then whoa baby. But it's never Thai stick. And I always said if I felt that way about a job, I'd walk. And I did, and I didn't, because of fuckin' money! :-()-: What's done is done, I need you cool. Are you cool? :-()-: I'm cool. :-()-: Splash some water on your face. Take a breather. | reservoir dogs | 1992 |
| 24287.0 | Larry, I'm gonna kill him. :-()-: Goddamn you, Joe, don't make me do this! :-()-: Larry, I'm askin' you to trust me on this. :-()-: Don't ask me that. :-()-: I'm not askin', I'm betting. | reservoir dogs | 1992 |
| 24278.0 | Give me this fucking thing. :-()-: What the fuck do you think you're doin'? Give me my book back! :-()-: I'm sick of fuckin' hearin' it Joe; I'll give it back when we leave. :-()-: Whaddaya mean, give it to me when we leave, give it back now. :-()-: For the past fifteen minutes now, you've just been droning on with names. "Toby... Toby... Toby... Toby Wong... Toby Wong... Toby Chung... fuckin' Charlie Chan." I got Madonna's big dick outta my right ear, and Toby Jap I-don't-know-what, outta my left. :-()-: What do you care? :-()-: When you're annoying as hell, I care a lot. :-()-: Give me my book. :-()-: You gonna put it away? :-()-: I'm gonna do whatever I wanna do with it. :-()-: Well, then, I'm afraid I'm gonna have to keep it. | reservoir dogs | 1992 |
| 24284.0 | Joe, I don't know what you think you know, but you're wrong. :-()-: Like hell I am. :-()-: Joe, trust me on this, you've made a mistake. He's a good kid. I understand you're hot, you're super-fuckin' pissed. We're all real emotional. But you're barking up the wrong tree. I know this man, and he wouldn't do that. :-()-: You don't know jack shit. I do. This rotten bastard tipped off the cops and got Mr. Brown and Mr. Blue killed. | reservoir dogs | 1992 |
| 24300.0 | Hey, just cancel that shit right now! You're hurt. You're hurt really fucking bad, but you ain't dying. :-()-: All this blood is scaring the shit outta me. I'm gonna die, I know it. :-()-: Oh excuse me, I didn't realize you had a degree in medicine. Are you a doctor? Are you a doctor? Answer me please, are you a doctor? :-()-: No, I'm not! | reservoir dogs | 1992 |
| 24307.0 | Have you guys been listening to K- BILLY's super sounds of the seventies weekend? :-()-: Yeah, it's fuckin' great isn't it? :-()-: Can you believe the songs they been playin'? :-()-: No, I can't. You know what I heard the other day? "Heartbeat-It's Lovebeat," by little Tony DeFranco and the DeFranco Family. I haven't heard that since I was in fifth fuckin' grade. :-()-: When I was coming down here, I was playin' it. And "The Night the Lights Went Out in Georgia" came on. Now I ain't heard that song since it was big, but when it was big, I heard it a million-trillion times. I'm listening to it this morning, and this was the first time I ever realized that the lady singing the song, was the one who killed Andy. | reservoir dogs | 1992 |
| 24228.0 | You fuckin' wish. :-()-: You tried to fuck me in my father's office, you sick bastard. Look, Vic, whatever you wanna do in the privacy of your own home, go do it. But don't try to fuck me. I don't think of you that way. I mean, I like you a lot - :-()-: Eddie, if I was a pirate, I wouldn't throw you to the crew. :-()-: No, you'd keep me for yourself. Four years fuckin' punks in the ass made you appreciate prime rib when you get it. :-()-: I might break you, Nice Guy, but I'd make you my dog's bitch. You'd be suckin' the dick and going down on a mangy T-bone hound. :-()-: Now ain't that a sad sight, daddy, walks into jail a white man, walks out talkin' like a nigger. It's all that black semen been shootin' up his butt. It's backed up into his brain and comes out of his mouth. | reservoir dogs | 1992 |
| 24293.0 | So, talk. :-()-: We think we got a rat in the house. | reservoir dogs | 1992 |
| 24322.0 | Assuming we can trust Joe, how we gonna get in touch with him? He's supposed to be here, but he ain't, which is making me nervous about being here. Even if Joe is on the up and up, he's probably not gonna be that happy with us. Joe planned a robbery, but he's got a blood bath on his hands now. Dead cops, dead robbers, dead civilians... Jesus Christ! I tend to doubt he's gonna have a lot of sympathy for our plight. If I was him, I'd try and put as much distance between me and this mess an humanly possible. :-()-: Before you got here, Mr. Orange was askin' me to take him to a hospital. Now I don't like turning him over to the cops, but if we don't, he's dead. He begged me to do it. I told him to hold off till Joe got here. :-()-: Well Joe ain't gettin' here. We're on our own. Now, I don't know a goddamn body who can help him, so if you know somebody, call 'em. :-()-: I don't know anybody. :-()-: Well, I guess we drop him off at the hospital. Since he don't know nothin' about us, I say it's his decision. :-()-: Well, he knows a little about me. :-()-: You didn't tell him your name, did ya? :-()-: I told him my first name, and where I'm from. | reservoir dogs | 1992 |
| 24267.0 | Toby... who the fuck is Toby? Toby... Toby... think... think... think... :-()-: It's not about a nice girl who meets a sensitive boy. Now granted that's what "True Blue" is about, no argument about that. | reservoir dogs | 1992 |
| 24304.0 | Look, I don't wanna be a fly in the ointment, but if help doesn't come soon, I gotta see a doctor. I don't give a fuck about jail, I just don't wanna die. :-()-: You're not gonna fucking die, all right? :-()-: I wasn't born yesterday. I'm hurt, and I'm hurt bad. :-()-: It's not good... :-()-: Hey, bless your heart for what you're trying to do. I was panicking for a moment, but I've got my senses back now. The situation is, I'm shot in the belly. And without medical attention, I'm gonna die. :-()-: I can' take you to a hospital. :-()-: Fuck jail! I don't give a shit about jail. But I can't die. You don't have to take me in. Just drive me up to the front, drop me on the sidewalk. I'll take care of myself. I won't tell them anything. I swear to fucking god, I won't tell 'em anything. Look in my eyes, look right in my eyes. I-won't-tell-them-anything. You'll be safe. :-()-: Lie back down, and try to - :-()-: I'm going to die! I need a doctor! I'm begging you, take me to a doctor. | reservoir dogs | 1992 |
| 24276.0 | Mr. Blue's dead? :-()-: Dead as Dillinger. | reservoir dogs | 1992 |
| 24235.0 | I don't buy it. It doesn't make sense. :-()-: It makes perfect fuckin' sense to me. Eddie, you didn't see how he acted during the job, we did. | reservoir dogs | 1992 |
| 24321.0 | He will be dead fer sure, if we don't get him to a hospital. :-()-: We can't take him to a hospital. :-()-: Without medical attention, this man won't live through the night. That bullet in his belly is my fault. Now while that might not mean jack shit to you, it means a helluva lot to me. And I'm not gonna just sit around and watch him die. :-()-: Well, first things first, staying here's goofy. We gotta book up. :-()-: So what do you suggest, we go to a hotel? We got a guy who's shot in the belly, he can't walk, he bleeds like a stuck pig, and when he's awake, he screams in pain. :-()-: You gotta idea, spit it out. :-()-: Joe could help him. If we can get in touch with Joe, Joe could get him to a doctor, Joe could get a doctor to come and see him. | reservoir dogs | 1992 |
| 24226.0 | I asked you a question. Are you clear about that? :-()-: Yes. :-()-: Now I'm not gonna bullshit you. I don't really care about what you know or don't know. I'm gonna torture you for awhile regardless. Not to get information, but because torturing a cop amuses me. There's nothing you can say, I've heard it all before. There's nothing you can do. Except pray for a quick death, which you ain't gonna get. | reservoir dogs | 1992 |
| 24234.0 | I don't know what he did to her, but she got even. :-()-: Was he all pissed off? | reservoir dogs | 1992 |
| 24251.0 | Say "hello" to a motherfucker who's inside. Cabot's doing a job and take a big fat guess who he wants on the team? :-()-: This better not be some Freddy joke. | reservoir dogs | 1992 |
| 24328.0 | Is that supposed to be funny? :-()-: We don't think this place is safe. :-()-: This place just ain't secure anymore. We're leaving, and you should go with us. | reservoir dogs | 1992 |
| 24273.0 | Why can't we pick out our own colors? :-()-: I tried that once, it don't work. You get four guys fighting over who's gonna be Mr. Black. Since nobody knows anybody else, nobody wants to back down. So forget it, I pick. Be thankful you're not Mr. Yellow. | reservoir dogs | 1992 |
| 24301.0 | Say-the-goddamn-words: You're gonna be okay! :-()-: I'm okay. :-()-: Correct. | reservoir dogs | 1992 |
| 24333.0 | Okay, Mr. Expert. If this is such a truism, how come every nigger I know treats his woman like a piece of shit? :-()-: I'll make you a bet that those same damn niggers who were showin' their ass in public, when their bitches get 'em home, they chill the fuck out. :-()-: Not these guys. :-()-: Yeah, those guys too. | reservoir dogs | 1992 |
| 24311.0 | These ladies aren't starvin' to death. They make minimum wage. When I worked for minimum wage, I wasn't lucky enough to have a job that society deemed tipworthy. :-()-: Ahh, now we're getting down to it. It's not just that he's a cheap bastard - | reservoir dogs | 1992 |
| 24289.0 | Aren't you? :-()-: I don't have the slightest fuckin' idea what you're talkin' about. | reservoir dogs | 1992 |
| 24253.0 | What kinds questions did Cabot ask? :-()-: Where I was from, who I knew, how I knew Nice Guy, had I done time, shit like that. | reservoir dogs | 1992 |
| 24330.0 | I told ya he'd be pissed. :-()-: What are you gonna do about him? | reservoir dogs | 1992 |
| 24227.0 | How ya doin', Toothpick? :-()-: Fine, now. :-()-: I'm sorry man, I shoulda picked you up personally at the pen. This whole week's just been crazy. I've had my head up my ass the entire time. :-()-: Funny you should mention it. That's what your father and I been talkin' about. :-()-: That I should've picked you up? :-()-: No. That your head's been up your ass. I walk through the door and Joe says "Vic, you're back, thank god. Finally somebody who knows what the fuck he's doing. Vic, Vic, Vic, Eddie, my son, is a fuck up." And I say "Well, Joe, I coulda told you that." "I'm ruined! He's ruining me! My son, I love him, but he's taking my business and flushing it down the fuckin' toilet! " I'm not tellin' tales out of school. You tell 'im Joe. Tell 'im yourself. | reservoir dogs | 1992 |
| 24294.0 | What's this guy's problem? :-()-: What's my problem? Yeah, I gotta problem. I gotta big problem with any trigger-happy madman who almost gets me shot! :-()-: What're you talkin' about? :-()-: That fuckin' shooting spree in the store. :-()-: Fuck 'em, they set off the alarm, they deserve what they got. :-()-: You almost killed me, asshole! If I had any idea what type of guy you were, I never would've agreed to work with you. :-()-: You gonna bark all day, little doggie, or are you gonna bite? :-()-: What was that? I'm sorry, I didn't catch it. Would you repeat it? :-()-: I said: "Are you gonna bark all day, dog, or are you gonna bite." | reservoir dogs | 1992 |
| 24244.0 | We were set up, the cops were waiting for us. :-()-: What? Nobody set anybody up. :-()-: The cops were there waitin' for us! :-()-: Bullshit. :-()-: Hey, fuck you man, you weren't there, we were. And I'm tellin' ya, the cops had that store staked out. :-()-: Okay, Mr. Detective, who did it? :-()-: What the fuck d'you think we've been askin' each other? :-()-: And what are your answers? Was it me? You think I set you up? :-()-: I don't know, but somebody did. :-()-: Nobody did. You assholes turn the jewelry store into a wild west show, and you wonder why cops show up. | reservoir dogs | 1992 |
| 24268.0 | Hey, fuck all that, I'm making a point here. You're gonna make me lose my train of thought. :-()-: Oh fuck, Toby's that little china girl. | reservoir dogs | 1992 |
| 24323.0 | Why! :-()-: I told him where I was from a few days ago. It was just a casual conversation. :-()-: And what was tellin him your name when you weren't supposed to? :-()-: He asked. | reservoir dogs | 1992 |
| 24280.0 | I'll take care of this, you guys leave the tip. And when I come back, I want my book back. :-()-: Sorry, it's my book now. :-()-: Blonde, shoot this piece of shit, will ya? | reservoir dogs | 1992 |
| 24230.0 | I don't wanna lift crates. :-()-: You don't hafta lift shit. You don't really work there. But as far as the records are concerned, you do. I call up Matthews, the foreman, tell him he's got a new guy. You're on the schedule. You got a timecard, it's clocked in and out for you everyday, and you get a pay check at the end of the week. And ya know dock workers don't do too bad. So you can move into a halfway decent place without Scagnetti thinkin "what the fuck." And if Scagnetti ever wants to make a surprise visit, you're gone that day. That day we sent you to Tustin. We gotta bunch of shit you needed to unload there. You're at the Taft airstrip pickin' up a bunch of shit and bringing it back. Part of your job is goin' different places - and we got places all over the place. | reservoir dogs | 1992 |
| 24279.0 | No, she did it. She killed the cheatin' wife, too. :-()-: Who gives a damn? | reservoir dogs | 1992 |
| 24231.0 | Holy shit, this guy's all fucked up! :-()-: No shit, he's gonna fuckin' die on us if we don't get him taken care of. | reservoir dogs | 1992 |
| 24329.0 | Both of you two assholes knock it the fuck off and calm down! :-()-: So you wanna git bit, huh? :-()-: Cut the bullshit, we ain't on a fuckin' playground! I don't believe this shit, both of you got ten years on me, and I'm the only one actin like a professional. You guys act like a bunch of fuckin' niggers. You ever work a job with a bunch of niggers? They're just like you two, always fightin', always sayin' they're gonna kill one another. :-()-: You said yourself, you thought about takin' him out. :-()-: Then. That time has passed. Right now, Mr. Blonde is the only one I completely trust. He's too fuckin' homicidal to be workin' with the cops. :-()-: You takin' his side? :-()-: Fuck sides! What we need is a little solidarity here. Somebody's stickin' a red hot poker up our asses and we gotta find out whose hand's on the handle. Now I know I'm no piece of shit... And I'm pretty sure you're a good boy... And I'm fuckin positive you're on the level. So let's figure out who's the bad guy. | reservoir dogs | 1992 |
| 24248.0 | Go ahead and laugh, you know what I mean. What a while bitch will put up with, a black bitch won't put up with for a minute. They got a line, and if you cross it, they fuck you up. :-()-: I gotta go along with Mr. Pink on this. I've seen it happen. | reservoir dogs | 1992 |
| 24239.0 | Now Vic was tellin' me, he's got a parole problem. :-()-: Really? Who's your P.O.? | reservoir dogs | 1992 |
| 24225.0 | Alone at last. :-()-: Now where were we? :-()-: I told you I don't know anything about any fucking set up. I've only been on the force eight months, nobody tells me anything! I don't know anything! You can torture me if you want - :-()-: Thanks, don't mind if I do. :-()-: Your boss even said there wasn't a set up. :-()-: First off, I don't have a boss. Are you clear about that? | reservoir dogs | 1992 |
| 24327.0 | He got it in the belly. He's still alive, but won't be for long. :-()-: Enough! You better start talkin' to us, asshole, cause we got shit we need to talk about. We're already freaked out, we need you actin freaky like we need a fuckin' bag on our hip. | reservoir dogs | 1992 |
| 24271.0 | Because you paid for the breakfast, I'm gonna tip. Normally I wouldn't. :-()-: Whatever. Just throw in your dollar, and let's move. See what I'm dealing with here. Infants. I'm fuckin' dealin' with infants. | reservoir dogs | 1992 |
| 24331.0 | He seems all right now, but he went crazy in the store. :-()-: This is what he was doin'. | reservoir dogs | 1992 |
| 24261.0 | I'm sorry. :-()-: What? Snap out of it! | reservoir dogs | 1992 |
| 24296.0 | For what, the cops? :-()-: Nice Guy Eddie. | reservoir dogs | 1992 |
| 24258.0 | Jesus Christ! :-()-: You can do some crazy things with it. | reservoir dogs | 1992 |
| 24277.0 | What's that? :-()-: I found this old address book in a jacket I ain't worn in a coon's age. Toby what? What the fuck was her last name? | reservoir dogs | 1992 |
| 24272.0 | But he's OK. If he wasn't OK, he wouldn't be here. Okay, let me introduce everybody to everybody. But once again, at the risk of being redundant, if I even think I hear somebody telling or referring to somebody by their Christian name... ...you won't want to be you. Okay, quickly. Mr. Brown, Mr. White, Mr. Blonde, Mr. Blue, Mr. Orange, and Mr. Pink. :-()-: Why am I Mr. Pink? :-()-: Cause you're a faggot, alright? | reservoir dogs | 1992 |
| 24281.0 | What's she doin' now? :-()-: She hooked up with Fed McGar, they've done a couple a jobs together. Good little thief. So, explain the telegram. :-()-: Five-man job. Bustin' in and bustin' out of a diamond wholesaler's. :-()-: Can you move the ice afterwards? I don't know nobody who can move ice. :-()-: Not a problem, got guys waitin' for it. But what happened to Marsellus Spivey? Didn't he always move your ice? :-()-: He's doin' twenty years in Susanville. :-()-: What for? :-()-: Bad luck. What's the exposure like? :-()-: Two minutes, tops. It's a tough two minutes. It's daylight, during business hours, dealing with a crowd. But you'll have the guys to deal with the crowd. :-()-: How many employees? :-()-: Around twenty. Security pretty lax. They almost always just deal in boxes. Rough uncut stones they get from the syndicate. On a certain day this wholesaler's gettin' a big shipment of polished stones from Israel. They're like a way station. They are gonna get picked up the next day and sent to Vermont. :-()-: No, they're not. | reservoir dogs | 1992 |
| 24325.0 | We ain't taking him to a hospital. :-()-: If we don't, he'll die. :-()-: And I'm very sad about that. But some fellas are lucky, and some ain't. :-()-: That fuckin did it! | reservoir dogs | 1992 |
| 24275.0 | Nobody's trading with anybody! Look, this ain't a goddamn fuckin' city counsel meeting! Listen up Mr. Pink. We got two ways here, my way or the highway. And you can go down either of 'em. So what's it gonna be, Mr. Pink? :-()-: Jesus Christ, Joe. Fuckin' forget it. This is beneath me. I'm Mr. Pink, let's move on. | reservoir dogs | 1992 |
| 24318.0 | Tagged a couple of cops. Did you kill anybody? :-()-: A few cops. :-()-: No real people? :-()-: Uh-uh, just cops. :-()-: Could you believe Mr. Blonde? :-()-: That was one of the most insane fucking things I've ever seen. Why the fuck would Joe hire somebody like that? :-()-: I don't wanna kill anybody. But if I gotta get out that door, and you're standing in my way, one way of the other, you're gettin' outta my way. :-()-: That's the way I look at it. A choice between doin' ten years, and takin' out some stupid motherfucker, ain't no choice at all. But I ain't no madman either. What the fuck was Joe thinkin'? You can't work with a guy like that. That motherfucker's unstable. What do you think? Do you think he panicked, or ya think he's just trigger-happy? :-()-: I think he's a sick fuckin' maniac! We're awful goddamn lucky he didn't tag us, when he shot up the place. I came this fucking close - to taking his ass out myself. Everybody panics. When things get tense, everybody panics. Everybody. I don't care what your name is, you can't help it. It's human nature. But ya panic on the inside. Ya panic in your head. Ya give yourself a couple a seconds of panic, then you get a grip and deal with the situation. What you don't do, is shoot up the place and kill everybody. :-()-: What you're supposed to do is act like a fuckin' professional. A psychopath is not a professional. You can't work with a psychopath, 'cause ya don't know what those sick assholes are gonna do next. I mean, Jesus Christ, how old do you think that black girl was? Twenty, maybe twenty-one? :-()-: Did ya see what happened to anybody else? :-()-: Me and Mr. Orange jumped in the car and Mr. Brown floored it. After that, I don't know what went down. :-()-: At that point it became every man for himself. As far as Mr. Blonde or Mr. Blue are concerned, I ain't got the foggiest. Once I got out, I never looked back. :-()-: What do you think? :-()-: What do I think? I think the cops caught them, or killed 'em. :-()-: Not even a chance they punched through? You found a hole. :-()-: Yeah, and that was a fucking miracle. But if they did get away, where the fuck are they? :-()-: You don't think it's possible, one of them got a hold of the diamonds and pulled a - :-()-: Nope. :-()-: How can you be so sure? :-()-: I got the diamonds. :-()-: Where? :-()-: I got 'em, all right? :-()-: Where? Are they out in the car? :-()-: No, they're not in the car. No, I don't have them on me. Ya wanna go with me and get 'em? Yes, we can go right now. But first listen to what I'm telling you. We were fuckin' set up! Somebody is in league with the cops. We got a Judas in our midst. And I'm thinkin' we should have our fuckin' heads examined for waiting around here. :-()-: That was the plan, we meet here. :-()-: Then where is everybody? I say the plan became null and void once we found out we got a rat in the house. We ain't got the slightest fuckin' idea what happened to Mr. Blonde or Mr. Blue. They could both be dead or arrested. They could be sweatin' 'em, down at the station house right now. Yeah they don't know the names, but they can sing about this place. I mean, that could be happening right now. As we speak, the cops could be in their cars, drivin' here this minute. :-()-: I swear to god I'm fuckin' jinxed. :-()-: What? :-()-: Two jobs back, it was a four man job, we discovered one of the team was an undercover cop. :-()-: No shit? :-()-: Thank god, we discovered in time. We hadda forget the whole fuckin' thing. Just walked away from it. :-()-: So who's the rat this time? Mr. Blue? Mr. Blonde? Joe? It's Joe's show, he set this whole thing up. Maybe he set it up to set it up. :-()-: I don't buy it. Me and Joe go back a long time. I can tell ya straight up, Joe definitely didn't have anything to do with this bullshit. :-()-: Oh, you and Joe go back a long time. I known Joe since I was a kid. But me saying Joe definitely couldn't have done it is ridiculous. I can say I definitely didn't do it, cause I know what I did or didn't do. But I can't definitely say that about anybody else, 'cause I don't definitely know. For all I know, you're the rat. :-()-: For all I know, you're the rat. :-()-: Now you're using your head. For all we know, he's the rat. :-()-: That kid in there is dying from a fuckin' bullet that I saw him take. So don't be calling him a rat. :-()-: Look, asshole, I'm right! Somebody's a fuckin' rat. How many times do I hafta say it before it sinks in your skull? | reservoir dogs | 1992 |
| 24292.0 | Joe, you want me to shoot him for you? :-()-: Shit, you shoot me in a dream, you better wake up and apologize. | reservoir dogs | 1992 |
| 24310.0 | I'd go over twelve percent for that. :-()-: Look, I ordered coffee. Now we've been here a long fuckin' time, and she's only filled my cup three times. When I order coffee, I want it filled six times. | reservoir dogs | 1992 |
| 24324.0 | We had just gotten away from the cops. He just got shot. It was my fuckin' fault he got shot. He's a fuckin' bloody mess - he's screaming. I swear to god, I thought we was gonna die right then and there. I'm tryin' to comfort him, telling him not to worry, he's gonna be okay, I'm gonna take care of him. And he asked me what my name was. I mean, the man was dyin' in my arms. What the fuck was I supposed to tell him, "Sorry, I can't give out that information, it's against the rules. I don't trust you enough."? Maybe I shoulda, but I couldn't. :-()-: Oh, I don't doubt is was quite beautiful - :-()-: Don't fuckin' patronize me. :-()-: One question: Do they have a sheet on you, where you told him you're from? :-()-: Of course. :-()-: Well that's that, then. I mean, I was worried about mug shot possibilities already. But now he knows: what you look like, what your first name is, where you're from and what your specialty is. They ain't gonna hafta show him a helluva lot of pictures for him to pick you out. That's it right, you didn't tell him anything else that could narrow down the selection? :-()-: If I have to tell you again to back off, me an you are gonna go round and round. | reservoir dogs | 1992 |
| 24290.0 | I know. :-()-: You do? :-()-: Your name's Freddy something. :-()-: Freddy Newendyke. :-()-: Frankie Ferchetti introduced us once, about five months ago. :-()-: Shit. I don't remember that at all. :-()-: I do. How do I look? | reservoir dogs | 1992 |
| 24240.0 | We can work this out, can't we? :-()-: This isn't all that bad. We can give you a lot of legitimate jobs. Put you on the rotation at Long Beach as a dock worker. | reservoir dogs | 1992 |
| 24269.0 | Then one day she meets a John Holmes motherfucker, and it's like, whoa baby. This mother fucker's like Charles Bronson in "The Great Escape." He's diggin' tunnels. Now she's gettin' this serious dick action, she's feelin' something she ain't felt since forever. Pain. :-()-: Chew? Toby Chew? No. :-()-: It hurts. It hurts her. It shouldn't hurt. Her pussy should be Bubble-Yum by now. But when this cat fucks her, it hurts. It hurts like the first time. The pain is reminding a fuck machine what is was like to be a virgin. Hence, "Like a Virgin." | reservoir dogs | 1992 |
| 24312.0 | Do you know what this is? It's the world's smallest violin, playing just for the waitresses. :-()-: You don't have any idea what you're talking about. These people bust their ass. This is a hard job. :-()-: So's working at McDonald's, but you don't feel the need to tip them. They're servin' ya food, you should tip em. But no, society says tip these guys over here, but not those guys over there. That's bullshit. | reservoir dogs | 1992 |
| 24260.0 | Myself and Mr. Pink? :-()-: You two take the manager in the back and make him give you the diamonds. We're there for those stones, period. Since no display cases are being fucked with, no alarms should go off. We're out of there in two minutes, not one second longer. What if the manager won't give up the diamonds? :-()-: When you're dealing with a store like this, they're insured up the ass. They're not supposed to give you and resistance whatsoever. If you get a customer or an employee who thinks he's Charles Bronson, take the butt of your gun and smash their nose in. Drops 'em right to the floor. Everyone jumps, he falls down, screaming, blood squirts out his nose. Freaks everybody out. Nobody says fuckin' shit after that. You might get some bitch talk shit to ya. But give her a look, like you're gonna smash her in the face next. Watch her shut the fuck up. Now if it's a manager, that's a different story. The managers know better than to fuck around. So if one's givin' you static, he probably thinks he's a real cowboy. So what you gotta do is break that son-of-a-bitch in two. If you wanna know something and he won't tell you, cut off one of his fingers. The little one. Then you tell 'im his thumb's next. After that he'll tell ya if he wears ladies underwear. I'm hungry, let's get a taco. | reservoir dogs | 1992 |
| 24302.0 | Just hold on buddy boy. Hold on, and wait for Joe. I can't do anything for you, but when Joe gets here, which should be anytime now, he'll be able to help you. We're just gonna sit here, and wait for Joe. Who are we waiting for? :-()-: Joe. :-()-: Bet your sweet ass we are. | reservoir dogs | 1992 |
| 24313.0 | Waitressing is the number one occupation for female non-college graduates in this country. It's the one job basically any woman can get, and make a living on. The reason is because of tips. :-()-: Fuck all that. | reservoir dogs | 1992 |
| 24270.0 | Wong? :-()-: Fuck you, wrong. I'm right! What the fuck do you know about it anyway? You're still listening to Jerry- fucking-Vale. :-()-: Not wrong, dumb ass, Wong! You know, like the Chinese name? | reservoir dogs | 1992 |
| 24298.0 | Because this guy's a fucking psycho. And if you think Joe's pissed at us, that ain't nothing compared to how pissed off I am at him, for puttin' me in the same room as this bastard. :-()-: You see what I been puttin' up with? As soon as I walk through the door I'm hit with this shit. I tell 'm what you told me about us stayin' put and Mr. White whips out his gun, sticks it in my face, and starts screaming "You motherfucker, I'm gonna blow you away, blah, blah, blah." :-()-: He's the reason the place turned into a shooting gallery. What are you, a silent partner? Fuckin' tell him. | reservoir dogs | 1992 |
| 24252.0 | Nice Guy. When we got to the bar... :-()-: ...What bar? :-()-: The Boots and Socks in Gardena. When we got there, I met Joe and a guy named Mr. White. It's a phony name. My name's Mr. Orange. :-()-: You ever seen this motherfucker before? :-()-: Who, Mr. White? :-()-: Yeah. :-()-: No, he ain't familiar. He ain't one of Cabot's soldiers either. He's gotta be from outta town. But Joe knows him real well. :-()-: How can you tell? :-()-: The way they talk to each other. You can tell they're buddies. :-()-: Did the two of you talk? :-()-: Me and Mr. White? :-()-: Yeah. :-()-: A little. :-()-: What about? :-()-: The Brewers. :-()-: The Milwaukee Brewers? :-()-: Yeah. They had just won the night before, and he made a killing off 'em. :-()-: Well, if this crook's a Brewers fan, his ass has gotta be from Wisconsin. And I'll bet you everything from a diddle-eyed Joe to a damned-if-I- know, that in Milwaukee they got a sheet on this Mr. White motherfucker's ass. I want you to go through the mugs of guys from old Milwaukee with a history of armed robbery, and put a name to that face. | reservoir dogs | 1992 |
| 24306.0 | Uhuh, uhuh, what's I tell ya? That sick piece of shit was a stone cold psycho. :-()-: You could've asked the cop, if you didn't just kill him. He talked about what he was going to do when he was slicing him up. | reservoir dogs | 1992 |
| 24233.0 | Let me tell you guys a story. In one of daddy's clubs there was this black cocktail waitress named Elois. :-()-: Elois? :-()-: Yeah, Elois. E and Lois. We called her Lady E. :-()-: Where was she from, Compton? :-()-: No. She was from Ladora Heights. | reservoir dogs | 1992 |
| 24232.0 | Jesus Christ, give me a fuckin' chance to breathe. I got a few questions of my own, ya know. :-()-: You ain't dying, he is. :-()-: I'll call somebody. :-()-: Who? :-()-: A snake charmer, what the fuck d'you think. I'll call a doctor, take care of him, fix 'm right up. No, where's Mr. Brown and Mr. Blue? | reservoir dogs | 1992 |
| 24257.0 | ...Her brother usually goes with her, but he's in county unexpectedly. :-()-: What for? :-()-: Traffic tickets gone to warrant. They stopped him for something, found the warrants on 'im, took 'im to jail. She doesn't want to walk around alone with all that weed. Well, I don't wanna do this, I have a bad feeling about it, but she keeps askin' me, keeps askin' me, finally I said okay 'cause I'm sick of listening to it. Well, we're picking this guy up at the train station. | reservoir dogs | 1992 |
| 24326.0 | You wanna shoot me, you little piece of shit? Take a shot! :-()-: Fuck you, White! I didn't create this situation, I'm just dealin' with it. You're acting like a first- year fuckin' thief. I'm actin like a professional. They get him, they can get you, they get you, they get closer to me, and that can't happen. And you, you motherfucker, are looking at me like it's my fault. I didn't tell him my name. I didn't tell him where I was from. I didn't tell him what I knew better than to tell him. Fuck, fifteen minutes ago, you almost told me your name. You, buddy, are stuck in a situation you created. So if you wanna throw bad looks somewhere, throw 'em at a mirror. | reservoir dogs | 1992 |
| 24250.0 | He's right about the ear, it's hacked off. :-()-: Let me say this out loud, just to get it straight in my mind. According to you, Mr. Blonde was gonna kill you. Then when we came back, kill us, grab the diamonds, and scram. That's your story? I'm correct about that, right? | reservoir dogs | 1992 |
| 24262.0 | Just hold on buddy boy. :-()-: I'm sorry. I can't believe she killed me... | reservoir dogs | 1992 |
| 24299.0 | I told 'em not to touch the alarm. They touched it. I blew 'em full of holes. If they hadn't done what I told 'em not it, they'd still be alive. :-()-: That's your excuse for going on a kill crazy rampage? :-()-: I don't like alarms. | reservoir dogs | 1992 |
| 24243.0 | Daddy, I'm sorry, I don't know what's happening. :-()-: That's okay, Eddie, I do. | reservoir dogs | 1992 |
| 24236.0 | The motherfucker killed Vic. :-()-: How do you know all this? | reservoir dogs | 1992 |
| 24288.0 | Mr. Pink. :-()-: Mr. Pink? Why? :-()-: He don't tip. :-()-: He don't tip? You don't tip? Why? :-()-: He don't believe in it. :-()-: He don't believe in it? You don't believe in it? :-()-: Nope. :-()-: Shut up! Cough up the buck, ya cheap bastard, I paid for your goddamn breakfast. :-()-: Okay ramblers, let's get to rambling. Wait a minute, who didn't throw in? | reservoir dogs | 1992 |
| 24266.0 | That's great, guy, thanks a bunch. When do you think you'll need me for real work? :-()-: Well, it's kinda a strange time right now. Things are kinda - | reservoir dogs | 1992 |
| 24305.0 | What happened? :-()-: Blonde went crazy. He slashed the cop's face, cut off his ear and was gonna burn him alive. | reservoir dogs | 1992 |
| 24317.0 | Okay, let's go through what happened. We're in the place, everything's going fine. Then the alarm gets tripped. I turn around and all these cops are outside. You're right, it was like, bam! I blink my eyes are they're there. Everybody starts going apeshit. Then Mr. Blonde starts shootin' all the - :-()-: That's not correct. :-()-: What's wrong with it? :-()-: The cops didn't show up after the alarm went off. They didn't show till after Mr. Blonde started shooting everyone. :-()-: As soon as I heard the alarm, I saw the cops. :-()-: I'm telling ya, it wasn't that soon. They didn't let their presence be known until after Mr. Blonde went off. I'm not sayin' they weren't there, I'm sayin' they were there. But they didn't move in till Mr. Blonde became a madman. That's how I know we were set up. You can see that, can't you, Mr. White? :-()-: Look, enough of this "Mr White" shit - :-()-: Don't tell me your name, I don't want to know! I sure as hell ain't gonna tell ya mine. :-()-: You're right, this is bad. How did you get out? :-()-: Shot my way out. Everybody was shooting, so I just blasted my way outta there. | reservoir dogs | 1992 |
| 24255.0 | What's this? :-()-: It's a scene. Memorize it. :-()-: What? :-()-: A undercover cop has got to be Marlon Brando. To do this job you got to be a great actor. You got to be naturalistic. You got to be naturalistic as hell. If you ain't a great actor you're a bad actor, and bad acting is bullshit in this job. :-()-: But what is this? :-()-: It's a amusing anecdote about a drug deal. :-()-: What? :-()-: Something funny that happened to you while you were doing a job. :-()-: I gotta memorize all this shit? :-()-: It's like a joke. You remember what's important, and the rest you make your own. The only way to make it your own is to keep sayin' it, and sayin' it, and sayin' it, and sayin' it, and sayin' it. :-()-: I can do that. :-()-: The things you gotta remember are the details. It's the details that sell your story. Now this story takes place in this men's room. So you gotta know the details about this men's room. You gotta know they got a blower instead of a towel to dry your hands. You gotta know the stalls ain't got no doors. You gotta know whether they got liquid or powdered soap, whether they got hot water or not, 'cause if you do your job when you tell your story, everybody should believe it. And if you tell your story to somebody who's actually taken a piss in this men's room, and you get one detail they remember right, they'll swear by you. | reservoir dogs | 1992 |
| 24309.0 | I don't even know a Jew who'd have the balls to say that. So let's get this straight. You never ever tip? :-()-: I don't tip because society says I gotta. I tip when somebody deserves a tip. When somebody really puts forth an effort, they deserve a little something extra. But this tipping automatically, that shit's for the birds. As far as I'm concerned, they're just doin' their job. | reservoir dogs | 1992 |
| 24332.0 | ...Hey, I know what I'm talkin' about, black women ain't the same as white women. :-()-: There's a slight difference. | reservoir dogs | 1992 |
| 24238.0 | Daddy, did ya see that? :-()-: What? :-()-: Guy got me on the ground, tried to fuck me. | reservoir dogs | 1992 |
| 24256.0 | Tell me more about Cabot. :-()-: He's a cool guy. A real nice, real funny, real cool guy. | reservoir dogs | 1992 |
| 24316.0 | Want a smoke? :-()-: Why not? | reservoir dogs | 1992 |
| 4074.0 | A baby? What are we supposed to do with a baby? :-()-: Name it. | birthday girl | 2001 |
| 4094.0 | You are not ashamed of it? It's no surprise to want to love. :-()-: No. It's not that. :-()-: Do you believe in love? :-()-: I suppose it's... I mean define your terms. :-()-: It's very strange. How many people are truly themselves with their love? It is the greatest human disaster and it is never in the newspapers. There are no Marches Against Heartache, no Ministries Against Loneliness, no Concerts Against Disappointment. We look away. And still we know in secret that nothing is more important to us. The one thing we all share but don't say. Look John I will show you something. | birthday girl | 2001 |
| 4150.0 | My name's Sophia. :-()-: Sophia. Hello Sophia. Mine's still John. | birthday girl | 2001 |
| 4113.0 | They go. John. They go. :-()-: What's wrong? :-()-: They go. :-()-: Of course. They go. Yes. Yes. :-()-: They go. | birthday girl | 2001 |
| 4077.0 | This is sensitive. Your car. Lovely car. Doesn't necessarily give the right impression. :-()-: Ch... :-()-: To customers approaching the bank from the rear :-()-: Right. :-()-: You can see why it's sensitive? :-()-: Uh... Yes. | birthday girl | 2001 |
| 4149.0 | Hurry. I'll wait for you here. :-()-: Right. | birthday girl | 2001 |
| 4091.0 | When? :-()-: "I saw you waiting there, by the gate." :-()-: I... :-()-: "I have these uh..." She explains to you... "When I was a little girl my father had these beautiful old glasses." Like... I don't know the word. Like for watching uh... for watching the birds. | birthday girl | 2001 |
| 4129.0 | It wasn't what I wanted. :-()-: So what did you want? I think we understand each other, no? :-()-: You don't understand me. :-()-: You don't understand you either. | birthday girl | 2001 |
| 4067.0 | Yeah it was okay. :-()-: Yeah. It was quite good actually. Some bits I really liked. :-()-: The sets were good. :-()-: The sets were excellent. Everything was big, you know, all the rubbish, coke cans, sweet wrappers, dustbins, so when you were watching it you felt cat size. It was really clever. | birthday girl | 2001 |
| 4098.0 | He says you scare him so much he must go to the toilet in his trousers. John, he is a soldier. A trained killer. We must do what he says. :-()-: What? What does he want? | birthday girl | 2001 |
| 4140.0 | You know you can come under the blanket. :-()-: It's alright. | birthday girl | 2001 |
| 4083.0 | Hold on. :-()-: Toast first then we talk seriously, I can see you are serious about us. | birthday girl | 2001 |
| 4065.0 | Put it in the cases. Split it up. And don't forget you owe me �150. :-()-: What for? :-()-: You know what for. :-()-: No I don't. :-()-: I got you those trousers from Paul Smith. :-()-: I've been buying you stuff all week. I've been buying him stuff all week. :-()-: Such as? | birthday girl | 2001 |
| 4081.0 | What did he say? :-()-: He says he feels safe here. | birthday girl | 2001 |
| 4104.0 | It's about forty miles from here. I don't know if you've looked at a map, it's close to London but it's a city in itself. A Roman city. It's a nice house. I'm having a problem with ants. I uh... It's the warmer weather. I can't seem to find the nest. Sorry, do you understand "ants"? :-()-: Yes. :-()-: I just can't find a nest. The root of the problem. I've looked everywhere. What's the Russian for ant? Sorry that's a stupid... Sorry. This is strange isn't it. :-()-: Yes. :-()-: I'm pretty nervous. Are you? :-()-: Yes. :-()-: I mean... "Ants." "I've got a problem with ants." | birthday girl | 2001 |
| 4148.0 | What does it mean? :-()-: Maybe you will find out. | birthday girl | 2001 |
| 4125.0 | I don't want to talk about it. :-()-: Why not? :-()-: Shut up. I'm not listening. :-()-: You don't want to talk about it. :-()-: No. :-()-: Okay we won't talk about it. | birthday girl | 2001 |
| 4152.0 | You have excellent English. :-()-: Thanks. :-()-: How do you want to pay? :-()-: Cash. | birthday girl | 2001 |
| 4117.0 | You must think... I'm the biggest pillock... In the world. :-()-: No I don't. :-()-: In the world. :-()-: I know you just want to punish me -- :-()-: I do. I want to very badly. :-()-: So you're just going to be vindictive :-()-: In every sense. If at all possible. :-()-: You can't hurt me more than I'm hurt already. :-()-: Well, Nadia, It it's all the same to you, I'd like to give it a bash. | birthday girl | 2001 |
| 4076.0 | What. You're what? You're with this creep now. :-()-: Leave him! :-()-: You have. You've actually fallen for this prick. :-()-: No I haven't. | birthday girl | 2001 |
| 4139.0 | I don't know. :-()-: What was her name? :-()-: What's your name? | birthday girl | 2001 |
| 4084.0 | So hang on. You're both Nadia's cousins? :-()-: Of course not. Alexei, he's is my problem. :-()-: Right. :-()-: We better watch him. He's crazy. :-()-: Right. :-()-: I am actor, he is actor, although he is an actor stroke musician. I just noodle along, I'm not so good. He makes me look like a retard -- He smokes me. I don't mean he smokes me. | birthday girl | 2001 |
| 4099.0 | What did he say? Tell me! :-()-: He says you are very sad ridiculous man. I don't agree of course. And that you must pay someone to have sex like a prostitute. Nadia is a prostitute. I'm sorry. :-()-: What does he want. The Russian shithead. What do you want ? :-()-: He wants money. :-()-: Tell him to put the kettle down and I'll give him money. | birthday girl | 2001 |
| 4075.0 | What? What are you doing? :-()-: What are you doing here? | birthday girl | 2001 |
| 4085.0 | No. :-()-: Right. So I can say he smokes me. So. | birthday girl | 2001 |
| 4134.0 | What happened between you and the blonde? :-()-: What? :-()-: The thin... the girl with small eyes. The one in your cupboard. :-()-: It's none of your business. She didn't have small eyes. :-()-: Did she leave you? Come on. It's nothing to be ashamed of. Who did she leave you for? Your best friend? Her boss? A woman? Did she leave you for a woman, John? :-()-: She's dead. | birthday girl | 2001 |
| 4116.0 | So what are you going to do? :-()-: I'm going to drink my coffee. Then, we're going to the police station. Where there will be lawyers, loss of job, house, humiliation, gutter press, and probably prison. :-()-: They don't blame you. When a bank employee does this they understand. You get your life back. Anyway I bet you hated that bank. :-()-: Even so I always felt the decision to burst in and rob it very much remained with me. :-()-: Why else would you send off for me? If you just wanted sex just go to a prostitute. :-()-: Well as it turns out I did. | birthday girl | 2001 |
| 4142.0 | Yeah. No thanks. :-()-: Please. Why not? :-()-: Because it was a lie. | birthday girl | 2001 |
| 4123.0 | So uh... :-()-: Look, if you want to know is he better in bed than you then yes he is. :-()-: Oh Jesus. :-()-: If what you want to know is does he have a bigger cock than you, then yes he does. :-()-: Of course. Of course. Of course he does. Of course. Thank you. Thanks. :-()-: But, you know, so what? | birthday girl | 2001 |
| 4111.0 | Syevodnya? :-()-: Syevodnya :-()-: Happy Birthday. Happy Birthday. | birthday girl | 2001 |
| 4096.0 | I understand. I'm so sorry :-()-: You can stay tonight. :-()-: I have brought you trouble. Maybe I should have come alone. :-()-: Good night. | birthday girl | 2001 |
| 4070.0 | What's this? :-()-: It's nothing. I burnt myself. :-()-: That's not a burn. :-()-: It is. I did it cooking. | birthday girl | 2001 |
| 4122.0 | So, uh, Alexei, which I know isn't his name... :-()-: I don't want to talk about him. :-()-: Fine. :-()-: It's none of your business. :-()-: Fine. Absolutely. Must be disappointing though. Must come as a hell of a shock. | birthday girl | 2001 |
| 4108.0 | Uh... Are you a giraffe? :-()-: Yes. | birthday girl | 2001 |
| 4106.0 | And how was the flight. Sorry, am I speaking too fast for you? :-()-: Yes. | birthday girl | 2001 |
| 4109.0 | Today is bath day. :-()-: Sorry? | birthday girl | 2001 |
| 4095.0 | How is bank? :-()-: Fine. I thought you were leaving today. :-()-: To be indoors on such a day. It's crime. | birthday girl | 2001 |
| 4105.0 | So. Is it different to how you imagined it? :-()-: Yes. :-()-: I bet. What about me? Am I how you imagined? :-()-: Yes. | birthday girl | 2001 |
| 4082.0 | I need to know who you are first please. :-()-: Oh. We are Russian. :-()-: Yes. I know. :-()-: Good. :-()-: And... :-()-: And what? You mean from the beginning? Jesus. Can I uh okay, as we say in Russia can I cut a long story short. Okay. Nadia is my little cousin. Except she's not. But we say cousin. This is for you. | birthday girl | 2001 |
| 4097.0 | Put the fucking kettle down. :-()-: John. :-()-: Put the fucking kettle down. Tell, Yuri, tell him put it down or I'm going to make him. | birthday girl | 2001 |
| 4146.0 | It's not mine. :-()-: It's not mine either. :-()-: It's what you came back for. | birthday girl | 2001 |
| 4124.0 | Not the kids type then is he? Not that broody. You must be pretty miffed. :-()-: He will come back. :-()-: Excuse me? :-()-: He left me my passport and ticket. It's pretty clear he wants to see me again. :-()-: Yeah. I tend to tie up and abandon women I really want to see again too. :-()-: No. But you tend to tie them up. | birthday girl | 2001 |
| 4103.0 | Is that everything? :-()-: Yes. :-()-: Right. Okay. Good. | birthday girl | 2001 |
| 4093.0 | What was that? :-()-: Oh nothing. :-()-: Tell me. :-()-: No. It is too judgmental. :-()-: Tell me what he said. :-()-: He says why did you send to Russia for a wife. | birthday girl | 2001 |
| 4131.0 | You know, in Russia, there's no work for women. It's a different world. :-()-: You don't have to say anything :-()-: What? I... I wasn't saying... :-()-: Please, there's no... Oh. :-()-: I wasn't saying anything. :-()-: Then okay. So how old were you when you met him? | birthday girl | 2001 |
| 4126.0 | We'll pretend it never happened. :-()-: So. What's it like having to fuck men you hate? :-()-: I don't hate you. :-()-: Okay. Let's... Okay. Okay. You have had sex with people you don't like haven't you? For money. To make money. :-()-: And? What are you saying? :-()-: And. It's wrong. :-()-: And who says what is wrong. :-()-: And that would be Morals. That would be one's own moral sense of decency. :-()-: What's a moral orgasm John? Tell me how it feels exactly. :-()-: So. What then? You just detach sex from everything.. :-()-: Whereas "Wet 'n' Wild" is an emotional journey. "Tied and Tethered". It's pretty moving huh? Like Anna Karenina. :-()-: Listen. I didn't go rooting around in your private stuff. | birthday girl | 2001 |
| 4135.0 | I'm sorry. :-()-: I don't know why I said that. She's not dead at all. | birthday girl | 2001 |
| 4128.0 | What? :-()-: You... :-()-: I got what I paid for. :-()-: You didn't mind too much. | birthday girl | 2001 |
| 4132.0 | Fifteen. You don't know him. He was very kind and strong. :-()-: Yeah. He's a smashing bloke. :-()-: The rest of the world, John, it's not all like St. Albans. :-()-: Thank Christ for that. :-()-: You are pretty naive if you think it is. :-()-: I'm pretty naive? Look at you. You have to do all this, and what have you got to show for it? Nothing. :-()-: I don't have nothing. :-()-: Well what have you got? | birthday girl | 2001 |
| 4080.0 | Sorry. You've lost me... :-()-: I'm asking what you're here for. :-()-: What? | birthday girl | 2001 |
| 4069.0 | It's enough isn't it? :-()-: What do you mean? :-()-: You know what I mean babe, It's enough. We can stop. :-()-: Do you want to stop? :-()-: Yes. :-()-: We'll stop then. | birthday girl | 2001 |
| 4137.0 | She's alive!! She is not dead? :-()-: Laugh it up. | birthday girl | 2001 |
| 4078.0 | Hello. :-()-: I thought you could give us the tour this morning. Sort of be our Indian Guide. :-()-: Right. | birthday girl | 2001 |
| 4144.0 | Something else. :-()-: Okay. Promise? | birthday girl | 2001 |
| 4118.0 | Where's the restroom? :-()-: What? :-()-: I'm going to be sick. Where's the... :-()-: What? No you're not.. :-()-: I'm going... I am... I'm going to be sick. :-()-: No you're not. How... Nice one. How dumb do you think I am ? | birthday girl | 2001 |
| 4086.0 | So? :-()-: So I come to England with other actors to make shows, I meet this freak from Novgorod I tell him of you and Chicken and the birthday here we are. | birthday girl | 2001 |
| 4141.0 | I've got an hour. Can I buy you a coffee? :-()-: No. I think I better just go. :-()-: Okay. Thank you. :-()-: Whatever. | birthday girl | 2001 |
| 4079.0 | How's that? We can't drink our piss can we? :-()-: Hang on hang on, sorry, but like, who are you? :-()-: You must find some glasses, small, for the toast, and some plates. :-()-: What are you doing here? | birthday girl | 2001 |
| 4087.0 | What was that? :-()-: I asked her if you were happy to see us. I find it hard to tell with you. :-()-: Yes it's okay. Thank you for the food. | birthday girl | 2001 |
| 4107.0 | Do uh... Sorry. Can you follow me? Do you understand what I'm saying? :-()-: Yes. :-()-: Good. Or should I speak slower? :-()-: Yes. :-()-: Do you follow or should I speak slower? :-()-: Yes. | birthday girl | 2001 |
| 4092.0 | Binoculars. :-()-: Binoculars. He had these Binoculars he has kept from the war. | birthday girl | 2001 |
| 4138.0 | You should stop smoking. You're pregnant. You smoke like a fucking lab dog. :-()-: I'm trying to quit. :-()-: I've got news for you. It's not working. :-()-: I smoke more these days. I smoke more when I'm unhappy. :-()-: Nobody's that unhappy. :-()-: Maybe I want to die. Don't you want me to die? :-()-: I don't want anyone to die. :-()-: Except for Small Eyes. :-()-: Except for Small Eyes. | birthday girl | 2001 |
| 4115.0 | Unless. Unless this is part of the routine. You get tied up, stick around, distract me, they both bust in and Steal My Cup Of Coffee. :-()-: It's makes it easier. Okay. :-()-: I don't want to know. :-()-: It makes it faster. If I don't speak to the men, they fall faster. It's pretty obvious why. :-()-: That's a relief. It's nice to know I'm a regular guy. | birthday girl | 2001 |
| 4090.0 | She says she has a secret to tell. :-()-: What? | birthday girl | 2001 |
| 4127.0 | So what? Do you just switch off in your head or do you imagine you're with him, or what? :-()-: Sometimes. :-()-: Sometimes which? :-()-: Sometimes neither. :-()-: Some... What does that mean? :-()-: There's nothing wrong in liking sex, John. :-()-: I don't like sex. I don't think I'll be having sex ever again. :-()-: Why? :-()-: Well, it's just that the thought trying to charm up an erection in front of a woman, or alone for that matter, makes me want to die. :-()-: So now you hate all women? :-()-: I think it's my safest bet, don't you? :-()-: Oh. I think you will recover okay. I think you got what you paid for. | birthday girl | 2001 |
| 4143.0 | Okay. Goodbye. :-()-: Goodbye. | birthday girl | 2001 |
| 4121.0 | What? :-()-: I said I don't have any. | birthday girl | 2001 |
| 4130.0 | Excuse me? :-()-: Get out :-()-: You are throwing me out. :-()-: Get out. | birthday girl | 2001 |
| 4133.0 | Do you know if it's a boy or a girl? :-()-: No. :-()-: Have you had any before? :-()-: No. :-()-: Are you scared? :-()-: Not really. Maybe a little. | birthday girl | 2001 |
| 4102.0 | Just leave her alone. :-()-: I'm so sorry. :-()-: Leave her alone. | birthday girl | 2001 |
| 4147.0 | Why? :-()-: I'm not asking you to marry me. :-()-: No. What? No. I know. :-()-: It's more like a date. :-()-: It's a long way to go for a date. :-()-: Tell me about it. | birthday girl | 2001 |
| 4072.0 | Say thank you. :-()-: Thank you. | birthday girl | 2001 |
| 4120.0 | Give me some money. :-()-: I don't have any money. | birthday girl | 2001 |
| 4110.0 | I don't understand. :-()-: Happy bath day. | birthday girl | 2001 |
| 4101.0 | Oh Jesus. :-()-: He is sure you can do this. Of course you can not. :-()-: Oh Jesus. Of course I can't. | birthday girl | 2001 |
| 4071.0 | Do you like it? :-()-: Yeah. | birthday girl | 2001 |
| 4136.0 | What? :-()-: I don't know why I said it. I'm sorry. :-()-: She's alive? | birthday girl | 2001 |
| 4112.0 | "Frenzy". :-()-: Yes I know. | birthday girl | 2001 |
| 4151.0 | Is the flight full? :-()-: I'm sorry Sir. I believe the flight is closed. :-()-: Please check. Is it full? Please could you check. | birthday girl | 2001 |
| 4145.0 | You can probably buy them on the flight. :-()-: I'm quitting. This will be my last one. So. Goodbye. :-()-: Goodbye. :-()-: You didn't deserve me John Buckingham. :-()-: Whatever. :-()-: I'm sorry. :-()-: Please. | birthday girl | 2001 |
| 4068.0 | Sixty four thousand, eight hundred. :-()-: There's over eighty thousand here. | birthday girl | 2001 |
| 4088.0 | So how long will you be in England? :-()-: Plans are for the architects, politicians and so forth. :-()-: You must have a visa or something... :-()-: You're asking for my documents? :-()-: No, no... | birthday girl | 2001 |
| 4119.0 | You don't have to do this. I can look after myself. :-()-: Have you got your passport? :-()-: What? :-()-: Shut up. Have you got your passport? :-()-: Yes. | birthday girl | 2001 |
| 4114.0 | Oh, I don't know. In my job as Deputy Assistant of New Business at the bank would have to listen to the problems of a great many individuals. This took a lot of understanding and sympathy, to try to work out solutions to their problems. But, you see, I'm not in that line of work anymore. Nowadays I'm a bank robber. :-()-: You don't understand anything. :-()-: I think that about covers it. I think I have grasped the part about you being dumped though. That's got to hurt, I imagine. That's got to smart a bit. I mean strictly in my observer's capacity it seemed you two were getting on Pretty Fucking Famously. | birthday girl | 2001 |
| 4073.0 | What? :-()-: You heard what I said. I'm pregnant. I've been throwing up for weeks. | birthday girl | 2001 |
| 4064.0 | You first. :-()-: Fifty thousand. Almost exactly. | birthday girl | 2001 |
| 4089.0 | She says 'Hello' to you. Go for it John! :-()-: Uh. Do you like England? :-()-: Classic! Thank God. She says 'Yes!' | birthday girl | 2001 |
| 4100.0 | He wants a lot of money. :-()-: I'll give him money. Tell him to put the... :-()-: He wants the money from your bank. :-()-: I'll fuckin' give it to him! We'll go down there. :-()-: You don't understand. He wants all the money that is in your bank. :-()-: I've got eight hundred pounds. Oh Jesus. | birthday girl | 2001 |
| 4066.0 | When we went to the Hard Rock Cafe. Who paid? When we went to see 'Cats'. Who paid? :-()-: Those aren't presents. That's normal friendship stuff :-()-: I paid for those guitar cases. | birthday girl | 2001 |
| 58970.0 | Richard, shut it and keep it down. :-()-: If he hadn't pissed his pants, we woulda won. We fucking had this game. | magnolia | 1999 |
| 59078.0 | Linda -- :-()-: What are you doing? :-()-: I've got Frank...Frank Earl's son. He's...he asked me to get him and I did -- :-()-: Hang up the phone. :-()-: No, Linda, you don't understan -- :-()-: PUT THE FUCKIN' PHONE DOWN, HANG IT UP. | magnolia | 1999 |
| 59062.0 | ...Jimmy... :-()-: I really don't know. :-()-: But you can't say.... :-()-: <u>I don't know what I've done</u>. :-()-: Yes you do....you do and you won't say. :-()-: ...I don't know... | magnolia | 1999 |
| 58926.0 | Why don't they have the same last name? They don't have the same last name. :-()-: I know -- and I can't really explain that, but I have a feeling there's something, some situation between them, like they don't really know each other much or well, something like they don't talk much anymore -- :-()-: Uh-huh. :-()-: Does this sound weird? :-()-: Well I'm not sure why you're calling me. :-()-: There's no number for Frank in any of Earl's stuff and he's pretty out of it -- I mean, like I said, he's dying, y'know. Dying of Cancer. :-()-: What kind of Cancer? :-()-: Brain and Lung. :-()-: My mother had breast cancer. :-()-: It's rough. I'm sorry, did she make it? :-()-: Oh, she's fine. :-()-: Oh that's good. :-()-: It was scary though. :-()-: It's a helluva disease. :-()-: Sure is. So why call me? | magnolia | 1999 |
| 58937.0 | ...yeah... :-()-: I'm sorry, I had to get dressed. | magnolia | 1999 |
| 58993.0 | I love you. I love you and I'm sick. I'II talk to you....I'll talk to you tommorrow. I'm getting corrective oral surgery tomorrow. For my teeth. For my teeth and for you....for you so we can speak. You have braces. Me too. Me too. I'm getting braces, too. For you. For you, dear Brad. And I don't have any money. And I don't have any money now but I'II get it...I will for you, Brad. I love you, Brad. Brad the Bartender. You wanna love me back? Love me back and I'll be good to you. I'II be god damn good for you. And I won't be mad if you don't know who said what. I won't punish you if you get the answer wrong. I can teach and tell you: Samuel Johnson. :-()-: Brad, honey, you have a special secret crush over here I think, don't take him too lovely -- he might get hurt -- :-()-: You mind your own bussines. :-()-: Gently, son -- :-()-: Brad, I know you don't love me now -- :-()-: "It's a dangerous thing to confuse chidlren with angels..." :-()-: -- and you wanna know the common element for the entire group, like he asks...I'll tell you the answer: I'll tell you, 'cause I had that question. I had that same question....Carbon. In pencil led, it's in the form of graphite and in coal, it's all mixed up with other impurities and in the diamond it's in hard form. "Well...all we were asking was the common element, Donnie...but thank you for all that unnecessary knowledge...ahhh, Kids! Full of useless thoughts, eh?" Thank you. Thank you. And the book says: "We may be through with the past but the past is not through with us." And NO IT'S NOT DANGEROUS TO DO THAT. | magnolia | 1999 |
| 58921.0 | You okay? huh? Jimmy? :-()-: And the book says: "We may by through with the past, but the past ain't through with us." :-()-: C'mon, Jimmy, snap up, snap up -- :-()-: In my sleep, Burt. | magnolia | 1999 |
| 59046.0 | Willa Cather. :-()-: For 25. Best known for the "tragedy and blood" genre, this author-playwright -- | magnolia | 1999 |
| 59048.0 | What were you saying, Stanley? :-()-: I was saying...thinking maybe I'd get my own quiz show someday, Jimmy. Just like you! | magnolia | 1999 |
| 58919.0 | You smell like trouble -- :-()-: I'm fuckin' hammered, Burt. :-()-: You ok? :-()-: ooohhhhhh no. | magnolia | 1999 |
| 58959.0 | No, what's wrong? :-()-: You mind if I come in, check things? | magnolia | 1999 |
| 59064.0 | Hello? :-()-: Hello. Is Claudia here? :-()-: She's asleep. :-()-: Are you her boyfriend? :-()-: You're Jimmy Gator, right? :-()-: Yes. What's your name? :-()-: I'm Bob. :-()-: You're her boyfriend? :-()-: No, I'm just a friend. What are you doing here, I mean...you know Claudia? :-()-: I'm her father. | magnolia | 1999 |
| 59068.0 | Do you still have to do homework? :-()-: Not as much as I used to. Ever since we started, I haven't really gone in to school that much because I've been getting more and more auditions -- | magnolia | 1999 |
| 59052.0 | I don't think I want that. :-()-: It'll take the pain away. :-()-: It's not really pain. | magnolia | 1999 |
| 59016.0 | -- that's right, that's right, and what I'M saying, that none of my competitors can say is this: That there is no need for insight or understanding. Things of the past! Gone, Over, Done. Do you realize how fucking miraculous this is? How fucking razor sharp and cutting edge and ahead of it's time this concept is? I'm talking about eliminating <u>insight</u> and <u>understanding</u> as human values. GOD DAMN I'M GOOD. There is no need for INSIGHT. There is no need for UNDERSTANDING. I have found a way to take <u>any</u> subjective human experience -- in other words -- all the terrible shit or all the great shit that you've had happen to you in your life -- and quickly and easily transform it in the unconscious mind through the subtle and cunning use of language. The "listener-patient" settles into a very light, very delicate, conversationally induced state: NOT A TRANCE, mind you, but a STATE. A state that is brand new. The System's state. What did I do? I REALIZED that concept and put it into practical "get my dick hard and fuck it" use. I'm gonna build a state for the seducer and the seducee to live, vote, breath, pay takes and party 'till dawn. I'm gonna teach methods of language that will help anyone get a piece of ass, tit and tail -- :-()-: Let's talk about -- :-()-: I just realized this is for television, isn't it? I can't swear up and down like I just did. :-()-: It's fine. I can bleep it out. :-()-: I warned you -- I get on a roll... :-()-: -- let's talk more about your background -- :-()-: Muffy -- coffee? | magnolia | 1999 |
| 59023.0 | Are you asking me a question? :-()-: Well I guess the question is this: Do you remember Miss Simms? | magnolia | 1999 |
| 58988.0 | Who was it that said: "A man of genius has seldom been ruined but by himself." :-()-: -- Samuel Johnson. | magnolia | 1999 |
| 58917.0 | Your teeth are straight. :-()-: I need corrective oral surgery. I need the braces. :-()-: Don, you got hit by lightning that time in Tahoe, you went on vacation, I don't think braces is a good idea -- :-()-: I can't believe you're gonna do this to me, the situation I'm in, I don't -- Avi: You know what? Being hit by lighting doesn't matter for getting braces, ok? Now Solomon, let me just ask you once: Please. Please. Don't do this. :-()-: How are you paying tor the braces, Donnie? :-()-: I don't know. | magnolia | 1999 |
| 59001.0 | Give me your keys, Don. :-()-: PLEASE DON'T DO THIS! :-()-: GIMME YOUR FUCKIN' KEYS. | magnolia | 1999 |
| 58956.0 | ....now that I've met you.... Would you object to never seeing me again? :-()-: What? :-()-: Just say no. :-()-: I won't say, no, wait, Claudia -- | magnolia | 1999 |
| 59061.0 | Jimmy, did you touch her? :-()-: I don't know. | magnolia | 1999 |
| 59067.0 | I'm sorry. :-()-: It's alright. | magnolia | 1999 |
| 59014.0 | All it takes is one second? :-()-: Just one look, one hesitation, one subtle gesture for me to know -- And Bing-Bam-Boom I'm away on a tangent -- I get so fuckin' amped at these seminars and lemme tell you why: Because I Am What I Believe. I am what I teach, I do as I say, I live by these rules as religiously as I preach them: And you wanna know what? I'm gettin' pussy left, right, up, down, center and sideways. :-()-: I'm gonna start rolling -- :-()-: -- go, go, go. I'm givin' pearls here. And I'II tell you samethin' else: I'm not succeding in the bush because I'm Frank TJ Mackey. If anything, there are women out there that want to <u>destroy</u> me -- it makes it twice as hard for me, I run into some little muffin, knows who I am, knows my schemes and plans -- shit, she's gonna wanna fuck around, prove to her friends, say, "Yaddda-yadda-yadda, I saw that guy, he wasn't anything, didn't get me." So me? I'm runnin' on full throttle the whole fuckin' time. Dodging bullets left and right from terrorist blonde beauties. But I'II tell you this: The battle of the bush is being fought and won by Team Mackey. Can I have a cigarette? :-()-: Ok. So, lemme just ask you a couple questions to start -- | magnolia | 1999 |
| 59073.0 | They look pretty smart, I think. :-()-: No they don't -- | magnolia | 1999 |
| 59024.0 | Hello. Frank. Frank TJ Mackey. :-()-: ...are you Phil...? :-()-: Yeah. I was trying to get in touch with you. We got dissconnected. :-()-: I got your message. That you were trying to get me -- right? :-()-: Yes. I didn't know how to find you. Earl asked me, so I looked through the adress books and there was no number, nothing -- :-()-: Is Linda here? :-()-: She's not here, she went out. I'm sorry. This is all just so, I don't know what, what to do -- your Dad asked me to try and track you down. To get you and I did, I called the number -- Do you wanna come in? :-()-: Yeah let's...maybe just stand. :-()-: These Dogs'll calm down -- you just have to come in -- | magnolia | 1999 |
| 59004.0 | How's today then? :-()-: Fuckin' bullshit is what this is. :-()-: Fuckin' bullshit is right, in'it? | magnolia | 1999 |
| 59034.0 | --- What was that? :-()-: I didn't hear anything. | magnolia | 1999 |
| 59087.0 | What are they gonna do -- beat us? :-()-: Maybe. | magnolia | 1999 |
| 59098.0 | What's the problem, what's the problem here? :-()-: I'm fine. nothing. :-()-: Why didn't you answer those questions? :-()-: I didn't know the answer -- :-()-: Bullshit. Bullshit. You know the answer to every goddamn question and I knew the answer to those questions and I'm not half as smart as you are so What Happened? :-()-: I don't know. | magnolia | 1999 |
| 58947.0 | I'm gonna run to the bathroom real quick. :-()-: Okey-doke. | magnolia | 1999 |
| 59041.0 | Thirty fuckin' years I've been with Rose, don't -- y'know -- with this, and I know what you think -- :-()-: All your other fluzzies? :-()-: Yeah. Yes. :-()-: You're making me feel so dirty and shitty. I feel like a big piece of shit right now. :-()-: Are you gonna tell her what you've done? :-()-: Yes. :-()-: Will you say my name? :-()-: If she asks me any question I want to tell her. I want to tell her everything I've done. :-()-: Well can you do me one favor and don't do that. | magnolia | 1999 |
| 59044.0 | Rose is on the phone and here's the cards for today -- :-()-: Fifteen minutes ago, where were those cards? :-()-: I'm sorry. :-()-: I need you to get me Paula -- :-()-: You want her right now? :-()-: Yes. Now. Find her. She's somewhere in the building -- :-()-: We're on the air in twenty minutes, Jimmy. :-()-: Find her, get her and tell her I want to talk to her, Mary. Fucking hell. | magnolia | 1999 |
| 59082.0 | Cats and Dogs out there, huh? :-()-: mmmhmm. :-()-: Must have alot goin' on for all that stuff you got back there, eh? You could have quite a party all that stuff.... | magnolia | 1999 |
| 59049.0 | We are not on display. I am not a doll. I AM NOT A DOLL...I' M NOT SILLY AND CUTE. I'M SMART SO THAT SHOULDN'T MAKE ME SOMETHING, SOMETHING SO PEOPLE CAN WATCH HOW SILLY IT IS THAT HE'S SMART? I KNOW. I KNOW THINGS. I KNOW. I HAVE TO GO TO THE BATHROOM I HAVE TO GO TO THE BATHROOM AND I HAVE TO GO. :-()-: I'm sorry, Stanley. | magnolia | 1999 |
| 58974.0 | DADDY, FUCK, DADDY, DON'T GET MAD AT ME. DON'T GET MAD AT ME -- JUST GIMME YOUR MONEY. :-()-: I'm not mad, son, I will not be mad at you and it's ok and please put it down and I won't be mad and I won't -- :-()-: DAD. | magnolia | 1999 |
| 59042.0 | Come and tell me it's over and I'll walk away, Jimmy. I've fucked you behind your wife's back for three years, and you've fucked teenage girls behind mine for the same amount of time -- I'll walk away, you need something for your life, for your conscience, but don't put me in the middle -- :-()-: I won't. :-()-: What happend to you? :-()-: I got in trouble at school. | magnolia | 1999 |
| 59070.0 | We're not going out two days before we set the record, it's not gonna happen. :-()-: When they want us done, they'll call in the Harvard S.W.A.T team or some shit. | magnolia | 1999 |
| 59099.0 | I didn't I'm fine, I'm fine. :-()-: Stand up. :-()-: I said I'm fine. | magnolia | 1999 |
| 59037.0 | This is the LAPD. If anyone is back here I want you to come out and I want you to show yourself to me with your hands in the air -- :-()-: THERE'S NO ONE IN THERE. STAY OUT OF MY MOTHERFUCKIN BEDROOM. | magnolia | 1999 |
| 58962.0 | Diet Coke. :-()-: I want a shot of tequila too. :-()-: -- what kind? :-()-: It doesn't matter. | magnolia | 1999 |
| 59055.0 | ...No...I don't hate you. Do you want talk...do you really want to talk to me and say things and get things figured out, Jimmy? :-()-: Yeah. :-()-: The question isn't wether or not you cheated on me, the question is how many times have you cheated on me? :-()-: Will that help? :-()-: Yeah. | magnolia | 1999 |
| 59071.0 | Do you have an agent, Stanley? :-()-: No. :-()-: You should get one, I'm serious, you could get a lot of stuff out of this -- :-()-: Like what? | magnolia | 1999 |
| 58961.0 | Hello. You're back again, huh? :-()-: yeah, yes, hi, hello. :-()-: -- can I get you? :-()-: Diet Coke. | magnolia | 1999 |
| 59095.0 | C'mon, man. :-()-: You're late, not me. :-()-: You coulda been in front -- :-()-: -- I didn't see you from the window. | magnolia | 1999 |
| 58963.0 | I'm fine. Yes. I'm fine. :-()-: Ready to go,go,go? :-()-: Where's Richard and Julia? :-()-: They're here, they're fine. In the dressing room. See you later -- | magnolia | 1999 |
| 59026.0 | How long have you taken care of him? :-()-: For six months. I'm the day nurse... :-()-: Uh-huh. What's going on? :-()-: He's...I'm sorry...so sorry...I've seen this before, you know and you don't.... He's going very fast....Frank...um.... :-()-: Is he in pain? :-()-: I just...he was...but I gave him, I just had to give him a small dose of liquid morphine. He hasn't been able to swallow the morphine pills so we now, I just had to go to the liquid morphine... For the pain, you understand? :-()-: ...uh-huh... | magnolia | 1999 |
| 59029.0 | So....Phil....um...I think I'm gonna step in and try and see him and say something if he can...talk...I mean: :-()-: ...ok... :-()-: Can you stand...back...maybe, I mean... just a little bit...in the room is ok, but back from us a little... :-()-: yeah. | magnolia | 1999 |
| 58990.0 | Things go round 'n round, don't they? :-()-: Yes they do, they do, but I'll make my dreams come true, you see? I will. :-()-: This sounds Sad as a Weeping Willow. :-()-: I used to be smart but now I'm just stupid. :-()-: Shall we drink to that? | magnolia | 1999 |
| 58941.0 | No. :-()-: I got a call of a disturbance, screaming and yelling, loud music. Has there been some screaming and yelling? :-()-: Yes. I had someone come to my door, someone I didn't want here and I told them to leave -- so -- it's no big deal. They left. I'm sorry. :-()-: Was it a boyfriend of yours? :-()-: No. :-()-: You don't have a boyfriend? :-()-: No. :-()-: Who was it? :-()-: I was...he's gone...I mean it's not. It's over, y'know -- | magnolia | 1999 |
| 58950.0 | This is my job. :-()-: We were just gettin' warmed up. We were just getting started. :-()-: Well if you listen' to that music too loud again and that fella returns maybe we'll share another cup of coffee -- :-()-: If you're not here for a 422 -- :-()-: No. No. Don't joke about that. That's not funny, Claudia. Please, now. :-()-: I'm sorry. :-()-: Ok, then. Keep your chin up and your music down, alright? :-()-: Yes. I will. It was nice to meet you Officer Jim. :-()-: Just Jim. :-()-: yeah, good, ok. :-()-: Bye, bye, Claudia. :-()-: Good bye. | magnolia | 1999 |
| 59005.0 | ...no...no goddamn use. I have a son, y'know? :-()-: You do? :-()-: ...ah... :-()-: Where is he? :-()-: I don't know...I mean, he's around, he's here, in town, y'know, but I don't know...he's a tough one...very.... Do you have a girlfriend, Phil? :-()-: No. :-()-: Get a girlfriend. :-()-: I'm trying. :-()-: And do good things with her...share the thing...all that bullshit is true, y'know...find someone and hold on all that...Where's Linda? :-()-: She went out. She said she went out to run some errands. She'll be back. :-()-: She's a good girl. She's a little nuts, but she's a good girl I think. She's a little daffy. :-()-: She loves you. :-()-: ...ah...maybe...yeah...she's a good one... :-()-: When was the last time you talked to your son? :-()-: ....I dunn...o....maybe ten...five, fuck, fuck....that's another thing that goes -- :-()-: -- memory? :-()-: Time lines, y'know? I remember things but not so -- right there -- y'know? :-()-: Yeah. :-()-: "yeah." the fuck do you know? :-()-: I've seen it before. :-()-: Other fuckin' assholes like me. :-()-: There's no asshole like you. :-()-: ...cocksucker.... :-()-: How come every word you say is either "cocksucker," or "shitballs," or "fuck?" :-()-: Do me a personal favor -- :-()-: Go fuck myself? :-()-: You got it. | magnolia | 1999 |
| 59060.0 | She thinks terrible things that somehow got in her head...that I might have done something. She said that to me last time...when it was...ten years ago she walked out the door, "You touched me wrong..." "I know that." Some crazy thought in her, in her head... :-()-: Did you ever touch her? :-()-: ...No.... | magnolia | 1999 |
| 59063.0 | You deserve to die alone for what you've done. :-()-: I don't know what I've done. :-()-: Yes you do. :-()-: Stay here, please don't leave me, please, please, if I said I knew would you stay? :-()-: No. :-()-: I don't know what I've done. :-()-: You should know better. | magnolia | 1999 |
| 58920.0 | It's been the same fuckin' thing for thirty years, Burt -- :-()-: These adults are tough enough, I think you'll be surprised -- the Mexican's a bit of a question mark -- | magnolia | 1999 |
| 59022.0 | And you went to Van Nuys High, right? :-()-: I don't how much I went -- but I was enrolled. I was such a loser back then. I was -- misguided, pathetic -- I was very fat. Not even close to what I am today. Not the Frank TJ Mackey you're eager to talk to because I was swimming in what <u>was</u> as opposed to I <u>wanted</u>. :-()-: Where does that name come from? :-()-: What name? My name? :-()-: It's not your given name, right? :-()-: My mother's name, actually. Good question. You've done you're research. :-()-: And "Frank?" :-()-: "Frank" was my mother's father. :-()-: Ok. That's why. I had trouble locating your school records at Berkely and UCLA. Your name change -- they had no official enrollment -- :-()-: Oh, yeah. No, no, no. They wouldn't -- :-()-: They wouldn't? :-()-: no, no, no. Certainly not. I wasn't officialy enrolled, that's right. Was that unclear? :-()-: Kind of. :-()-: I wouldn't want that to be misunderstood: My enrollment was totally unoffical because I was, sadly, unable to afford tuition up there. But there were three wonderful men who were kind enough to let me sit in on their classes, and they're names are: Macready, Horn and Langtree among others. I was completely independent financially, and like I said: One Sad Sack A Shit. So what we're looking at here is a true rags to riches story and I think that's what most people respond to in "Seduce," And At The End Of The Day? Hey -- it may not even be about picking up chicks and sticking your cock in it -- it's about finding What You Can Be In This World. Defining It. Controling It and saying: I will take what is mine. You just happen to get a blow job out of it, then hey-what-the-fuck- why-not? he.he.he. | magnolia | 1999 |
| 58964.0 | Where's the news department at this studio? :-()-: It's upstairs. :-()-: Have you ever been there? :-()-: Sure, why? :-()-: I'm wondering about the weather department. I'm wonderin' wether or not the weather people use outside meteorlogical services or if they have in-house instruments? :-()-: I can check on that for you, maybe we can take a tour -- :-()-: Ok. | magnolia | 1999 |
| 58957.0 | Let me go, leave me, let me go, it's ok, please. :-()-: please, what is it, please -- :-()-: just let me walk out, ok? | magnolia | 1999 |
| 59009.0 | ...find him on the...Frank. His name's Frank Mackey -- :-()-: Frank Mackey. That's your son? :-()-: that'snotmy name...find Lily, gimmme that, give it -- | magnolia | 1999 |
| 58940.0 | Wilson. :-()-: Ok. Claudia Wilson: You tryin' to go deaf? :-()-: What? :-()-: Did you hear what I said? :-()-: Yeah, but I don't know -- :-()-: -- listenin' to that music so loud: You Tryin' To Damage Your Ears? :-()-: No. :-()-: Well if you keep listenin' to the music that loud you're not only gonna damage your ears but your neighbors ears. :-()-: I didn't realize it was that loud. :-()-: And that could be the sign of a damaged ear drum, you understand? :-()-: Yeah. :-()-: You got the TV on too, keep those on at that same time usually? | magnolia | 1999 |
| 59036.0 | WHAT'S THIS? WHAT'S THIS? GOD DAMN BULLSHIT. BULLSHIT. DON'T PUT THOSE -- :-()-: Marcie - CALM DOWN. CALM DOWN and don't do this. I want you to stay -- | magnolia | 1999 |
| 59030.0 | I'm here. I'm here now. What do you want? Do you want anything? :-()-: I don't think, he can't... :-()-: ...just wait...Dad...you want something...can you say... | magnolia | 1999 |
| 59031.0 | What? What? What now? :-()-: Quietly, slow down, whoa -- :-()-: You can't just come in here. :-()-: The door was open, I got a call -- :-()-: You're just come in -- :-()-: Calm down. :-()-: I am calm. :-()-: I got a call to this apartment, report of a disturbance -- :-()-: There's no disturbance. :-()-: I got a call of a disturbance, you're door was open, I just wanna see what's goin' on -- :-()-: There's no disturbance. :-()-: Then you've got nothin' to worry about. :-()-: You don't tell me, I know my rights, just come right in, you can't -- :-()-: Don't test me, you wanna talk about what the law book says, we can do that, push me far enough and I'll take you to jail -- now calm down. :-()-: I AM CALM. :-()-: You're not calm. You're screamin' and yellin' and I'm here to check on a disturbance that was reported and that's what I'm gonna do - now are you alone in here? :-()-: I don't have to answer your questions. :-()-: No you don't: But I'm gonna ask you one more time: Are you alone in here? :-()-: What does it look like? :-()-: No one else in here? :-()-: You're here. :-()-: OK. That's true. Is anyone else, besides me and besides you in this house? :-()-: No. I said that. :-()-: Are you lyin' to me? :-()-: I live alone. :-()-: Maybe so, but I'm gonna ask you one more time: Is Anyone Else In This House Right Now? :-()-: No I Said. :-()-: Ok. What's your name? :-()-: Marcie. :-()-: Ok. Marice why don't you take a seat for me? :-()-: I preffer to stand. :-()-: I'm not askin', Marcie. | magnolia | 1999 |
| 59092.0 | Let's go,let's go, let's go, you shoulda done that ten minutes ago -- :-()-: We need more dog food -- :-()-: -- talk in the car, talk in the car, moves your ass, c'mon -- | magnolia | 1999 |
| 59103.0 | Hi. :-()-: Hi. :-()-: ..sorry... :-()-: It's ok. | magnolia | 1999 |
| 58923.0 | Hey, Janet, it's Chad. :-()-: What's wrong? :-()-: Nothing's wrong, I just got some guy on the phone on my other line, he's says he works for this guy, this guy who's Frank's father -- :-()-: -- no,no,no what is this? who? What's this guy's name? | magnolia | 1999 |
| 58989.0 | It was the lovely Samuel Johnson who also spoke of a fella "Who was not only dull but a cause of dullness in others." :-()-: "The" cause of dullness in others -- :-()-: Picky, picky. :-()-: -- and lemme tell you this: Samuel Johnson never had his life shit on and taken from him and his money stolen -- who took his life and his money? His parents? His mommy and daddy? Make him live this life like this -- "A man of genius" gets shit on as a child and that scars and it hurts and have you ever been hit by lighting? It hurts and it doesn't happen to everyone, it's an electrical charge that finds it's way across the universe and lands in your body and your head -- and as for "ruined but by himself," not if his parents take his friggin' life and his money and tell you to do this and do that and if you don't? well, what -- | magnolia | 1999 |
| 59047.0 | I'm gonna need a full name, Stanley. :-()-: Jean Baptiste Poquelin Moliere. | magnolia | 1999 |
| 59006.0 | ...I can't hold onto this anymore... :-()-: I'll get you another pain pill. Another morphine pill -- :-()-: ...gimme that fuckin' phone... :-()-: Who are you gonna call? :-()-: I wanna see this...where is he, do you know? :-()-: Who? :-()-: Jack. :-()-: Is Jack your son? | magnolia | 1999 |
| 58935.0 | uh...uh...What is it? :-()-: It's the LAPD, can you open the door, please? | magnolia | 1999 |
| 58938.0 | Yes. :-()-: No one else in there with you? | magnolia | 1999 |
| 58975.0 | I - just - thought - that - I - didn't want - I - didn't - I - didn't - :-()-: It's ok, boy. | magnolia | 1999 |
| 59008.0 | This is so boring...so goddamn... and dying wish and all that, old man on a bed...fuck...wants one thing: :-()-: It's ok. | magnolia | 1999 |
| 58968.0 | What do you mean, "like what?" -- you could get endorsments and shit -- :-()-: -- Richard. :-()-: Bite it, Cynthia. You could get free things from people that want you to endorse their products. | magnolia | 1999 |
| 59057.0 | That's it. :-()-: No one else that I know? :-()-: No. :-()-: How long with Ellen? :-()-: Just once. :-()-: How long with Paula? :-()-: Two years...three years... :-()-: What about now? :-()-: It's over. I talked to her this morning. :-()-: Is it over 'cause you're sick? :-()-: It's over becuase...for all the the right reasons I hope, what I said. :-()-: Do you have any children with anyone? :-()-: What? No, Rose, jesus, no -- :-()-: Well maybe. :-()-: I don't. :-()-: Do you feel better now that you've said this? :-()-: I don't know.... :-()-: I'm not mad. I am, but I'm not. Y'know? :-()-: I love you so much. :-()-: I'm not through asking my questions. | magnolia | 1999 |
| 58927.0 | Yeah, hey. Chad. :-()-: Alright, so I'm gonna transfer you over to Frank's assitant, Janet she's gonna see what she can do -- :-()-: Thank you, Chad, and good luck to you and your mother -- :-()-: Thank you. Thank you very much. | magnolia | 1999 |
| 58939.0 | For what? :-()-: Ok. For one thing, we're gonna need to turn that music down so we can talk, ok? :-()-: I'm sorry. | magnolia | 1999 |
| 59066.0 | Want me to wake her up? :-()-: I'II go....is it...back here? | magnolia | 1999 |
| 58954.0 | Wow....huh..."...piss and shit..." :-()-: What? :-()-: You really use strong language. :-()-: I'm sorry -- :-()-: -- no, no, it's fine. Fine. :-()-: I didn't mean...it's seems vulgar or something, I know -- :-()-: It's fine. :-()-: I'm sorry. :-()-: ...nothing. I'm sorry... :-()-: No, I'm sorry. I'm saying I'm sorry. I talk like a jerk sometimes -- :-()-: -- well I'm a real...y'know, straight when it comes to that...curse words I just don't use much -- :-()-: I'm sorry. :-()-: I'm gonna run to the bathroom for a minute...maybe just -- :-()-: ok. :-()-: ok. | magnolia | 1999 |
| 59043.0 | Are you ok? :-()-: Fuck no. | magnolia | 1999 |
| 58997.0 | What is that? :-()-: Braces. :-()-: Braces? :-()-: Yes. :-()-: You don't need braces. :-()-: Yes I do. :-()-: Your teeth are fine. | magnolia | 1999 |
| 59002.0 | -- he's fucking dying, he's dying as we're sitting here and there isn't a fucking thing -- jesus, how can you tell me to calm down? :-()-: I can help you through this the best I know how but there are certain things you are gonna have to be strong about and take care of, now we can go over them, but I need to know that you're listening to me, ok? :-()-: I just, I just -- I just -- I'm just in a fucking state, I know he's going and it's like I don't know how -- just tell me <u>practical</u> things -- What the fuck do I do with his body? What happens when he dies? That next moment: What? What do I do? Then What? :-()-: Well that's what Hospice will take care of for you. They will send a nurse, someone who can take care of all of that for you -- :-()-: He has Phil right now. :-()-: Phil's one of the nurses from the service? :-()-: Yeah. :-()-: If you're happy with Phil taking care of him and helping you, that's fine, but contact Hospice to arrange for the body -- :-()-: -- you don't understand: it's more pain than before and the fucking morphine pills aren't working, he's -- past two days it's like he can't really swallow them and I don't know if they're going down -- I can't see inside his mouth anymore -- I'm up all night staring at him and I don't think the pills are going down and he moans and he hurts -- :-()-: We can fix that, because I can give you -- are you listening? :-()-: I'm listening. I'm getting better. :-()-: Do you wanna sit down? :-()-: I need to sit down. :-()-: Ok. Linda: Earl is not gonna make it. He's dying. He is. He is dying very, very rapidly -- | magnolia | 1999 |
| 58971.0 | DADDY! DAD! DAD WHAT THE HELL IS GOIN' ON? :-()-: Stay quiet...stay quiet, son -- :-()-: LET'S GO, LET'S GO, LET'S GET HIS MONEY AND GO -- DID YOU GET HIS MONEY? DID YOU GET IT? DID YOU GET HIS MONEY, DAD? :-()-: No, Son...be quiet...be quiet now... :-()-: C'mon, Dad. We gotta just GET HIS MONEY AND GO, LET'S GO. Let's get the money -- :-()-: We're not gonna do that now. We're not gonna do that now and that's over. :-()-: BULLSHIT. BULLSHIT, DAD WE NEED TO GET HIS MONEY AND GO. | magnolia | 1999 |
| 59080.0 | listen...listen to me now, Phil: I'm sorry, sorry I slapped your face. ...because I don't know what I'm doing... ...I don't know how to do this, y'know? You understand? y'know? I...I'm...I do things and I fuck up and I fucked up....forgive me, ok? Can you just... :-()-: ....it's alright.... :-()-: Tell him I'm sorry, ok, yes, you do that, now, I'm sorry, tell him, for all the things I've done...I fucked up and I'm sorry.... And I'm Gonna Turn Away And Walk Now And Not Look At Him Not See My Man, My Earl, I'll leave now...and tell him it's ok and I'm ok. The whole thing was ok with me -- and I know. | magnolia | 1999 |
| 59017.0 | I'm confused about your past is the thing. :-()-: Is that still lingering? :-()-: -- just to clarify -- :-()-: So boring, so useless -- :-()-: I would just want to clear some things up: :-()-: Thank you, Muffy. Funny thing is: This is an important element of, "Seduce and Destory:" "Facing the past is an important way in not making progress," that's something I tell my men over and over -- :-()-: This isn't meant -- :-()-: -- and I try and teach the students to ask: What is it in aid of? :-()-: Are you asking me that? :-()-: Yes. :-()-: Well, just trying to figure out who you are, and how you might have become -- :-()-: In aid of what? :-()-: I'm saying, Frank, in trying to figure out who you are -- :-()-: -- there's a lot more important things I'd like to put myself <u>into</u> -- :-()-: It's all important -- :-()-: Not really. :-()-: It's not like I'm trying to attack you -- :-()-: This is how you wanna spend the time, then go, go, go -- you're gonna be surprised at what a waste it is -- "The most useless thing in the world is that which is behind me," Chapter Three -- :-()-: We talked earlier about your mother. And we talked about your father and his death. And I don't want to be challenging or defeatist here, but I have to ask and I would want to clarify something -- something that I understand -- :-()-: I'm not sure I hear a question in there? :-()-: Do you remember a Miss Simms? :-()-: I know alotta women and I'm sure she remembers me. :-()-: She does. From when you were a boy. :-()-: Mm. Hm. :-()-: She lived in Tarzana. :-()-: An old stomping ground --is this the "attack" portion of the interview, I figured this was coming sooner or later -- Is "the girl" coming in for the kill? :-()-: No, this is about getting something right and claryfying one of your answers to an earlier question -- :-()-: Go ahead and waste your time. :-()-: I was told that your mother died. That your mother died when you were young -- :-()-: And that's what you've heard? :-()-: I talked to Miss Simms. Miss Simms was your caretaker and neighbor after your mother died in 1980. | magnolia | 1999 |
| 59045.0 | I can't fuckin' do this. :-()-: Are you alright? :-()-: Fuck. I think I'm gonna throw up, I think. I haven't thrown up since I was twenty years old. | magnolia | 1999 |
| 58916.0 | What surery? :-()-: Oral surgery. Corrective teeth surgery. | magnolia | 1999 |
| 58985.0 | Just throw some money around. Money, money, money. :-()-: This sounds threatening. :-()-: Do you have love in your heart? :-()-: I have love all over. I even have love for you, friend. :-()-: Is it real love? :-()-: Well -- :-()-: -- the kind of love that makes you feel that intagible joy. Pit of your stomach. Like a bucket of acid and nerves running around and making you hurt and happy and all over you're head over heels....? :-()-: Well you lost me with the last couple of cocktail words spoken, m'boy, but I believe it's that sort of love. Sounds nice to me. :-()-: I have love. :-()-: A very chatty-kind, you do, indeed, it seems. :-()-: No. I mean, I'm telling you: I'm telling you that I have love. :-()-: And I'm listening avidly, fellow. :-()-: My name is Donnie Smith and I have lot's of love to give. | magnolia | 1999 |
| 59000.0 | I don't know. :-()-: You were gonna ask me weren't you? :-()-: I've been a good worker, Solomon. A hard and loyal -- | magnolia | 1999 |
| 59039.0 | What the hell is this Marcie? :-()-: THAT'S NOT MINE. | magnolia | 1999 |
| 58977.0 | Ok. Listen. You: c'mere. :-()-: No. :-()-: You wanna disrespect an officer of the law? :-()-: I can help you solve the case, I can tell you who did it. :-()-: Are you a joker? huh? Tellin' jokes? :-()-: I'm a rapper. :-()-: Oh, you're a rapper, huh? You got a record contract? :-()-: Not yet -- "give you the clue for the bust if you show me some trust --" :-()-: Have you ever been to Juvenille Hall? :-()-: I ain't fuckin with you -- :-()-: Hey. Watch the mouth. Watch it. :-()-: C'mon, man, just watch me, watch and listen -- :-()-: Go. Hurry up. Let's go. | magnolia | 1999 |
| 59065.0 | Can I come in? :-()-: Yeah. She's sleeping now, I mean -- | magnolia | 1999 |
| 58922.0 | Call 911. Call 911 right now. :-()-: No, no, no. I'm fine. It's small, I wanna keep going -- :-()-: no, no, c'mon Jimmy we need to call this quits and you need to see a doctor. :-()-: I'm telling you right now, I'm fine. I lost my goddamn balance and I couldn't see a moment, but I'm ok. :-()-: Call 911, Mary, do it right now. :-()-: You fuckin' don't do that. You don't do it, you cocksucker. I'll fuckin' kill you with my barehands. Go. get the fuck fuck -- we're going back and we finish the show -- :-()-: Jimmy you look like you're about to fuckin' die right here -- :-()-: Shut it. Shut yer fuckin' mouth. | magnolia | 1999 |
| 58994.0 | ...please... :-()-: Don't Donnie. Don't do it. | magnolia | 1999 |
| 59105.0 | ....you have it...easy....you know? You have a father who loves you, huh? :-()-: Yes. :-()-: You know what it's like to come home scared, scared that maybe if you don't have the money you're supposed to go out each day and get that you're gonna get beaten....by a belt...he hits me with a belt, Stanley.... I'm supposed to sell those candy bars, and if I don't, I come home without the money.... :-()-: ....Why does he do it...? :-()-: Cause he hates me....he hates me so much. :-()-: It's not right. :-()-: I hate it. | magnolia | 1999 |
| 59032.0 | I didn't do anything. :-()-: Maybe you didn't, but I'm here to find out about a disturbance. Some neighbors called said they heard screaming and a loud crash. :-()-: I don't know a loud crash. :-()-: And what about screaming? :-()-: I said: I DON'T KNOW. You can't just come in here and start pokin' around -- :-()-: What's this, how did this happen? | magnolia | 1999 |
| 59106.0 | I'm sorry to put all this on you, Stanley -- :-()-: I have money. :-()-: ...what...? :-()-: I have money to give you. :-()-: No. No. I have to do this on my own. :-()-: I can take you to get money. I don't need it...I don't <u>need</u> it -- listen to me: I can let you have money so your father won't hit you ever again -- you'll have the money because I don't need it. | magnolia | 1999 |
| 58951.0 | I'm sorry, Claudia. :-()-: What is it? Did you forget something? :-()-: No, no. I was wondering...man oh man. I think I feel like a bit of a scum-bucket doing this, considering that I came here as an officer of the law and the situation and all this but I think I'd be a fool if I didn't do something I really want to do which is to ask you on a date. :-()-: You wanna go on a date with me? :-()-: Please, yes. :-()-: Well...is that illegal? :-()-: No. :-()-: Then...I'd like to go...What do you want to do? :-()-: I don't know. I haven't thought about it -- you know what -- that's not true -- I have thought about it. I've thought about going on a date with you since you opened the door. :-()-: Really? :-()-: Yeah. :-()-: I thought you were flirting with me a little. | magnolia | 1999 |
| 58987.0 | But you're alright now, so what's the what? :-()-: What? :-()-: That's right. :-()-: I used to be smart but now I'm just stupid. :-()-: Brad, dear? | magnolia | 1999 |
| 59090.0 | I don't wanna go, I can't do it this time. :-()-: -- the fuck are you talking about? | magnolia | 1999 |
| 59011.0 | She had cancer...from her...in her stomach and I didn't go anywhere with her...and I didn't do a god thing... for her and to help her....shit...this bitch...the beautiful, beautiful bitch with perfect skin and child bearing hips and so soft...her namewasLilysee? He liked her though he did, his mom, Frank/Jack...he took care of her and she died. She didn't stick with him and he thinks and he hates me, ok...see...I'm...that's then what you get? ....are you still walkin' in that car...? :-()-: What? Say it again...walking in the car? :-()-: ....getthat on the tv....there... | magnolia | 1999 |
| 58925.0 | Hi, hello, great. This is Seduce and Destroy? :-()-: It is. Can I have your home phone number with area code? :-()-: Well I don't want to order anything, you see. I have a situation, a situation just come up that's really pretty serious and I'm not sure who I should talk to or what I should do but could you maybe put me in touch with the right person if I explain myself? :-()-: I'm really only equipped to take orders -- :-()-: Well can you connect me to someone else? :-()-: Well what's the situation? :-()-: Well, ok. Lemme see how I explain this without it seeming kinda crazy, but here go: I'm, my name is Phil Parma and I work for a man named Earl Partridge -- Mr. Earl Partidge. I'm his nurse. He's a very sick man. He's a dying man and he's sick and he's asked me to help him, to help him find his son -- Hello? Are you there, hello? :-()-: I'm here, I'm listening. :-()-: OK. See: Frank TJ Macky is Earl Partridge's son.... | magnolia | 1999 |
| 58966.0 | I have to go to the bathroom, Cynthia. :-()-: Jesus Christ, Stanley, you can't go to the bathroom now. You have exactly one minute before we're back on the air, this is NOT the time to go to the bathroom. :-()-: I'm, I need to go, I'm gonna -- | magnolia | 1999 |
| 59086.0 | I don't have regular classes anymore. :-()-: What do you do? :-()-: They just let me have my own study-time, my own reading time in the library. :-()-: That's pretty cool. | magnolia | 1999 |
| 58984.0 | You look like you've got money in your pocket. :-()-: Maybe I'm just happy to see my friend, Brad there. | magnolia | 1999 |
| 58995.0 | This is so fucked, Solomon. I don't deserve this. :-()-: Don't get strong, Donnie. This is making sense, this making a lot of sense. You are not doing the job, the job I ask you to do, a job I give you. Over and over and over and I'm sorry. But I'm not gonna say I'm sorry that much more. :-()-: Solomon: I am in the middle of so much. So much in my life and this is -- If you do this, if you fire me: I Am Fucked. I can't really explain much, but please, please, I've worked here for four years, four years I've given you and I'm, I'm, I mean what? I'm sorry I was late. I had a car accident. I accidentaly drove into a seven-eleven. It was not my fault. | magnolia | 1999 |
| 59072.0 | Commercials, a sitcom, an MOW or something. :-()-: What's MOW? :-()-: Movie Of The Week. I went up for one this morning with Alan Thicke and Corey Haim -- | magnolia | 1999 |
| 59013.0 | That's fine. It's nice to meet you. :-()-: Are we gonna tape some stuff now? :-()-: If you're up to it, I've got us set up in a suite upstairs -- :-()-: You got us a room so quick? | magnolia | 1999 |
| 59076.0 | I don't want him to die, I didn't love him when we met, and I've done so many bad things to him that he doesn't know, things I want to confess to him, but now I do: I love him. I love him so much and I can't stand -- he's going. :-()-: What kind of medication are you on right now, Linda that's -- :-()-: This is not any fucking medication talking, this isn't -- I don't know. I don't know -- Can you give me nothing? You have power of attorney, can you see him, can you, in this final fucking moment, go see him and make sure --- change the fucking will -- I don't want any money, I couldn't live with myself, this thing I've done -- I've fucking done so many bad things -- I fucked around. I fucked around on him, I fucking cheated on him, Alan. You're his lawyer, our laywer, THERE, I'm his wife, we are married. I broke the conract of marriage, I fucking cheated on him, many times over, I sucked other men's cocks and fuck - fuck - fuck - ....fuck.... Other Things I've Done.. :-()-: Adultery isn't illegal -- it's not something that can be used in a court to discredit the will or -- Linda. Linda. Calm down. :-()-: I can't. :-()-: You don't have to change the will, if what you want to do is get nothing you can renounce the will when it's time. :-()-: Where will the money go? :-()-: Well. Considering that there's no one else mentioned in the will...we'd have to go to the laws of intestacy, which is -- as if someone died without a will -- :-()-: What does that mean? :-()-: The money would go to Frank. The court would put the money in the hands of a relative -- :-()-: -- that can't happen. Earl doesn't want him to have the money, the things. :-()-: -- unless Frank is specifically ommitted as a beneficiary that's what will happen. :-()-: This is so over-the-top and fucked-up I can hardly stand it. :-()-: Linda, you just have to take a moment and breath and one thing at a time -- :-()-: Shut the fuck up. :-()-: I'm trying to help, Linda -- :-()-: Shut the fuck up. Shut the fuck up. :-()-: You need to sober up. :-()-: Now you must really shut the fuck up, please. Shut The Fuck Up. :-()-: Linda -- :-()-: I have to go. | magnolia | 1999 |
| 59040.0 | You look great. :-()-: What the fuck is this, Jimmy? :-()-: ...you know... :-()-: Did your wife find out? :-()-: No. :-()-: Then what? :-()-: It's just...too late for me to be fuckin' around. I gotta stop. I gotta clean my brain of all the shit I've done that I shouldn't have done -- :-()-: -- that you shouldn't have done? That you regret, what? This? What's this? Fuck, man, c'mon. Treat me like an asshole, but treat me like an <u>asshole</u>. :-()-: I don't wanna have to lie to anyone. I don't want to hurt anyone else, anymore. | magnolia | 1999 |
| 58928.0 | ...what the fuck is this...? :-()-: It's me. Claudia. It's me. | magnolia | 1999 |
| 59025.0 | He's in here. :-()-: Let's just wait one minute and stay here, okay? :-()-: Ok. | magnolia | 1999 |
| 58982.0 | What happend? :-()-: Doc, just - don't, how close are you? :-()-: I'm about to get off the elevator -- | magnolia | 1999 |
| 59093.0 | Cmon,cmon,cmon, that one to? :-()-: I need this one. :-()-: Why the hell do you need all four bags of books to go to school each day? :-()-: I can't carry all of them. I need them. I need my books. I need them to go to school. | magnolia | 1999 |
| 59019.0 | Time's up. Thank you for the interview. :-()-: So you sat it out, that's what you did? :-()-: You requested my time and I gave it you, you called me a liar and made accusations. And you say, "If I'd known I wouldn't have asked," then it's not an attack? Well, I don't wanna be the sort of fella who doesn't keep his word. I gave you my time, Bitch. So fuck you now. | magnolia | 1999 |
| 59051.0 | How you doing? :-()-: I'm drinking. :-()-: Slowly or quickly? :-()-: As fast as I can. :-()-: Come home soon after the show. :-()-: I went to see her -- some fuckin' asshole answers the door in his underwear, he's fifty years old, there's coke and shit laid out on the table -- :-()-: -- did she talk to you? :-()-: She went crazy. She went crazy, Rose. :-()-: Did you tell her? :-()-: I don't know. I have to go, I don't have time and I have more drinking to do before I go march -- :-()-: I love you. :-()-: Love you too.. :-()-: Bye. | magnolia | 1999 |
| 59091.0 | Stanley if you don't fuckin' stand up and go over there I'm gonna beat your ass -- :-()-: I'm sick of being the one, the one who always has to do everything, I don't want to be the one always -- | magnolia | 1999 |
| 58924.0 | Hello? :-()-: Ok. Janet you have Phil Parma -- :-()-: Hello, Phil. | magnolia | 1999 |
| 59035.0 | No. No. Stay down, Marcie, sit back down on that couch -- :-()-: I don't have to do a god damn thing. | magnolia | 1999 |
| 59010.0 | Do you know Lily? Phil..do you know her? :-()-: No. :-()-: ...Lily...? :-()-: No. :-()-: She's my love...my life...love of it... In school when you're 12 years old. In school, in six grade....and I saw her and I didn't go to that school...but we met. And my friend knew her...I would say, "What's that girl?" "How's that Lily?" "Oh, she's a bad girl...she sleeps with guys..." My friend would say this....but then sometime...I went to another school, you see? But then...when high school at the end, what's that? What is that? When you get to the end? :-()-: Graduation? :-()-: No, no, the grade...the grade that you're in? :-()-: Twelve. :-()-: Yeah...So I go to her school for that for grade twelve...and we meet...she was fuckin...like a doll...porcelain doll...and the hips...child bearing hips...y'know that? So beautiful. But I didn't have sex with anyone, you know? I was not...I couldn't do anything...always scared, y'know... she was...she had some boyfriends...they liked her y'know...but I didn't like that. I couldn't get over that I wasn't a man, but she was a woman. Y'see? Y'see I didn't make her feel ok about that....I would say, "How many men you been with?" She told me, I couldn't take it...take that I wasn't a man....because if I hadn't had sex with women...like as many women as she had men...then I was weak...a boy.... But I loved her...you understand? ....well, of course, I wanted to have sex with her...and I did and we were together....we met...age twelve, but then again...age seventeen...something, somethin... I didn't let her forget that I thought she was a bad...a slut.....a slut I would call her and hit her....I hit her for what she did...but we married...Lily and me and we married...but I cheated on her...over and over and over again...because I wanted to be a man and I couldn't let her be a woman...a smart, free person who was something...my mind then, so fuckin' stupid, so fuckin....jesus christ, what would I think...did I think....? ...for what I've done...She's my wife for thirty eight years...I went behind her... over and over...fucking asshole I am that I would go out and fuck and come home and get in her bed and say "I love you..." This'z Jack's mother. His mother Lily...these two that I had and I lost .... and this is the regret that you make...the regret you make is the something that you take...blah...blah...blah... something, something..... Gimme a cigarettee? | magnolia | 1999 |
| 59007.0 | You wanna call him on the phone? We can call him, I can dial the phone if you can remember the number -- :-()-: -- it's not him. it's not him. He's the fuckin' asshole...Phil..c'mere... | magnolia | 1999 |
| 59094.0 | Be ready at two -- :-()-: Should be one-thirty. :-()-: I got an audition, I won't make it here 'till two, c'mon, I'll see you later. Love you. :-()-: Love you too. | magnolia | 1999 |
| 58992.0 | I confuse melancholy and depression sometimes.... :-()-: Mmm.Hmm. :-()-: You see? :-()-: Why don't you run along now friend, your dessert is getting cold. :-()-: I'm sick. :-()-: Stay that way. :-()-: I'm sick and I'm in love. :-()-: You seem the sort of person who confuses the two. :-()-: That's right. That's the first time you're right. I CONFUSE THE TWO AND I DON'T CARE. | magnolia | 1999 |
| 58955.0 | Well. :-()-: That felt good to do...to do what I wanted to do. :-()-: Yeah. :-()-: Can I tell you something? :-()-: Yeah, of course. :-()-: I'm really nervous that you're gonna hate me soon. That you're gonna find stuff out about me and you're gonna hate me -- :-()-: -- no, like what, what do you mean? :-()-: You're a police officer. You have so much, so many good things and you seem so together...so all straight and put together without problems. :-()-: I lost my gun. :-()-: What? :-()-: I lost my gun after I left you today and I'm the laughing stock of a lot of people. I wanted to tell you that. I wanted you to know...and it's on my mind and it makes me look like a fool and I feel like a fool and you asked that we should say things, that we should say what we're thinknig and not lie about things and I'll tell you that, this: that I lost my gun and I'm not a good cop...and I'm looked down at...and I know that....and I'm scared that once you find that out you might not like me. :-()-: Oh my god, Jim. Jim, that was so -- :-()-: I'm sorry -- :-()-: That was so great what you just said. :-()-: I haven't been on a date since I was married and that was three years ago....and Claudia...whatever you wanna tell me, whatever you think might scare me, won't...and I will listen...I will be a good listener to you if that's what you want...and you know, you know...I won't judge you.... I can do that sometimes, I know, but I won't...I can...listen to you and you shouldn't be scared of scaring me off or anything that you might think I'll think or on and on and just say it and I'll listen to you.... :-()-: You don't how fuckin' stupid I am. :-()-: It's ok. :-()-: You don't know how crazy I am. :-()-: It's ok. :-()-: I've got troubles. :-()-: I'll take everything at face value. I'll be a good listener to you. :-()-: Ohhhh I started this, didn't I, didn't I, didn't I, fuck. :-()-: Say what you want and you'll see -- :-()-: Wanna kiss me, Jim? :-()-: Yes I do. | magnolia | 1999 |
| 58949.0 | --- yeah, yeah, I get in it in my ear. It's TMJ is what it's called technically. :-()-: What's that stand for? :-()-: Tempural-something-mandibular, thing with something, I dunno. But it affects my ear, I don't even know if I have TMJ exactly but just very tight, like - it's like a muscle spasm and it's just gets so clenched -- | magnolia | 1999 |
| 58960.0 | You live alone? :-()-: Yes. :-()-: What's your name? :-()-: Claudia. :-()-: Claudia What? | magnolia | 1999 |
| 59069.0 | Was it a call back? :-()-: No. But I probably will get a call back. :-()-: If we beat the record, you might get a call back -- :-()-: I'll get it because I'm a good actress, Richard. :-()-: Saucy-saucy. | magnolia | 1999 |
| 58978.0 | Presence - with a double ass meaning gifts I bestow, with my riff, and my flow but you don't hear me though think fast, catch me, yo cause I throw what I know with a Resonance - fo'yo'trouble-ass fiend in weenin yo-self off the back of the shelf Jackass crackas, bodystackas dicktootin niggas, masturbatin' yo trigga butcha y'all just fake-ass niggas -- :-()-: -- watch the mouth, homeboy, I don't need to hear that word -- :-()-: -- livin' to get older with a chip on your shoulder 'cept you think you got a grip, cauze you hip gotta holster? Ain't no confessor, so busta, you best just Shut The fuck up, try to listen and learn -- :-()-: Alright, alright, cut it, coolio. That's enough with the mouth and the language. :-()-: I'm almost done. :-()-: Finish it up without the lip. :-()-: Check that ego - come off it - I'm the profit - the proffesor Ima teach you 'bout The Worm, who eventually turned to catch wreck with the neck of a long time oppressor And he's runnin from the devil, but the debt is always gaining And if he's worth being hurt, he's worth bringin' pain in - When the sunshine don't work, the Good Lord bring the rain in. :-()-: Now that shit will help you SOLVE the case. :-()-: Whatever that meant, I'm sure it's real helpful Ice-T. | magnolia | 1999 |
| 59085.0 | Pink Dot. :-()-: Hi. I'd like to get an order for delivery. :-()-: Phone number. :-()-: 818-753-0088. :-()-: Partridge? :-()-: Yeah. :-()-: What would you like? :-()-: I'd like to get an order of...um...peanut butter. :-()-: Mmm.Hmmm. :-()-: Cigarettes. Camel Lights. :-()-: mmm.hmm. :-()-: Water. :-()-: Bottled Water? :-()-: Um, no, y'know what? Forget the water, just give me a loaf of bread...white bread. :-()-: Ok. :-()-: And um....do you have Swank magazine? :-()-: Yeah. :-()-: Ok. One of those. Do you have Ram Rod? The magazine, Ram Rod? :-()-: Yeah. :-()-: Ok. One of those. And...um...Barely Legal? :-()-: yeah. :-()-: Do you have that? :-()-: yeah, I said. Is that it? :-()-: That's it. :-()-: Do you still want the peanut butter, bread and cigarettes ? :-()-: Yes. What? Yes. :-()-: Total is $15.29. Thirty minutes or less. :-()-: Thank you. | magnolia | 1999 |
| 59079.0 | You don't do that, you don't call him, you don't know to get involved in the bussiness of his, of his of <u>my</u> family. this is the <u>family</u>, <u>me</u> and <u>him</u> do you understand? You understand? NO ONE ELSE. THERE IS NO ONE ELSE. That man, his son does not exist. HE IS DEAD. HE IS DEAD and WHO TOLD YOU TO DO THAT? :-()-: Earl asked me, Linda, please, Linda, I'm sorry -- Earl asked me -- :-()-: BULLSHIT. BULLSHIT HE DIDN'T ASK YOU, HE DOESN'T WANT HIM, HE DOESN'T WANT TO TALK TO HIM, SO FUCK YOU THAT HE ASKED THAT. THERE IS NO ONE BUT ME AND HIM. | magnolia | 1999 |
| 58981.0 | I'm walking towards the elevator's, Janet. :-()-: Fine. Phil, you still there? | magnolia | 1999 |
| 59077.0 | Let me call you a car, Linda. :-()-: Shut the fuck up. | magnolia | 1999 |
| 58967.0 | What happend, what's going on? :-()-: NOTHING. NOTHING HAPPEND. GO AWAY. :-()-: Don't tell me to go away, Stanley. I am the Co-ordinator in this show and you will answer the questions that I ask, you understand? | magnolia | 1999 |
| 59021.0 | Where are you from originally? :-()-: Around here. :-()-: the valley? :-()-: Hollywood, mainly. :-()-: And what did your parents do? :-()-: My father worked in televison. My mother -- this is gonna sound silly to you -- she was a librarian. :-()-: Why does that sound silly? :-()-: Well I guess it doesn't. :-()-: Does you mother still work? :-()-: She's retired. :-()-: Are you close? :-()-: She's my mother. :-()-: What does she say about, "Seduce and Destroy." :-()-: "Go Get 'Em, Honey." :-()-: And your father? :-()-: He passed away. :-()-: I'm sorry. :-()-: people die. :-()-: I wouldn'tve asked -- :-()-: Not a problem. :-()-: And you ended up at UC Berkely -- :-()-: From '84 to '89. :-()-: Psychology major? :-()-: Right. :-()-: Do you have your masters? :-()-: ...this close... :-()-: In five years? | magnolia | 1999 |
| 58932.0 | Please put your clothes on, please -- :-()-: YOU BURN IN BELL. You burn in hell and you deserve it -- YOU GET THE FUCK OUT. :-()-: Honey. :-()-: GET OUT. | magnolia | 1999 |
| 58933.0 | Your mother wants to hear from you -- :-()-: GET THE FUCK OUT OF HERE. | magnolia | 1999 |
| 58934.0 | ...Hello...? :-()-: LAPD. Open the door. | magnolia | 1999 |
| 58986.0 | ....do you know who I am? :-()-: You're a friend of the family I presume? :-()-: What? What does that mean? :-()-: Nothing special, just a spoke in the wheel. :-()-: You talk in rhymes and riddles and ra...rub-adub --- but that doesn't mean anything to me, see....see...see I used to be smart....I'm Quiz Kid Donnie Smith. I'm Quiz Kid Donnie Smith from the tv -- :-()-: Might of been before my time. | magnolia | 1999 |
| 59101.0 | Are we gonna keep going with this game? :-()-: Yes. :-()-: You're two fuckin' days from the record, get through this and I'II do anything for you, you just gotta get through this -- :-()-: Alright. :-()-: hang in there, ok. I love you. | magnolia | 1999 |
| 59059.0 | ...say it, Jimmy... :-()-: Do you know the answer to this? :-()-: I'm asking you. I'm asking you if you know why Clauida will not speak to you....please, Jimmy....tell me. :-()-: I think that she thinks I may have molested her. | magnolia | 1999 |
| 58931.0 | What the fuck do you want? :-()-: I want to sit. I want to talk to you. :-()-: Don't sit down. :-()-: ...I want to....I want so many things, Claudia. Maybe we can just talk to straighten our things out....there are so many things that I want to tell you -- :-()-: I don't wanna talk to you. :-()-: Please. It doesn't have to be now. Maybe we can make a date to sit down, I didn't mean to walk in on you like this -- :-()-: Why are you here, why are you doing this? Coming in here -- you wanna call me a whore? :-()-: I don't want you to think that I'm that way to you -- I'm not gonna call you a slut or something -- :-()-: Yeah, yeah right -- what the fuck are doing? WHAT THE FUCK ARE YOU DOING IN MY HOUSE? :-()-: Don't yell, honey. Please don't go crazy -- :-()-: I'M NOT CRAZY. Don't you tell me I'm crazy. :-()-: I'm not saying that, I'm sorry -- :-()-: I'M NOT CRAZY. You're the one. You're the one who's wrong. You're the one -- :-()-: I have something, so much -- I'm sick, Claudia. I'm sick. :-()-: Get out of here, get the fuck out of my house -- :-()-: Now STOP IT and LISTEN to me right now. I AM DYING, I GOT SICK...now I fell down and I'm Not...DON'T -- :-()-: GET THE FUCK OUT. :-()-: I'm dying, Claudia. I have cancer. I have cancer and I'm dying, soon. It's metastasized in my bones and I -- :-()-: FUCK YOU. FUCK YOU, YOU GET OUT. :-()-: I'm not lying to you, I'm not -- :-()-: FUCK YOU. YOU GET THE FUCK OUT OF HERE. :-()-: baby, please, please -- :-()-: I'M NOT YOUR BABY, I'M NOT YOUR GIRL. I'm not your fuckin' baby -- | magnolia | 1999 |
| 59084.0 | You motherfucker...you motherfucker.... YOU FUCKING ASSHOLE, WHO THE FUCK ARE YOU? WHO THE FUCK DO YOU THINK YOU ARE? :-()-: -- what-what-what, ma'am -- I -- :-()-: I COME IN HERE - YOU DON'T KNOW, YOU DON'T KNOW WHO THE FUCK I AM OR WHAT MY LIFE IS AND YOU HAVE THE FUCKING BALLS, THE <u>INDECENCY</u> TO ASK ME A QUESTION ABOUT MY LIFE -- | magnolia | 1999 |
| 59015.0 | -- see, I thought you grew up here in the valley -- :-()-: Like I said, yeah -- | magnolia | 1999 |
| 59054.0 | You're my handsome man. :-()-: I'm a bad person. :-()-: No. No. :-()-: No, I mean: I'm telling you this, now. You see? You see I want to make everything clear and clean...and apologize for me....for all the stupid things I've done....that will eat me up.... :-()-: You feel like you want to be forgiven for your sins? Honey, you're not on your death bed, yet....this kinda talk's gonna get you in trouble -- :-()-: --- don't. don't. Please. Just... listen to me...honey.... ...I've done...I've cheated on you. | magnolia | 1999 |
| 59102.0 | You have to be nicer to me, Dad. :-()-: Go to bed. :-()-: I think that you have to be nicer to me. :-()-: Go to bed. | magnolia | 1999 |
| 59056.0 | How many times....it's ok...just say... Just say... :-()-: I don't even remember...many...twenty... maybe more...not much more...twenty times. :-()-: I don't hate you, Jimmy. But I have a couple questions that I wanna ask.... :-()-: I'll answer anything. :-()-: Was there anyone that I know? :-()-: Yes. :-()-: Who? :-()-: Rose, I don't -- :-()-: hey. :-()-: Paula. Ellen. | magnolia | 1999 |
| 59074.0 | You have to go, Stanley. You're the smartest. :-()-: I don't wanna do it. Why can't one of you do it -- | magnolia | 1999 |
| 58929.0 | What do you want? Why are you here? :-()-: I'd like to talk to you. Your boyfriend let me in, I just knocked on the door -- :-()-: He's not my boyfriend. | magnolia | 1999 |
| 59012.0 | Hello? :-()-: Hi. Is Frank there? :-()-: I think you have the wrong number. :-()-: I'm looking for Frank Mackey. :-()-: No. :-()-: Is this 509-9027? :-()-: Yeah. You have the wrong number. There's no one named Frank here. :-()-: Alright. Thank you. :-()-: Yep. | magnolia | 1999 |
| 59053.0 | How do we do this, then? :-()-: We just do it...we do it and we figure it out and we do as we do, I guess... :-()-: Do you love me, Rose? | magnolia | 1999 |
| 59038.0 | This is the LAPD, if anyone is in the closet I want you to come out and show yourself to me, slowly and with your hands up -- :-()-: THERE'S NO ONE IN THERE! :-()-: Marcie - quiet down! Now if anyone is in the closet, come out now -- :-()-: THERE'S NO ONE IN MY MOTHERFUCKIN CLOSET AND STAY OUT OF MY BEDROOM, STAY OUT OF MY GOD DAMN BEDROOM. :-()-: -- do not do this -- my gun is drawn and If I Have To Open That Closet you will get shot -- Step Out Now. | magnolia | 1999 |
| 58918.0 | No need for braces, Donnie. :-()-: THAT'S NONE OF YOUR BUSSINESS. I HAVE BEEN A GOOD WORKER, A GOOD AND LOYAL WORKER FOR YOU, YOU FUCKING ASSHOLE. :-()-: HEY FUCK YOU DON WATCH IT NOW. | magnolia | 1999 |
| 58996.0 | I don't have any money, Solomon. If you fire me -- :-()-: -- I give you money, I give you a paycheck. Your sales suck, Don. I give, I give. When I find you, when I meet you, what? I put you on the billboard, I put you in the store, my salesman, my fucking representation of Solomon and Solomon Electronic, Quiz Kid Donnie Smith from the game show -- :-()-: I lent my name, my celebrity. Exactly -- :-()-: FUCK YOU. I pay you, I paid you. I give you a fucking chance and a chance and over and over, over you let me down. I trust you with so much. The keys to my store, the codes to my locks, the life, the blood of my bussiness and return is smashing in seven-eleven, late, <u>always</u> late, loans -- I loaned you money for your kitchen that you never did -- :-()-: I paid you back. :-()-: Two years! Two years later and out of your paycheck, I never charge interest -- :-()-: Solomon, please. Please. I am so fucked here if you do this. This is the worst timing. The worst timing I could ever imagine. I need to keep working. I have so many debts, so many things, I have, I have, I have -- I have surgery -- I have my oral surgery coming -- | magnolia | 1999 |
| 59003.0 | This is the number for Hospice. Ok. Now. As far as the morphine pills go, there is something else to consider that can take the pain away that he is in, there is a very strong and very potent solution of liquid morphine....it's a little bottle, with an eye dropper and it's easy to get in his mouth and drop on his tounge and it will certainly diminish the pain that he is in but you have to realize that once you give it to him; there really is no coming back, I mean, it will certainly cure his pain, but he will float in and out of consciousness, even worse than he is now, Linda. I mean, any sign of the recognizable Earl will pretty much go away -- :-()-: -- how the fuck can I say anything to that -- I don't know what to say to that -- :-()-: The job here is to make him as comfortable as possible -- right now -- our job is to just try and make it as painless as possible. Right? You understand? | magnolia | 1999 |
| 59100.0 | ...oh Jesus, what the fuck...? :-()-: I'm fine. I'm fine, I just wanna keep playing -- :-()-: Why did you do this? | magnolia | 1999 |
| 58946.0 | Is this boyfriend bothering you? :-()-: I don't have a boyfriend. :-()-: The gentleman who came to the door -- :-()-: -- is not my boyfriend. :-()-: Many times, in damestic abuse situations the young lady is afraid to speak, but I have to tell you that, being a police officer, I've seen it happen: Young woman afraid to speak, next thing you know, I'm gettin' a call on the radio, I got a 422 -- :-()-: It's not -- what's a 422? :-()-: It's where situations like these lead, Claudia, unless you do something about it early, if and when the police call and come for help. Now there are certain measures you can take -- :-()-: It's not my boyfriend -- and it's not anything -- it's over. Really. It's not. He won't came back. :-()-: I don't wanna have to come back here in an hour and find that there's been another disturbance. :-()-: You won't. You won't have to. :-()-: But I wouldn't mind comin' back in an hour just to see your pretty face! | magnolia | 1999 |
| 58944.0 | got some coffee brewing, huh? :-()-: Yeah...it's not...it's been on for a bit -- :-()-: I like iced coffee, generally, but a day like this, rain and what not, I enjoy a warm cup -- :-()-: -- do you wanna cup? :-()-: That's great, thank you. | magnolia | 1999 |
| 58980.0 | Doc it's Janet. :-()-: What's up? :-()-: I have to talk to Frank, is he nearby? :-()-: He's doing the interview with the lady -- :-()-: I need you to interupt him, I need to get him on the phone with me right away -- :-()-: What happend? :-()-: Doc, go get Frank and put him on the phone. | magnolia | 1999 |
| 58983.0 | Phil, hang in just one more minute ok? I'm gonna put you on hold -- Doc you still there? :-()-: Yeah, I'm here, I'm off the elevator, walking down the hall, now -- | magnolia | 1999 |
| 59096.0 | You ready to keep winning? :-()-: Sure. | magnolia | 1999 |
| 59081.0 | Hello. :-()-: Hi. | magnolia | 1999 |
| 58965.0 | C'mon guys, settle down -- :-()-: Cynthia? :-()-: What? :-()-: How much time do we have? :-()-: Not enough, what do you want? :-()-: I should maybe go to the bathroom. :-()-: Can you hold it? :-()-: I don't know. :-()-: Just hold it, you'll be fine. | magnolia | 1999 |
| 58942.0 | You mind if I check things back here? :-()-: It's fine. | magnolia | 1999 |
| 58945.0 | I don't know how fresh it's gonna be -- :-()-: Oh, it'll be fine, I'm sure, Claudia. :-()-: You take cream or sugar? :-()-: That'd be fine. So, Claudia, lemme just say, so I can get my role of LAPD officer out of the way before we enjoy our coffee I'm not gonna write you up or anything, I'm not gonna give you a citation here -- but the real problem we have is that there are people around here, people that work from their homes, people tryin' to get some work done, and if you're listenin' to your music that loud: They're incovenienced by that. If you had a job you'd probably understand, but I see you like listenin' to your music and that's fine, you're just gonna wanna keep it down at a certain volume, maybe memorize what number you see on the dial and just always put it to that -- If it's the middle of the day -- that's what I do -- just put it on two and a half and that's a good listening level, alright? I see you like listenin' to your music loud, but, hey, forget about the neighbors, you end up damaging your own ears ok? :-()-: Yeah. :-()-: Arlight, then. Cheers. | magnolia | 1999 |
| 58948.0 | Ok, ok. I'm back. :-()-: This is, for not a fresh cup, a great cup of coffee, Claudia -- :-()-: Thank you. | magnolia | 1999 |
| 58958.0 | -- you the resident here? :-()-: Yes. :-()-: You alone in there? | magnolia | 1999 |
| 59104.0 | Hi. :-()-: Hi. :-()-: ..sorry... :-()-: It's ok. | magnolia | 1999 |
| 59020.0 | You're hurting a lot of people, Frank -- :-()-: -- fuck you. | magnolia | 1999 |
| 58976.0 | How much you pay me for my help? :-()-: I think it's more complicated than that little man. :-()-: Put me on the payroll, find out, find out wassup -- :-()-: You don't just sign up to be a police officer -- it's about three years of training -- ok? :-()-: I'm trained, I'm ready to go, you wanna buy some candy to help underprivelaged youth in the -- :-()-: Sorry, little man. :-()-: You wanna take my statement, I'll perform for you, gotta get paid though, gotta get PAID. :-()-: Why the hell aren't you in school? :-()-: No school today. My teacher got sick. :-()-: They don't have substitute teachers where you go to school? :-()-: Nope. So what'd they find out in there? :-()-: That's confidential information, little man. :-()-: Tell me what you know, I'll tell you what I know -- :-()-: No Can Do. :-()-: Leave this one to the detectives, they ain't gonna solve shit, I can help you, make you the man with a plan, give you the gift that I flow -- think fast -- you wanna know who killed that guy? | magnolia | 1999 |
| 58953.0 | Did you ever go out with someone and just....lie....question after question, maybe you're trying to make yourself look cool or better than you are or whatever, or smarter or cooler and you just -- not really lie, but maybe you just don't say everything -- :-()-: Well, that's a natural thing, two people go out on a date, something. They want to impress people, the other person...or they're scared maybe what they say will make the other person not like them -- :-()-: So you've done it -- :-()-: Well I don't go out very much. :-()-: Why not? :-()-: I've never found someone really that I think I would like to go out with. :-()-: And I bet you say that to all the girls -- :-()-: No, no. :-()-: You wanna make a deal with me? :-()-: ok. :-()-: What I just said...y'know, people afraid to say things....no guts to say the things that they...that are real or something... :-()-: ...yeah... :-()-: To not do that. To not do that that we've maybe done -- before -- :-()-: Let's make a deal. :-()-: Ok. I'll tell you everything and you tell me everything and maybe we can get through all the piss and shit and lies that kill other people.... | magnolia | 1999 |
| 59018.0 | C'mon, Frank. What are you doing? :-()-: What am I doing? :-()-: Yeah. :-()-: I'm quietly judging you. | magnolia | 1999 |
| 58972.0 | GIVE US YOUR MONEY MAN. :-()-: Son, don't -- :-()-: BULLSHIT, BULLSHIT DAD WE GOTTA GET HIS MONEY -- :-()-: -- no. :-()-: GIVE US YOUR MONEY. :-()-: Put the gun down, please, boy. :-()-: GIVE US YOUR MONEY, KID. :-()-: Son, please, now.... :-()-: DAD -- :-()-: Please, boy, put it down and it's ok. | magnolia | 1999 |
| 59083.0 | You been on Prozac long? Dexadrine? :-()-: ...I don't.... | magnolia | 1999 |
| 59027.0 | How long...you think? :-()-: Um...soon tonight...I think, yes? Tommorrow...I mean...very soon...very... :-()-: When did he go off chemo? :-()-: About three weeks ago. :-()-: .....have you ever seen this..I mean, never mind, you said -- :-()-: I work as a nurse, for a proffesion -- :-()-: Uh. huh. :-()-: I'm really sorry. :-()-: He's in here --? :-()-: Yeah. | magnolia | 1999 |
| 59033.0 | You just woke up. And what'd you have a party last night, the way this place looks? :-()-: I went out last night. :-()-: Ok. Marcie. Starting now I want you to have a new attitude with me. The more you play games, the more suspicious I'm gonna become that you've been up to something. :-()-: It's a free country, you can think anything you want. :-()-: Yes I can, Marcie. And until you start givin' me some straight answers: I'm gonna assume that some mischief has been goin' on here. :-()-: Mischief? What the fuck you talkin' about, mischief? :-()-: Bad and illegal behavior. That's what I mean. Ok? Mischief. Now have you been doin' some drugs today? :-()-: No. :-()-: You on any medication? :-()-: No. :-()-: Been drinkin' today? :-()-: It's ten o'clock in the morning -- | magnolia | 1999 |
| 59088.0 | The fuck is wrong with you? :-()-: I gotta go to the bathroom. | magnolia | 1999 |
| 59107.0 | What is that? :-()-: It's frogs. It's raining frogs. :-()-: ...fuck you mean, it's raining frogs? :-()-: It's raining frogs from the sky. :-()-: ....what the fuck, what the fuck.... :-()-: This happens....this is something that happens. :-()-: What the fuck is goin' on, WHAT THE FUCK IS GOING ON? | magnolia | 1999 |
| 59075.0 | You don't want any water? :-()-: No...I just... I'm so fucked up here Alan, I don't know...there's so much...so many things -- :-()-: Are you on drugs right now? :-()-: If I talk to you...y'know...if I tell you things...then you're a lawyer, right? You can't say things, you can't tell anyone, it's like the privelage, right? Attornery-client, you understand? :-()-: Not exactly, Linda. I'm not sure where you're going with this -- :-()-: Like a shrink, like if I go to see a shrink, I'm protected, I can say things -- fuck -- I don't know what I'm doing -- :-()-: Linda, you're safe. Ok. It's alright. You're my friend. You and Earl are my clients and what you need to talk about won't leave this room, you have something you have to say -- :-()-: -- I have something to tell you. I have to tell you something. I want to change his will, can I change his will?...I need to --- :-()-: You can't change his will. Only Earl can change his will. :-()-: No, no....no, you see...I never loved him. I never loved him, Earl. When I started, when I met him, I met him and I fucked him and I married him because I wanted his money, do you understand? I'm telling you this now...this I've never told anyone...I didn't love him. And now....I know I'm in that will, I know, I was there with him, we were all there together when we made that fucking thing and all the money I'II get -- I don't want it -- Because I love him so much now...I've fallen in love with him now, for real, as he's dying, and I look at him and he's about to go, Alan, he's dead...he's moments... I took care of him through this, Alan. And What Now Then? | magnolia | 1999 |
| 58969.0 | Let's go, c'mon, get up -- :-()-: Did we win or lose, I mean --? :-()-: I don't know, Richard, they need to talk it over -- | magnolia | 1999 |
| 58979.0 | Did you listen to me? :-()-: I was listening -- :-()-: -- I told you who did it and you're not listening to me. :-()-: -- and I'm through playin' games. | magnolia | 1999 |
| 59089.0 | Did you piss your fuckin' pants, Stanley? :-()-: Shut up -- shut up -- | magnolia | 1999 |
| 58952.0 | Do you wanna go tonight? I mean, are you working? :-()-: No, I'm off tonight. I would lov-like, to go tonight, I can pick you up, I can pick you up here at about what time? What time? :-()-: Eight o'clock? :-()-: What about ten o'clock, is that too late? I don't get off and then -- :-()-: Oh sure yes, that's fine, late dinners are good. Should I get dressed up or -- ? :-()-: No, no, just casual maybe, maybe I thought -- there's a spot I like to go, it's real nice that overlooks a golf course and the course is lit up at night -- :-()-: Billingsley's? :-()-: Yeah, You know it? You know Billingsley's? :-()-: It's my favorite place -- :-()-: Oh, see? This is great. Ten o'clock. :-()-: Great, bye. :-()-: Bye. | magnolia | 1999 |
| 58943.0 | What are you lookin' for? :-()-: Claudia: Why don't you let me handle the questions and you handle the answers, ok? :-()-: ok. :-()-: You just move in here? :-()-: About two years ago. :-()-: Bit messy. :-()-: Yeah. :-()-: I'm a bit of a slob myself. :-()-: Yeah. :-()-: You and your boyfriend have a party last night? :-()-: I don't have a boyfriend. | magnolia | 1999 |
| 58936.0 | OPEN THE DOOR. :-()-: I'm coming! | magnolia | 1999 |
| 58973.0 | It's ok -- :-()-: We gotta get his money so we can get outta here -- we gotta -- :-()-: That idea is over now. We're not gonna do that now. | magnolia | 1999 |
| 59028.0 | I've heard your tapes on the phone. :-()-: Oh yeah. :-()-: When they put me on hold, to talk to you...they play the tapes. I mean: I'd seen the commercials and heard about you, but I'd never heard the tapes .... :-()-: Uh. huh. :-()-: It's interesting. :-()-: Mmm. | magnolia | 1999 |
| 59050.0 | I don't mean to cry, I'm sorry. :-()-: It's okay, Stanley. It's alright. | magnolia | 1999 |
| 58999.0 | I've been a good worker -- :-()-: Don't do this, Don. | magnolia | 1999 |
| 59058.0 | Why doesn't Claudia talk to you, Jimmy? :-()-: Why, well I think we've, we both don't know...what do you mean? :-()-: I think that you know. :-()-: Maybe...I don't... | magnolia | 1999 |
| 58991.0 | "If a brick weighs one pound plus one half brick -- how much does the brick weigh?" "Well if subtracting the half of brick from the whole brick you got one half of brick, equals one pound so therefore the brick equals two pounds --" "A little more than kin and less than kind," is Hamlet to Claudius. "The sins of the father laid upon the children," is Merchant of Venice but borrowed from Exodus 20:5 and "win her with gifts if she respects not words," is Two Gentleman from Verona. Where? Who? How and Why, Kids? :-()-: "Why don't you shut the fuck up," is me to you, Chapter Right Here, Verse Right Now. | magnolia | 1999 |
| 58930.0 | Wanna call me a slut now, something? :-()-: No. No. | magnolia | 1999 |
| 59097.0 | Go to it, handsome. :-()-: See you. | magnolia | 1999 |
| 58998.0 | And how much is braces? :-()-: It's...doesn't matter.... | magnolia | 1999 |
| 82825.0 | Dude, what are you doing here? :-()-: You killed me, remember? Now I have to hide out here with you. Where have you been? | xxx | 2002 |
| 82807.0 | Nothing. He had two days. :-()-: Perhaps he is not all he says he is. | xxx | 2002 |
| 82771.0 | We're on to the game, ese. You got the fake blood splattered all over the walls, you got your torture tools... It's all very cute. But come on, let's quit while you're ahead. I'm only trying to save you a beatin'. :-()-: You saving me? You talking pretty tough for a guy got himself chained to the ceiling. :-()-: Alright. You wanna eat through a tube, be my guest. :-()-: Now you're gonna make me enjoy this, funny guy. Now I'm gonna take the whole foot off. Whatchu think of that? | xxx | 2002 |
| 82838.0 | Don't look at me, X, she handles all the details. :-()-: It'll work faster if I have an account number. | xxx | 2002 |
| 82796.0 | Don't you see, X? For the betterment of humanity, he's going to start World War Three. :-()-: He's a regular humanitarian. And all this time I thought he was just a tool. | xxx | 2002 |
| 82765.0 | The usual prospects. Convicts, contract killers, murde... :-()-: The scum of the Earth. :-()-: But programmable. And expendable. :-()-: I've seen enough. Turn it off. | xxx | 2002 |
| 82751.0 | You've really got me confused, Cage. On the one hand you showed leadership, courage under fire, a willingness to protect men you hardly knew... and on the other you have an arrest record that pegs you as near sociopathic. Help me out here. I'm not following your evolution. :-()-: You want the cheap backstory? The runaway mom, the suicide dad and the foster homes? Gimme a break. You're not interested in my past, you're interested in my future as some kind of spy. :-()-: You're perceptive too. I forgot to add that to the list of surprises. I'm with the National Security Agency. And unlikely as it may sound, I need your help. :-()-: I'm not interested. I've already got a job. :-()-: You're an adrenaline junkie with one foot in the penitentiary. You risk your ass building a daredevil myth that means nothing and you're not getting any younger. :-()-: I plan on getting a lot older. And playing spy games sounds like a quick way to get yourself dead. :-()-: That's too bad. I thought a guy like you would appreciate the challenge. | xxx | 2002 |
| 82799.0 | Where's he headed? :-()-: He owns a big industrial complex on the river. He'll launch it from there. | xxx | 2002 |
| 82803.0 | He's shut out the communication circuit! :-()-: You can't talk to it? :-()-: No, it's like a safety. We are going to have to access the manual controls. :-()-: A manual control on a moving torpedo. This day keeps getting better and better. | xxx | 2002 |
| 82811.0 | Hate those Russkie choppers. Rattle- trap pieces of garbage. I'm agent Shavers. Toby Lee Shavers. I'm looking for what's his name. Three X' s. :-()-: That's would be me. | xxx | 2002 |
| 82795.0 | They left. To a fortress in the mountains. :-()-: We'll have to go after them. :-()-: Aren't you afraid? :-()-: A long time ago I learned how to put fear away in a little place in my head. Maybe that's my problem. If you don't feel fear you can do some amazing things, but you can also screw your life up in ways you can't imagine. :-()-: I must be healthy, then, because I'm scared to death. I guess I'd better get back to Yorgi. :-()-: You don't have to do that. :-()-: Yes, I do. You're right. I can't just let him go free. Besides, I can help you from the inside. :-()-: Aren't they gonna wonder were you've been? :-()-: I was supposed to wait for the cop to kill you and then kill the cop. They'll believe me. I've been lying for so long, one more time won't hurt. :-()-: When this is over, we're just gonna take off, the two of us. Take a vacation. :-()-: What the hell are we doing? We're doing all this together, trusting each other, making plans, and we haven't even slept together yet. | xxx | 2002 |
| 82783.0 | Bullshit. I see you look at everything, study everything, ask questions. I know exactly what you're doing. Empty your pockets. :-()-: You've got to relax, baby, you're paranoid. | xxx | 2002 |
| 82756.0 | Up yours. :-()-: Crude and defiant as always, Triple X. It fits so well because you're obscene. | xxx | 2002 |
| 82792.0 | Are you alright? :-()-: What the hell are you doing? You could've killed me! :-()-: I heard you talking. I could tell where you were in the room. :-()-: I don't know what's going on anymore. I thought I was square with that dude. Now everybody's switching sides like it's the WWF. | xxx | 2002 |
| 82763.0 | You know what they say, the only way to change the system is from the inside. :-()-: Oh yeah? Why don't we start by taking this thing off my ankle? The poison needles? :-()-: On come on, X. Did you really think we were that barbaric? :-()-: No needles, huh? What about the acid bath? :-()-: All for show. Just a psychological mind game to get you to do the right thing. :-()-: You're a jerk-off, you know that? | xxx | 2002 |
| 82817.0 | Now that I've given you the overview, we're going to spend the next couple hours going into extensive operational detail. :-()-: I thought that was in detail. | xxx | 2002 |
| 82845.0 | Screw the world. If I'm gonna die for something, it better be bitches and money. :-()-: That's what I'm talking about, man! That's why Anarchy 99 was born. | xxx | 2002 |
| 82750.0 | Congratulations, you've just graduated at the head of your class. :-()-: You're a cold piece of work. You almost got three people killed out there. :-()-: Good thing you were there to save the day. Come walk with me. | xxx | 2002 |
| 82787.0 | That's how you eat? It's like a gerbil. :-()-: I'm from L.A. :-()-: So why don't you tell me something about who you gather information for. They obviously have very deep pockets. Are you from a rival clan? :-()-: Let's just say I'm freelancing and leave it at that. :-()-: Whoever sent you knew what they were doing. You and Yorgi are practically twins. Two nihilistic fashion victims who make a lot of noise but don't say much. :-()-: Don't judge a comic book by it's cover. :-()-: You agree with a lot of what he says, don't you. :-()-: I know where he's coming from. The only thing I really don't get is why he dumped you. Up until I found that out, I thought his judgment was pretty sound. :-()-: Don't even start. I'll be honest, I don't trust you. Bur lets pretend for a minute that what you say is true, that I want to leave. What can you do about it? :-()-: The people I know want facts, the kinds of things an insider would know. They're interested in putting Anarchy 99 out of business. :-()-: All I have to do is risk my life providing you with these facts. :-()-: We could work together. Then maybe we could both get the hell out of here. There's a beach in Bora Bora with my name on it. :-()-: A beach named X? That I'd like to see. :-()-: So there you have it. I guess you just have to ask yourself, how bad to you want out? | xxx | 2002 |
| 82789.0 | I can't let this happen. I want you to go out the back. I'll make up some excuse. :-()-: They'll kill you, you know they will. Besides, there is no back door. Keep smiling. Ready? Now. | xxx | 2002 |
| 82852.0 | You both could have been a part of it. You could have ridden with us into a new day, the dawning of a new age of -- :-()-: Oh just shoot us already. I'm sorry I asked. | xxx | 2002 |
| 82810.0 | The Peace conference... :-()-: Nice place to start, don't you think? | xxx | 2002 |
| 82773.0 | Next we got a Siberian redneck, Viktor. Into snowmobiles and snowboarding. So naturally, he covers prostitution and the drug trade. :-()-: Naturally. | xxx | 2002 |
| 82840.0 | I had to inspect the merchandise first. :-()-: Of course. | xxx | 2002 |
| 82822.0 | My name is Slovo, Czech secret police. When you are here you are under my jurisdiction, you take my orders, you do what I say. If you become any kind of an inconvenience, I'll shoot you. :-()-: Here we go again. :-()-: You're here because your government is putting pressure on my government. This is an internal affair, a Czech affair, that you are interfering with. I will warn you once: Don't shit in my lawn. Get whatever information your government seeks and get our. :-()-: First of all, you should kill whoever sold you that suit. Two, I don't wanna be here either, so just step off. Three, if you had the authority to shoot me you would've done it by now, so just ease up on the machismo, bitch. Now that we've got that sorted out, I'm gonna get some rest. | xxx | 2002 |
| 82843.0 | I don't believe it! You can't shoot a cop in the middle of the street! :-()-: Had to do what I had to do. | xxx | 2002 |
| 82784.0 | Hey, you're good. :-()-: Maybe we should start again, yes? I know so little about you, except that you're not what you say you are. :-()-: That makes two of us then, sweetheart, because I ain't buying your bullshit either. You're not like them, I can see it in your eyes. So you tell me, who's bullshitting who? | xxx | 2002 |
| 82834.0 | Cops. Like a plague. No matter how many you pay there's always another with his hand out. How did you pick him out? :-()-: He flashed his badge to half the bar when he bought his drink. :-()-: I appreciate you bringing this to our attention. Whatever you want, the rest of the night, consider it on the house. | xxx | 2002 |
| 82831.0 | Help me with this guy. :-()-: It's like you said, every man for himself. | xxx | 2002 |
| 82837.0 | We seen all your shit! So you want cars? We get whatever cars you need. What are you looking for? :-()-: Ferrari's, Lamborghini's... high end pasta rockets. Ten to start. | xxx | 2002 |
| 82766.0 | They're degenerates. There's not a man in there that would give a damn if the Chinese took over. :-()-: That's exactly why we need them. | xxx | 2002 |
| 82785.0 | The eyes don't lie. All this has gotten to you, hasn't it, Petra? You came in as Yorgi's girlfriend and you stayed because it was fun. Now you don't like it so much, but you're in so deep you can't get out anymore. Tell me if I'm wrong, Petra. :-()-: Go to hell! :-()-: Look at you. You're helping run things now. You're a gangster. I bet that snuck up on you. You woke up one day and you were a criminal. :-()-: I don't know what you're talking about. | xxx | 2002 |
| 82809.0 | What was that for? :-()-: I'd forgotten how exciting it is working together. :-()-: You've been so quiet lately, Petra, I thought you no longer cared. :-()-: Why don't we go below decks and work off some of this adrenaline. :-()-: Viktor! Stay the course. | xxx | 2002 |
| 82782.0 | Who are you? :-()-: We hung out last night, remember? :-()-: I also remember you drove your car here. Who are you really? Make no mistake, I will shoot you and not feel bad about it. Who are you working for? :-()-: Hey, take it easy. I'm just a dude trying to make a buck. | xxx | 2002 |
| 82804.0 | It's running three meters down. Twenty knots. :-()-: It has to surface to release the nerve agent, right? :-()-: By then it's too late! The only way to safely dispose of the nerve agent is in deep water! The chemicals will break down and dissipate! :-()-: Use your grenades! Maybe we can blow it up underwater! | xxx | 2002 |
| 82781.0 | Come on, don't front like that. You'll put a guy right off you. :-()-: Don't even bother, X man, I'm not your type. :-()-: That right? Why's that? | xxx | 2002 |
| 82776.0 | Just about anything I want, it looks like. This is gonna be tough, though. There's no way to save this game. I gotta get it right the first time through. :-()-: What do you get if you finish? :-()-: Nothing, really. My guy is just doing it to stay alive. :-()-: Oh. I bet by the end he gets something out of it. :-()-: Like what? :-()-: He gets to be the hero. | xxx | 2002 |
| 82820.0 | What...? :-()-: I was gonna wait till the islands, but... :-()-: You're serious? :-()-: Of course I'm serious. I bought it, didn't I? :-()-: This is so typical. I can't believe you. I've barely seen you for the last three weeks and now this? Are you out of your mind? :-()-: I don't know. I thought this is what you wanted. You want stability, here it is. :-()-: You can't just propose to me out of nowhere. You think that's going to solve our problems? I'm sorry, X. It was a kick for a while, but it's over. You're just not going anywhere. :-()-: You're not exactly "going anywhere" yourself. :-()-: You're wrong about that. I'm heading out that door right now. | xxx | 2002 |
| 82772.0 | What game is that? :-()-: Slick graphics, huh? See these dudes? They're called "Anarchy 99", they're the bad guy bosses. :-()-: Do they have any special powers? The bosses always have special powers. :-()-: You wanna check it out? Come here. | xxx | 2002 |
| 82761.0 | Snow covered fortress. Army of bad guys. The usual. :-()-: I'm on the way with a team to relieve you. What's the latest? :-()-: They retrofitted Ahab with some kind of rocket launcher. They were loading canisters of liquid into it, light colored and dark colored in the same tube but separate. Something tells me this it that "classified" stuff you didn't want me to know about. Does the song "Silent Night" mean anything to you? :-()-: Yes it does. :-()-: Come on, Gibbons, you can do better then that. :-()-: Silent Night is the name of a top secret binary nerve agent. The glass canister is shot into the air and detonated. The black and white chemicals mix, forming a toxic cloud. When it settles down to Earth, it'll kill everything in the vicinity. :-()-: Jesus Christ, Gibbons, this is something we came up with? :-()-: Yeah, that's right. So now that you know, you understand why it's important that we get it back. :-()-: Kinda funny, though, isn't it? We're not supposed to be making weapons like that anyway. Guess we shoulda played by the rules. :-()-: Somebody else makes those decisions, not guys like you and me. You've done your job, X, head back to Prague. I'll be landing in 90 minutes. This is my operation now. :-()-: We don't have that kinda time, boss, I don't think Prague's gonna be around much longer. I've gotta go now. I've got a lot of bad guys to kill. | xxx | 2002 |
| 82778.0 | Do you know what a wire transfer is? :-()-: Is she for real? Honey, maybe you should quiet down and let the grownups have a conversation. :-()-: My goodness, a word with four syllables. I should get some ice before your brain gets too hot. :-()-: Sure. Just chisel some off your heart. :-()-: So cute. He shows up for a battle of wits with a mental butter knife. | xxx | 2002 |
| 82768.0 | Alright, do what you want. But keep the screws on him. He's a wild card. That could be either good or bad. :-()-: So the odds are up to fifty-fifty? I can deal with that. | xxx | 2002 |
| 82806.0 | What are you doing? :-()-: It's about your next mission. You need to be debriefed. | xxx | 2002 |
| 82836.0 | I remember that one where you jump the motorcycle over the freeway at rush hour. :-()-: When else are you gonna do it? | xxx | 2002 |
| 82760.0 | Thanks. :-()-: You're a grungy little phoenix, you know that? Keep up the good work. :-()-: You ain't seen nothing yet. | xxx | 2002 |
| 82828.0 | What's so damn funny? :-()-: Been to any good diners lately? | xxx | 2002 |
| 82798.0 | Did you do your homework? :-()-: If they launch it, I know how to bring it back. :-()-: Let's do this. | xxx | 2002 |
| 82827.0 | Sorry, "dude". :-()-: What's the deal? :-()-: Oh come on, don't feel bad. You got played, so what? You're new at this. Did you think you could just stroll into this business one day and have all the angles figured out? :-()-: Why you dogging me up? :-()-: They came and found me. They said they'd kill me unless I help them. Plus they offer me a lot of money. No big deal. These things happen. Dump your gun on the floor. | xxx | 2002 |
| 82847.0 | You got a great set-up here Yorgi. You really know how to live. :-()-: It's a beautiful town, Prague. It's been good to me. :-()-: I've been here before, when I was a kid. My old man was in the service, we used to live on the Army base in Hamburg. :-()-: You, an Army brat? I don't see that one at all. Did you join the service as well? :-()-: Hell no. My dad was a straight up tin soldier. Somehow he pissed this general off and got himself dishonorably discharged. Had a court martial and everything. The charges were total bullshit, so he was sure he'd get his name cleared, but it didn't happen. :-()-: Connections and politics, it's the same everywhere. :-()-: My old man, he bought into the system, and it screwed him. So he swallowed a bullet. Me, I don't believe in nothing I can't see and touch. | xxx | 2002 |
| 82797.0 | Yorgi and Kirill. :-()-: Let me guess, they had a big white torpedo with them. | xxx | 2002 |
| 82786.0 | What are you? :-()-: I'm an information gatherer. You wanna go somewhere and talk about it? | xxx | 2002 |
| 82841.0 | What's this? :-()-: This is a gift. From me to you. | xxx | 2002 |
| 82759.0 | You did say "Ahab"? You're sure about that? :-()-: Yeah, I sent you pictures. Here's what I'm thinking. If they take out the cameras and sensors, they could probably put a bomb in this thing. You drop it in the water in the Red Sea and three weeks later it's swimming up the Potomac. :-()-: This is good work, X, damn fine work. You need to press on at all costs, find out what they have planned with this "Ahab". :-()-: Hey, one miracle at a time. They're on to me now, remember? :-()-: I'm gonna be en route with a team shortly to relieve you. Just keep the pressure on until the cavalry arrives. And X? You done yourself proud helping those people today. | xxx | 2002 |
| 82770.0 | We seen this kind of parachutes before, you know. U.S. Army. You got some friends here, jump out with you guys? :-()-: We're anti-social. We don't have any friends. | xxx | 2002 |
| 82819.0 | French Polynesia? This wouldn't last us a week in a cheap hotel. Do you know how expensive it is down there? :-()-: Alright, so I'll get more. :-()-: It's not about the money, X! You never plan for anything. I can't live like that anymore, I need some stability. :-()-: Well if you're bailing, I guess I'd better give you your surprise now. | xxx | 2002 |
| 82832.0 | They're all over the place! What the hell's going on? :-()-: Looks like we're in the middle of the drug war. | xxx | 2002 |
| 82835.0 | Thanks, but I'm here on business. I heard you're the G around here. I'm looking for some cars, expensive ones. A lot of them. :-()-: Sorry, man, don't know what you're talking about. :-()-: I'm talking about the sports cars that disappear off the docks in Genoa and wind up here. If you don't know about 'em, who does? | xxx | 2002 |
| 82813.0 | Miniature power cams set in on contact, giving you a sure grip on any surface. :-()-: They come in any other styles or colors? | xxx | 2002 |
| 82839.0 | Very nice. I'm impressed. :-()-: I'm somewhat less so. We seem to have a bit of a problem... | xxx | 2002 |
| 82779.0 | If you got a problem with me, why are we dancing? :-()-: Yorgi asked me to. :-()-: You do everything Yorgi says? :-()-: Go to hell. :-()-: It's gonna be like that, huh? You got all bent out of shape as soon as he started dancing with someone else. Why's that? :-()-: Mind your business. :-()-: Did you guys used to date? That's it, right? He broke your heart and you're still soft on him. That's funny, it don't seem to fit with a tough broad like you. | xxx | 2002 |
| 82754.0 | How about a pedicure as long as you're down there. What's this? Lo-Jack? :-()-: Wherever you go on the planet, I'll find you. There's no quitting. If you try to take it off, a ring of needles will inject enough curare into your bloodstream to kill you before you hit the ground. Is all that clear? :-()-: Yeah, I spy or I die. | xxx | 2002 |
| 82833.0 | That's the guy. :-()-: This pizda? Never seen him before. :-()-: Who you workin' for? What do you do for a living, dickhead? | xxx | 2002 |
| 82801.0 | Toadies right behind. :-()-: Let me. | xxx | 2002 |
| 82816.0 | From the Beastie Boys collection? :-()-: It's a stakeout suit. It's got food, water, recording gear, anything you need for covert spying. It's all- weather, fire retardant, and if you give this buckle a sharp pull, the whole outfit deploys into a parachute. :-()-: You're joking, right? | xxx | 2002 |
| 82826.0 | I found something big enough for us to take these guys down with. They've got a of nerve agent they're gonna unleash. :-()-: Is this what you're looking for? | xxx | 2002 |
| 82788.0 | They tell me you're an American agent. :-()-: What are you talking about? :-()-: There's no more time for games. They made you. There's a sniper out front waiting to put a bullet in your eye. Tell me if it's true. :-()-: It's a long story, but yeah, more or less. :-()-: Jesus Christ. You're going to have to go out the back. The data that you copied with your toy. Tomorrow, at six o'clock. Look it up. We're taking a trip. Be there at six and you'll have plenty to tell your people. :-()-: If I go out the back, he'll know you've warned me. :-()-: That's alright, I'll figure something out. :-()-: Not an option. Just get up slowly like everything's cool. Petra, trust me. You just hung yourself out to dry for me, I'm not gonna let you down. | xxx | 2002 |
| 82846.0 | To us it means no walls, no speed limits, no jails. It's everybody does what he wants. People think democracy is freedom but they don't have a clue. There's an old punk song. It says: "America stands for freedom, but if you think you're free..." :-()-: "...try walking into a deli and urinating on the cheese". 'Anarchy Burger' by the Vandals. :-()-: You got it, man. It's stupid but it's true. True freedom is when you do whatever you want anytime you want. That's when you know you're living, man. :-()-: How you gonna do that with government and rules everywhere? :-()-: Easy. You get enough money that you grow an ass big enough for the whole world to kiss. | xxx | 2002 |
| 82793.0 | What are you doing with the Ivans? :-()-: I know which cops are good and which are bad. Come with me. Come on, right now. | xxx | 2002 |
| 82851.0 | What's the gimmick, Yorgi? That's the part I don't get. You gonna hold the world hostage with your gas bombs? I didn't think you were dumb enough to go with that hack cliche. :-()-: You think I'm after money? I told you, my friend. Anarchy. Time for a change. Money is good, but for true freedom you have to get rid of the rules themselves. :-()-: So all by yourself, you're going to destroy every government; in the world. :-()-: Easier than that, buddy. I'll get them to destroy each other. You kill an entire Peace conference, someone is going to have to pay. Then our friend Ahab continues down the river to the ocean and begins his world tour. London, Cairo, Beijing... Pretty soon everyone's involved. These guys bomb those guys, those guys invade these guys. Soon the whole world is like your wild West cowboy days. No rules, no law, everybody free to do what they want. :-()-: Including rape, pillage, murder. :-()-: Sure, if that's what you want. Why not? It's all human nature. So we just have to launch our baby and wait for the decline and fall of civilization as we know it. :-()-: And here I thought "anarchy" was just something cool to put on a Tshirt. :-()-: Come on, Xander. You used to stand for something. What happened to you, man? I thought you'd get it. | xxx | 2002 |
| 82844.0 | Total chaos, man! Welcome to Anarchy 99! :-()-: What's "Anarchy 99"? :-()-: It's all this craziness! It's what we've been living since 99, when we left the Army. One of our brothers died in Grozny and we said the hell with this shit. What for? He dies for what? Politics? Who's politics? Not ours. :-()-: You wanna see my politics? | xxx | 2002 |
| 82758.0 | I guess you got my E-mail. I set up a purchase. Ten cars. :-()-: If you're trying to push my buttons, you're on the right track. Don't make me question my own judgment, X. :-()-: Buddy, you sent me here to get close to their organization, that costs money. :-()-: One point two million dollars? I did not authorize you to spend one point two million dollars! :-()-: I'm already on a first name basis with these dudes, I got a deal set up, you want me to hammer it or not? :-()-: We're not after car thieves here. :-()-: What the hell am I after? You're telling me dick. :-()-: That information is classified. You're there to gather information on their operations, period. :-()-: Call me crazy, but I thought hooking up a million dollar deal was a great way to get on their good side. What else? You gonna hassle me about the weapons and spy stuff too? :-()-: My friend, if you're planning on crossing me... :-()-: I know, poison needles in my shins. You've got 36 hours. Peace, out. | xxx | 2002 |
| 82821.0 | Sit down. :-()-: I've been on a plane for twelve hours, I think I'll stand. | xxx | 2002 |
| 82767.0 | Ridiculous. Cut him off. I'm sorry Gibbons, you pulled up a shark this time. :-()-: I think we should send him whatever he wants. :-()-: You what? A wire transfer of this size? :-()-: If it means getting Silent Night back, absolutely. :-()-: You're talking about a very expensive risk here. :-()-: He's gotten closer in 24 hours than all of the other operatives combined. He got us an account number to boot. Now even if he's just dumb and lucky, I say we back his play. I put him out there, Sam. If he doesn't come up with the money, they'll kill him. I can't let that happen. :-()-: Why not? You were going to throw him in a tub full of acid, weren't you? | xxx | 2002 |
| 82749.0 | Most guys we ran through this either took off or helped them rob the place. I expected you to do the same. :-()-: That shows me already that you don't have a clue. What's it to you, anyway? :-()-: Whether you thought this was for real or not, you jumped in and helped the waitress on instinct. That tells me something about your character. :-()-: Good for you. Now why don't you stop wasting my time and tell me what you want. You didn't go through all this for my benefit. :-()-: It's your lucky day. You just might get the chance to pay back our wonderful country for all the freedom you enjoy. :-()-: A Fed, I shoulda known. Who else would have this kind of budget? Now you're gonna hit me with the sales pitch. :-()-: Alright, Cage, you've got me. This is one of those moments. "Many are called, few are chosen", that kind of thing. Your government needs you. Are you up for the challenge? :-()-: ..If you're trolling around for narcs, man, have you got the wrong guy. :-()-: Why is that? :-()-: Look at me, dude, do I look like a fan of law enforcement? Forget the tests, you shoulda just asked me. I woulda saved you a lot of trouble. :-()-: Oh I don't know, I think the tests work pretty well. Sometimes they give me answers you wouldn't admit to in a million years. :-()-: Are we done now? You've got nothing on me. So if you ain't booking me, I'm walking. :-()-: You've got this wrong. You're not under arrest, you've been abducted. And until I say different, you belong to me. :-()-: Is that a fact? :-()-: Sure as gravity. I've had a feeling about you from the start, Cage. It's nausea. :-()-: You know what I hate? Any scumbag with a clean shirt and a bad haircut can get one of those tin stars and suddenly they think they're God. :-()-: You know what I hate? It's always the assholes that pass the tests. | xxx | 2002 |
| 82800.0 | Where'd the damn truck go?! :-()-: Go to the water, it's that way! | xxx | 2002 |
| 82850.0 | And you. Why do you have such a problem staying away, my friend? Did you miss us that much? :-()-: I wanted to get the hell out of Prague before Silent Night falls. :-()-: Not bad, man, not bad. You put things together quick. Prague it is. | xxx | 2002 |
| 82748.0 | Who the hell are you? :-()-: The name's Gibbons. You were saying? :-()-: What is this place? :-()-: Looks like a diner. :-()-: Diner, huh? Let me tell you what the problem is. I wake up drugged to find what? You've got a "salesman" over here reading a three week old newspaper. He's pulling a 211 with a "trucker", who happens to be packing a cop issue H&K 9mm. I get tipped that something is going down when she passes me a note. How's that for twisted logic? How did she know there was trouble unless they pulled the guns before I "walked" in? And if that's true, why'd they stop and wait until I woke up to gaffle the money? Then I notice how beefy they both are. Hell, even the waitress is cut. All three of them look like they went through the same training program. Ergo they ain't strangers and this isn't what it seems. That's how I knew yo-yo wouldn't get a round off even if I gave him all day. Why? | xxx | 2002 |
| 82849.0 | That was for Anders. :-()-: Don't forget goatee boy. I greased him up on the diamond run. | xxx | 2002 |
| 82794.0 | Where are we going? :-()-: We're getting out of this place. We've done enough. :-()-: Wait a minute, whoa. We can't go now. What's Yorgi got planned with that nerve agent? :-()-: It doesn't matter anymore, forget it. :-()-: Of course it matters. Hey, hold on! :-()-: What do we owe our governments? Yours kidnapped you, mine abandoned me. :-()-: Petra, this ain't about the people that sent us here, you know that. :-()-: No, now it's about us. Let's do something for us before we both get killed. Don't you want to get to Bora Bora, Xander? :-()-: We can't leave now. We have to do something. :-()-: Why? You're the one who believes in nothing. Screw the world, or whatever you said. | xxx | 2002 |
| 82815.0 | I think I'll hang on to these. :-()-: Think again, that's government property. You have to sign for everything. I've got one more that wasn't on your list. | xxx | 2002 |
| 82818.0 | Hey, you don't need all this just to go on vacation. Unless this ain't for going on vacation... :-()-: I'm through, Xander. :-()-: I can see that. Why? :-()-: You run around with maniacs jumping motorcycles in the desert, you break 36 bones crashing wave runners and snowboarding off cliffs, all with no health insurance... It's insane, I can't take it anymore. :-()-: I'm having fun, what's the problem? :-()-: You have all this talent, and you waste it. You won't take a single endorsement deal. Meanwhile those other guys have their own video games! But no, you've got too much "integrity" for that. :-()-: I don't wanna go mersh, you know that. But hey, if this is about money... | xxx | 2002 |
| 82777.0 | Ten is hardly worth the effort. We're talking a lousy million five U.S. :-()-: I have Japanese buyers who are looking to move a fleet, if you have the quality of merchandise they're after. And it's a mil two, max. | xxx | 2002 |
| 82824.0 | I'll leave you two alone to talk. :-()-: Yeah, thanks a lot. | xxx | 2002 |
| 82752.0 | Now I'm the one who's nauseous. So what's the deal? What do you need me for? :-()-: There's some folks I want to keep tabs on. Dirty, tattooed, uncivilized. Your kind of people. :-()-: What do I get out of it? :-()-: If you find out what I want to know, and if I'm able to successfully use that information, you get to go back to your degenerate little life. If not, you take a bath. :-()-: You're one sick bastard. A sadist with a badge looking to rope me in to a suicide mission. I think no matter which way I go I'm likely to wind up face down on a sheet of plastic. So here's my answer: kiss my ass, Hop-along. | xxx | 2002 |
| 82753.0 | Not bad for a gimp. :-()-: What's with that "X" on the back of your head? Does that mean you're "extreme"? I've got some news for you, Mr. X, you're a three time loser. So maybe you should tattoo another couple of X's on your head. | xxx | 2002 |
| 82805.0 | I can't believe my mission is finally over. I don't even know what to do with myself. :-()-: You can't welch on me now. We had a deal. | xxx | 2002 |
| 82762.0 | Thought you bought the farm down there, X. Glad as hell to see you. :-()-: I'm pretty happy to see me too. So are you done with me yet? :-()-: You've kept up your end of the bargain, I'll do the same. But you really should consider staying on, you make a decent agent. :-()-: But I hate cops, remember? Except for her. :-()-: Cut the crap, X, I saw you down there. You're a hero. Don't be afraid to join the good guys. :-()-: Who says you're the good guys? | xxx | 2002 |
| 82775.0 | Yorgi masterminded the take-over of three Red Mafiya clans. Cops called it "blood week". He combined all of their global enterprises into one huge crime syndicate: Anarchy 99. :-()-: "Anarchy" 99"? :-()-: What are you gonna do? They're Euro- trash. :-()-: What weapons do you have? | xxx | 2002 |
| 82842.0 | What's going on, my friend? :-()-: You tell me! You got a sniper up there or what? :-()-: He's not with us, Xander. He must be with you. :-()-: Bullshit! You get your boy off that roof or I swear to God I'm gonna give you another hole to breathe out of. | xxx | 2002 |
| 82755.0 | I expect you to call in regularly with progress reports. :-()-: Fine, but I'm not gonna be no Bulldog Omega 5. | xxx | 2002 |
| 82769.0 | Whatchu doing here? :-()-: Oh, I don't know. It was an accident. :-()-: That's some accident, hombre. You accidentally fall out of a plane in the middle of the night and land up in my back yard. | xxx | 2002 |
| 82808.0 | What are you doing? :-()-: It all happened so fast. Such an unfortunate accident. | xxx | 2002 |
| 82790.0 | So what's the plan with this Ahab? Are they selling it or what? :-()-: No, something worse. He says it's a surprise, but I know him. This is his masterpiece. :-()-: We've got to find out what it is. Can you handle that? :-()-: There's something I didn't have time to tell you before. :-()-: What's that? :-()-: I'm a agent as well. KGB. I've been undercover for over a year. :-()-: You're what? What the hell have you been doing? :-()-: I don't know. Eight months ago I stop hearing from my people. No explanation, just silence. So I stay and I wait like I was told. Soon I'm in so far, they'd kill me if I left. Now every day I get farther and farther away from what I was. I'm like you said, a criminal. :-()-: I'm busted up for you, but Jesus, what the hell are you doing? Those people almost drowned on that boat and you didn't lift a finger. | xxx | 2002 |
| 82830.0 | Who's writing this dude's dialogue? :-()-: He's a pretty good actor, though. | xxx | 2002 |
| 82802.0 | You okay? :-()-: A piece of shrapnel hit me. :-()-: I'm sick of that guy. Let's stop playing nice. | xxx | 2002 |
| 82764.0 | What do you have? :-()-: Not a whole helluva lot. His final transmission was mangled. About something or someone called "Ahab". Whatever it is, it cost him his life. :-()-: He was the best there was. That makes three agents lost. :-()-: They're ruthless and they have a lot of firepower. It's only a matter of time before they figure out how to deploy Silent Night. Then we're gonna have a catastrophe on our hands. :-()-: Silent Night in the hands of a bunch of impertinent cowboys :-()-: We're gonna have to step in, Sam. Those CIA boys couldn't find a clown in a field of cactus. Let me take care of it. :-()-: How will you fix it? :-()-: I'll go in with a team. But first I'm going to need some intel. I'll have to put someone inside, someone new, someone they won't see coming. One of their own. :-()-: So you'll dredge the bottom again. You've done that before. The results were... uneven. :-()-: You wanna find out about rats, ask a rat. I've had my people put together a talent pool. I downloaded it to your desktop. | xxx | 2002 |
| 82823.0 | Nice hops. :-()-: The corner. Anarchy 99. | xxx | 2002 |
| 82780.0 | Where are you going? :-()-: Why are you still hanging around? Your business is finished, you should go home. :-()-: I was invited. What's your story? :-()-: I don't know who you are or where you come from, but I don't like you. You ask too many questions. | xxx | 2002 |
| 82774.0 | This guy's kinda dorky lookin'. :-()-: Kirill, the sniper. Looks like a bookworm, but he had 72 confirmed kills in Chechnya, they called him the "Finger of God". Assassination and weapons. | xxx | 2002 |
| 82829.0 | Heads up, man. What's this thing on my back? :-()-: A parachute. This does not argue well. | xxx | 2002 |
| 82848.0 | I've got meetings this afternoon, I've got to get some sleep. Just pick a girl. :-()-: That's alright, I'm kinda tired. :-()-: You want to insult me? This is my hospitality. Pick one. | xxx | 2002 |
| 82757.0 | Is that all? :-()-: Just remember, I'll be watching. | xxx | 2002 |
| 82791.0 | No, I guess I've been no help at all to you. :-()-: Get your head back in the game. There are lives at stake here. :-()-: Of course. I'll do what I can. I have to go, before they notice. :-()-: When can I see you again? :-()-: You can't, it's too risky. I'm not much help anyway, remember? | xxx | 2002 |
| 82812.0 | Back off, just go away you klutz. Alright, here's the story. The items in these cases belong to me. I designed them, built them, and was going to use them in the field myself until you showed up. :-()-: I stole your beat, huh? Guess you forgot to brown-nose the right people. :-()-: Is that supposed to be funny? I'm not laughing. I've worked for ten years to get my shot at being a field agent, funny boy. And ar the last minute I get bumped by you, some reject from the Ozzfest. :-()-: Why don't you show me some gear before you get hurt. :-()-: Listen to you. Right away, sir, anything you say, sir. | xxx | 2002 |
| 82814.0 | This is your standard dart gun. :-()-: That one I'm real familiar with. | xxx | 2002 |
| 47127.0 | Mac's signature. :-()-: Give me a break. Remember Manzini? When he stole Montezuma's scepter he left a Pepto Bismal bottle. The best ones always copy Mac. :-()-: You're saying the thief wants us to think it's Mac but it's really not. :-()-: Exactly. | entrapment | 1999 |
| 47172.0 | We're going to die, aren't we... :-()-: I'm not the one to ask... | entrapment | 1999 |
| 47178.0 | Lose something? :-()-: I'm just curious what sort of security system you'd have in your own house. :-()-: And-- :-()-: I'm impressed. Can't spot a thing. :-()-: Hmmm. I'd be surprised if you could. | entrapment | 1999 |
| 47254.0 | You can't be serious. :-()-: That's the beauty of it. There's only one tiny window of time when this will work. At the handover of Hong Kong--from Britain to China. The handover from them to us. :-()-: You're out of your mind. We can't do a job like this with no rehearsal. :-()-: There's no way to practice this. And no time. Besides, I've planned it all. There aren't any surprises. :-()-: There's always a surprise. :-()-: I've covered everything. And you're the best, so-- :-()-: You're overestimating yourself again. What's worse, this time you're overestimating me. :-()-: Look, there's a video security system to bypass, that's the only hard part. You've done that a dozen times. :-()-: For God's sake, the only way I can get from one elevator to the other is to jump. :-()-: Feeling old? | entrapment | 1999 |
| 47307.0 | My friend, you always surprise me. :-()-: We wear so many goddam masks after a while they get stuck to our faces, you know? And they don't come off. But when you find the one person who really knows you, and then you lose her... | entrapment | 1999 |
| 47187.0 | I go in alone. :-()-: You don't get the Mask code unless I go. | entrapment | 1999 |
| 47232.0 | We're living history here. :-()-: You don't know the half of it. | entrapment | 1999 |
| 47240.0 | Insidious thing, wondering if your partner...has another partner. :-()-: Okay, look, I'm delivering this to a man who's going to give us the key to our job. But it's pointless to try to explain it yet. You just have to trust me. I don't have any more secrets. :-()-: Everyone has secrets, it's what makes us human. | entrapment | 1999 |
| 47303.0 | My antenna is up, it is fully extended, and I am picking up...what is it? It is, can it be? The boy is in el, u, v. :-()-: It's never happened before. What makes you think it's happening now? :-()-: You better be keeping your mind on business. It's not just me you got to worry about. I've got some very unpleasant folks looking over my shoulder. :-()-: You know where my loyalties lie. :-()-: To yourself, that's where. | entrapment | 1999 |
| 47166.0 | A film case. :-()-: That's what you're buying. Put it in your pocket. | entrapment | 1999 |
| 47218.0 | One, two, three... :-()-: You're not on the beat. | entrapment | 1999 |
| 47188.0 | Pack up. I'll see you get back to London. :-()-: Look, I can help. You need a sensor expert. You've got one. :-()-: This isn't some Picasso print you steal out of a car dealer's rec room. | entrapment | 1999 |
| 47198.0 | Let's do it again. :-()-: Try laps. Say a hundred. | entrapment | 1999 |
| 47190.0 | Nice try. Everyone thinks I did. :-()-: That's because I wanted them to! :-()-: I wondered who'd been giving me a bad name. :-()-: I drilled the bolts and went in through the window. It was the only way to bypass the smart glass. :-()-: True enough. :-()-: You need a partner for this job. You'll never find one as good as me. | entrapment | 1999 |
| 47293.0 | Right. :-()-: Because I never assume anything. :-()-: I need you to get one more thing for me. A dress, elegant but sexy, something Grace Kelly would wear. Maybe a Balenciaga. :-()-: That's it, I sure as hell ain't no personal shopper. :-()-: Black of course. | entrapment | 1999 |
| 47203.0 | Got it. :-()-: Charged? :-()-: Charged. Receiver? | entrapment | 1999 |
| 47170.0 | Motorcycle. :-()-: Got it. | entrapment | 1999 |
| 47230.0 | What? :-()-: A billion dollars. | entrapment | 1999 |
| 47244.0 | Oh my god, I thought-- :-()-: I'd taken the Mask? | entrapment | 1999 |
| 47259.0 | No way to take another day or two? :-()-: No. You'll see when we get inside. | entrapment | 1999 |
| 47212.0 | To us. To the Mask. :-()-: To our...partnership. | entrapment | 1999 |
| 47231.0 | Your share. :-()-: Get the hell in. | entrapment | 1999 |
| 47243.0 | Do you know what you've done!? Do you know? :-()-: Would you quiet down! Just for a minute? | entrapment | 1999 |
| 47304.0 | You'll just have to have faith. :-()-: Faith is angels dancing on the head of a pin. I got to have trust. :-()-: She won't tell me everything. It's a bank job, that's all I know. | entrapment | 1999 |
| 47158.0 | We going somewhere? :-()-: Possibly. :-()-: Maybe I should drive this time. :-()-: Maybe you should go buy yourself some clothes. | entrapment | 1999 |
| 47255.0 | How do I look? :-()-: Like a woman of mystery. | entrapment | 1999 |
| 47194.0 | Don't worry, I can get rid of this. No trace. And I'll even go fifty fifty, we're partners aren't we? :-()-: No. It's a down payment. | entrapment | 1999 |
| 47153.0 | For God's sake. :-()-: I'm sorry. This just means so much to me. | entrapment | 1999 |
| 47221.0 | Can you see the other PIRs? :-()-: Got it. | entrapment | 1999 |
| 47249.0 | Peoples China Bank. :-()-: Where the money is. | entrapment | 1999 |
| 47300.0 | Tonight your league night? :-()-: Check out my ball. | entrapment | 1999 |
| 47258.0 | Done. And I assume you have the magic CD- ROM? :-()-: Surgically attached. | entrapment | 1999 |
| 47216.0 | Camera in the-- :-()-: Bookshelf. Sensors-- :-()-: Popup, on floorboards. And-- :-()-: In eye of that painting. | entrapment | 1999 |
| 47292.0 | Let me ask you, this Mask, when they made it--was the old bitch dead or alive? :-()-: It's a death mask. Death mask means dead. | entrapment | 1999 |
| 47281.0 | Take the airport subway, but change at Jordan Station for Kowloon Tong. :-()-: But-- :-()-: It's only ninety seconds up the line. You're on a connection to a trans Siberian express. :-()-: What about you? | entrapment | 1999 |
| 47142.0 | You disappeared. :-()-: You seemed to be handling everything quite nicely. :-()-: Are you...okay? :-()-: It was only a scratch. Far more damaging to my trousers than to me. :-()-: That's good. Terrific. | entrapment | 1999 |
| 47125.0 | Meaning-- :-()-: Meaning no one arranges calalilies like that. He left the window open when he came in. His only mistake. | entrapment | 1999 |
| 47295.0 | Oh, it is. :-()-: It better be worth it for me. | entrapment | 1999 |
| 47165.0 | I'm telling you it's a forgery. The paint's still wet for God's sake. :-()-: Look on the back. What do you see? | entrapment | 1999 |
| 47136.0 | How about if I try humility. :-()-: How about if you try disappearing. | entrapment | 1999 |
| 47152.0 | Are you under the impression that now I'm in some way obligated to you? :-()-: Well, no...but... :-()-: Good. I'll call the concierge. They can get you a new room, book your flight home, so forth. | entrapment | 1999 |
| 47217.0 | We're working. :-()-: It's a party. :-()-: Let's mingle a little, shall we? | entrapment | 1999 |
| 47272.0 | Don't panic, now, there's no rush... :-()-: We can't leave it IN THERE, it's got all our accounts, everything that can NAIL us to a goddam CROSS!!! | entrapment | 1999 |
| 47195.0 | On what? Another job? :-()-: We get the Mask I'll tell you. :-()-: A partner with secrets isn't much of a partner. :-()-: Without the Mask it doesn't matter. :-()-: So the Mask is part of the down payment too. Must be a really big job. :-()-: Let's just see how we do. | entrapment | 1999 |
| 47235.0 | No, really, it's your plan, you should get at least 30 per cent-- :-()-: My eighty, your twenty, smart guy-- :-()-: It's fifty-fifty, less the cost of the dress. | entrapment | 1999 |
| 47223.0 | Be careful not to break the laser beams. :-()-: Duh. | entrapment | 1999 |
We will use the convenient Feature extraction and transformation APIs.
Step 3. Text Tokenization
We will use the RegexTokenizer to split each document into tokens. We can setMinTokenLength() here to indicate a minimum token length, and filter away all tokens that fall below the minimum. See:
import org.apache.spark.ml.feature.RegexTokenizer
// Set params for RegexTokenizer
val tokenizer = new RegexTokenizer()
.setPattern("[\\W_]+") // break by white space character(s)
.setMinTokenLength(4) // Filter away tokens with length < 4
.setInputCol("corpus") // name of the input column
.setOutputCol("tokens") // name of the output column
// Tokenize document
val tokenized_df = tokenizer.transform(corpusDF)
import org.apache.spark.ml.feature.RegexTokenizer
tokenizer: org.apache.spark.ml.feature.RegexTokenizer = regexTok_5380a11bc0d5
tokenized_df: org.apache.spark.sql.DataFrame = [id: bigint, corpus: string ... 3 more fields]
display(tokenized_df.sample(false,0.001,1234L))
display(tokenized_df.sample(false,0.001,123L).select("tokens"))
Step 4. Remove Stopwords
We can easily remove stopwords using the StopWordsRemover(). See:
If a list of stopwords is not provided, the StopWordsRemover() will use this list of stopwords, also shown below, by default.
are,around,as,at,back,be,became,because,become,becomes,becoming,been,before,beforehand,behind,being,below,beside,besides,between,beyond,bill,both,bottom,but,by,call,can,cannot,cant,co,computer,con,could,
couldnt,cry,de,describe,detail,do,done,down,due,during,each,eg,eight,either,eleven,else,elsewhere,empty,enough,etc,even,ever,every,everyone,everything,everywhere,except,few,fifteen,fify,fill,find,fire,first,
five,for,former,formerly,forty,found,four,from,front,full,further,get,give,go,had,has,hasnt,have,he,hence,her,here,hereafter,hereby,herein,hereupon,hers,herself,him,himself,his,how,however,hundred,i,ie,if,
in,inc,indeed,interest,into,is,it,its,itself,keep,last,latter,latterly,least,less,ltd,made,many,may,me,meanwhile,might,mill,mine,more,moreover,most,mostly,move,much,must,my,myself,name,namely,neither,never,
nevertheless,next,nine,no,nobody,none,noone,nor,not,nothing,now,nowhere,of,off,often,on,once,one,only,onto,or,other,others,otherwise,our,ours,ourselves,out,over,own,part,per,perhaps,please,put,rather,re,same,
see,seem,seemed,seeming,seems,serious,several,she,should,show,side,since,sincere,six,sixty,so,some,somehow,someone,something,sometime,sometimes,somewhere,still,such,system,take,ten,than,that,the,their,them,
themselves,then,thence,there,thereafter,thereby,therefore,therein,thereupon,these,they,thick,thin,third,this,those,though,three,through,throughout,thru,thus,to,together,too,top,toward,towards,twelve,twenty,two,
un,under,until,up,upon,us,very,via,was,we,well,were,what,whatever,when,whence,whenever,where,whereafter,whereas,whereby,wherein,whereupon,wherever,whether,which,while,whither,who,whoever,whole,whom,whose,why,will,
with,within,without,would,yet,you,your,yours,yourself,yourselves
You can use getStopWords() to see the list of stopwords that will be used.
In this example, we will specify a list of stopwords for the StopWordsRemover() to use. We do this so that we can add on to the list later on.
display(dbutils.fs.ls("dbfs:/tmp/stopwords")) // check if the file already exists from earlier wget and dbfs-load
| path | name | size |
|---|---|---|
| dbfs:/tmp/stopwords | stopwords | 2237.0 |
If the file dbfs:/tmp/stopwords already exists then skip the next two cells, otherwise download and load it into DBFS by uncommenting and evaluating the next two cells.
wget http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words -O /tmp/stopwords # uncomment '//' at the beginning and repeat only if needed again
--2019-05-31 08:23:58-- http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words
Resolving ir.dcs.gla.ac.uk (ir.dcs.gla.ac.uk)... 130.209.240.253
Connecting to ir.dcs.gla.ac.uk (ir.dcs.gla.ac.uk)|130.209.240.253|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 2237 (2.2K)
Saving to: ‘/tmp/stopwords’
0K .. 100% 320M=0s
2019-05-31 08:23:59 (320 MB/s) - ‘/tmp/stopwords’ saved [2237/2237]
cp file:/tmp/stopwords dbfs:/tmp/stopwords
res41: Boolean = true
// List of stopwords
val stopwords = sc.textFile("/tmp/stopwords").collect()
stopwords: Array[String] = Array(a, about, above, across, after, afterwards, again, against, all, almost, alone, along, already, also, although, always, am, among, amongst, amoungst, amount, an, and, another, any, anyhow, anyone, anything, anyway, anywhere, are, around, as, at, back, be, became, because, become, becomes, becoming, been, before, beforehand, behind, being, below, beside, besides, between, beyond, bill, both, bottom, but, by, call, can, cannot, cant, co, computer, con, could, couldnt, cry, de, describe, detail, do, done, down, due, during, each, eg, eight, either, eleven, else, elsewhere, empty, enough, etc, even, ever, every, everyone, everything, everywhere, except, few, fifteen, fify, fill, find, fire, first, five, for, former, formerly, forty, found, four, from, front, full, further, get, give, go, had, has, hasnt, have, he, hence, her, here, hereafter, hereby, herein, hereupon, hers, herself, him, himself, his, how, however, hundred, i, ie, if, in, inc, indeed, interest, into, is, it, its, itself, keep, last, latter, latterly, least, less, ltd, made, many, may, me, meanwhile, might, mill, mine, more, moreover, most, mostly, move, much, must, my, myself, name, namely, neither, never, nevertheless, next, nine, no, nobody, none, noone, nor, not, nothing, now, nowhere, of, off, often, on, once, one, only, onto, or, other, others, otherwise, our, ours, ourselves, out, over, own, part, per, perhaps, please, put, rather, re, same, see, seem, seemed, seeming, seems, serious, several, she, should, show, side, since, sincere, six, sixty, so, some, somehow, someone, something, sometime, sometimes, somewhere, still, such, system, take, ten, than, that, the, their, them, themselves, then, thence, there, thereafter, thereby, therefore, therein, thereupon, these, they, thick, thin, third, this, those, though, three, through, throughout, thru, thus, to, together, too, top, toward, towards, twelve, twenty, two, un, under, until, up, upon, us, very, via, was, we, well, were, what, whatever, when, whence, whenever, where, whereafter, whereas, whereby, wherein, whereupon, wherever, whether, which, while, whither, who, whoever, whole, whom, whose, why, will, with, within, without, would, yet, you, your, yours, yourself, yourselves)
stopwords.length // find the number of stopwords in the scala Array[String]
res35: Int = 319
Finally, we can just remove the stopwords using the StopWordsRemover as follows:
import org.apache.spark.ml.feature.StopWordsRemover
// Set params for StopWordsRemover
val remover = new StopWordsRemover()
.setStopWords(stopwords) // This parameter is optional
.setInputCol("tokens")
.setOutputCol("filtered")
// Create new DF with Stopwords removed
val filtered_df = remover.transform(tokenized_df)
import org.apache.spark.ml.feature.StopWordsRemover
remover: org.apache.spark.ml.feature.StopWordsRemover = stopWords_294e3228eba8
filtered_df: org.apache.spark.sql.DataFrame = [id: bigint, corpus: string ... 4 more fields]
Step 5. Vector of Token Counts
LDA takes in a vector of token counts as input. We can use the CountVectorizer() to easily convert our text documents into vectors of token counts.
The CountVectorizer will return (VocabSize, Array(Indexed Tokens), Array(Token Frequency)).
Two handy parameters to note:
setMinDF: Specifies the minimum number of different documents a term must appear in to be included in the vocabulary.setMinTF: Specifies the minimum number of times a term has to appear in a document to be included in the vocabulary.
See:
import org.apache.spark.ml.feature.CountVectorizer
// Set params for CountVectorizer
val vectorizer = new CountVectorizer()
.setInputCol("filtered")
.setOutputCol("features")
.setVocabSize(10000)
.setMinDF(5) // the minimum number of different documents a term must appear in to be included in the vocabulary.
.fit(filtered_df)
import org.apache.spark.ml.feature.CountVectorizer
vectorizer: org.apache.spark.ml.feature.CountVectorizerModel = cntVec_48267a85f1b9
// Create vector of token counts
val countVectors = vectorizer.transform(filtered_df).select("id", "features")
countVectors: org.apache.spark.sql.DataFrame = [id: bigint, features: vector]
// see the first countVectors
countVectors.take(1)
res38: Array[org.apache.spark.sql.Row] = Array([28762,(10000,[7,112,179,308],[1.0,1.0,1.0,1.0])])
To use the LDA algorithm in the MLlib library, we have to convert the DataFrame back into an RDD.
// Convert DF to RDD - ideally we should use ml for everything an not ml and mllib ; DAN
import org.apache.spark.ml.feature.{CountVectorizer, RegexTokenizer, StopWordsRemover}
import org.apache.spark.ml.linalg.{Vector => MLVector}
import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer}
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.sql.{Row, SparkSession}
val lda_countVector = countVectors.map { case Row(id: Long, countVector: MLVector) => (id, Vectors.fromML(countVector)) }.rdd
import org.apache.spark.ml.feature.{CountVectorizer, RegexTokenizer, StopWordsRemover}
import org.apache.spark.ml.linalg.{Vector=>MLVector}
import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer}
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.sql.{Row, SparkSession}
lda_countVector: org.apache.spark.rdd.RDD[(Long, org.apache.spark.mllib.linalg.Vector)] = MapPartitionsRDD[3912] at rdd at command-753740454082286:11
// format: Array(id, (VocabSize, Array(indexedTokens), Array(Token Frequency)))
lda_countVector.take(1)
res42: Array[(Long, org.apache.spark.mllib.linalg.Vector)] = Array((28762,(10000,[7,112,179,308],[1.0,1.0,1.0,1.0])))
Create LDA model with Online Variational Bayes
We will now set the parameters for LDA. We will use the OnlineLDAOptimizer() here, which implements Online Variational Bayes.
Choosing the number of topics for your LDA model requires a bit of domain knowledge. As we do not know the number of "topics", we will set numTopics to be 20.
val numTopics = 20
numTopics: Int = 20
We will set the parameters needed to build our LDA model. We can also setMiniBatchFraction for the OnlineLDAOptimizer, which sets the fraction of corpus sampled and used at each iteration. In this example, we will set this to 0.8.
import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer}
// Set LDA params
val lda = new LDA()
.setOptimizer(new OnlineLDAOptimizer().setMiniBatchFraction(0.8))
.setK(numTopics)
.setMaxIterations(3)
.setDocConcentration(-1) // use default values
.setTopicConcentration(-1) // use default values
import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer}
lda: org.apache.spark.mllib.clustering.LDA = org.apache.spark.mllib.clustering.LDA@3c173c8
Create the LDA model with Online Variational Bayes.
val ldaModel = lda.run(lda_countVector)
ldaModel: org.apache.spark.mllib.clustering.LDAModel = org.apache.spark.mllib.clustering.LocalLDAModel@5bf00930
Watch Online Learning for Latent Dirichlet Allocation in NIPS2010 by Matt Hoffman (right click and open in new tab)
![Matt Hoffman's NIPS 2010 Talk Online LDA]](http://videolectures.net/nips2010hoffmanoll/)
Also see the paper on Online varioational Bayes by Matt linked for more details (from the above URL): http://videolectures.net/site/normaldl/tag=83534/nips20101291.pdf
Note that using the OnlineLDAOptimizer returns us a LocalLDAModel, which stores the inferred topics of your corpus.
Review Topics
We can now review the results of our LDA model. We will print out all 20 topics with their corresponding term probabilities.
Note that you will get slightly different results every time you run an LDA model since LDA includes some randomization.
Let us review results of LDA model with Online Variational Bayes, step by step.
val topicIndices = ldaModel.describeTopics(maxTermsPerTopic = 5)
topicIndices: Array[(Array[Int], Array[Double])] = Array((Array(1, 2, 4, 49, 0),Array(0.0014102155338741765, 0.0012758924372910556, 0.0011214448310395873, 9.238914780871355E-4, 9.047647243869576E-4)), (Array(1, 6, 2, 0, 4),Array(0.0014443699497685366, 0.0012377629724506722, 0.0011714257476524842, 0.0010861657304183027, 8.604460434628813E-4)), (Array(1, 2, 8, 0, 3),Array(0.0014926060533533697, 0.0013429026076916017, 0.0013067364965238173, 0.0011607492289313303, 0.0011400804862230437)), (Array(5, 6, 4, 1, 7),Array(0.006717314446949222, 0.006002662754297925, 0.004488111770001314, 0.004408679383982238, 0.0042465917238892655)), (Array(0, 19, 3, 8, 6),Array(0.0050059173813691085, 0.0029731088780905225, 0.0022359962463711185, 0.002193246256785973, 0.0019111384839030116)), (Array(3, 0, 10, 1, 15),Array(0.003714410612506209, 0.0017122806517390608, 0.0017073041827440282, 0.0015712232707115927, 0.0012303967042097022)), (Array(0, 1, 6, 10, 2),Array(0.00467483294478972, 0.0038641828467113268, 0.003328578440542597, 0.002867941043688811, 0.002532629878316373)), (Array(0, 2, 9, 1, 13),Array(0.00960017865043255, 0.009308573745541343, 0.005704969701604644, 0.004085042285865179, 0.004031048471919761)), (Array(0, 4, 5, 77, 16),Array(0.004550808496981245, 0.004122146617438838, 0.0019092043643137734, 0.0018255598181846045, 0.001761167250972209)), (Array(6, 2, 5, 1, 0),Array(0.0016782125889211463, 0.0012427279906039904, 0.0012197157251243875, 0.0010635502545983016, 9.50137528050953E-4)), (Array(2, 1, 3, 0, 6),Array(0.003126597598330109, 0.0027451035751362273, 0.00228759303132256, 0.0017239166326848171, 0.0017047784964894794)), (Array(2, 1, 27, 4, 3),Array(0.004734133576359814, 0.004201386287998202, 0.0036983083453854372, 0.0025414887712607768, 0.002091795015523375)), (Array(0, 5, 1, 12, 2),Array(0.0035340054254694784, 0.002387182752907053, 0.0019263993964325303, 0.001843992584617911, 0.0018065489773133325)), (Array(2, 1, 5, 14, 0),Array(0.0016017017354850733, 0.0014834097260266685, 0.0014300356385979168, 0.001294952229819751, 0.0012788947989035501)), (Array(7, 1, 10, 6, 2),Array(0.002043769246809558, 0.0013757478946969802, 0.0013208455540129331, 0.0012662647575091633, 0.0011549537488969965)), (Array(0, 1, 2, 3, 4),Array(0.022087503347588935, 0.01571524947937798, 0.012895996754133662, 0.01026452087962411, 0.009873743305368164)), (Array(0, 1, 3, 4, 9),Array(0.002204551343207476, 0.0016283414468010306, 0.0014214537687803855, 0.0012768751041210551, 0.0011525954268574248)), (Array(46, 1, 2, 16, 5),Array(0.0022031979750387655, 0.0020637622110226085, 0.0019281346187348387, 0.0015712770524161123, 0.0014183600893726285)), (Array(0, 2, 3, 5, 8),Array(0.0035729889283848504, 0.0024215014894025766, 0.0018740761967851508, 0.001838630576321126, 0.0016262171049684524)), (Array(3, 10, 30, 9, 4),Array(0.0018098267577494882, 0.0015864305565599366, 0.0015861983258874525, 0.001331260635860306, 0.0012793651558771885)))
val vocabList = vectorizer.vocabulary
vocabList: Array[String] = Array(know, just, like, want, think, right, going, good, yeah, tell, come, time, look, didn, mean, make, okay, really, little, sure, gonna, thing, people, said, maybe, need, sorry, love, talk, thought, doing, life, night, things, work, money, better, told, long, help, believe, years, shit, does, away, place, hell, doesn, great, home, feel, fuck, kind, remember, dead, course, wouldn, wait, kill, guess, understand, thank, girl, wrong, leave, listen, talking, real, stop, hear, nice, happened, fine, wanted, father, gotta, mind, fucking, house, wasn, getting, world, stay, mother, left, came, care, thanks, knew, room, trying, guys, went, looking, coming, heard, friend, haven, seen, best, tonight, live, used, matter, killed, pretty, business, idea, couldn, head, miss, says, wife, called, woman, morning, tomorrow, start, stuff, saying, play, hello, baby, hard, probably, minute, days, took, somebody, today, school, meet, gone, crazy, wants, damn, forget, cause, problem, deal, case, friends, point, hope, jesus, afraid, looks, knows, year, worry, exactly, aren, half, thinking, shut, hold, wanna, face, minutes, bring, read, word, doctor, everybody, makes, supposed, story, turn, true, watch, thousand, family, brother, kids, week, happen, fuckin, working, open, happy, lost, john, hurt, town, ready, alright, late, actually, married, gave, beautiful, soon, jack, times, sleep, door, having, hand, drink, easy, gets, chance, young, trouble, different, anybody, shot, rest, hate, death, second, later, asked, phone, wish, check, quite, change, police, walk, couple, question, close, taking, heart, hours, making, comes, anymore, truth, trust, dollars, important, captain, telling, funny, person, honey, goes, eyes, inside, reason, stand, break, means, number, tried, high, white, water, suppose, body, sick, game, excuse, party, women, country, waiting, christ, answer, office, send, pick, alive, sort, blood, black, daddy, line, husband, goddamn, book, fifty, thirty, fact, million, hands, died, power, stupid, started, shouldn, months, city, boys, dinner, sense, running, hour, shoot, drive, fight, speak, george, ship, living, figure, dear, street, ahead, lady, seven, free, feeling, scared, frank, able, children, outside, moment, safe, news, president, brought, write, happens, sent, bullshit, lose, light, glad, child, girls, sounds, sister, lives, promise, till, sound, weren, save, poor, cool, asking, shall, plan, bitch, king, daughter, weeks, beat, york, cold, worth, taken, harry, needs, piece, movie, fast, possible, small, goin, straight, human, hair, tired, food, company, lucky, pull, wonderful, touch, state, looked, thinks, picture, leaving, words, control, clear, known, special, buddy, luck, order, follow, expect, mary, catch, mouth, worked, mister, learn, playing, perfect, dream, calling, questions, hospital, takes, ride, coffee, miles, parents, works, secret, explain, hotel, worse, kidding, past, outta, general, unless, felt, drop, throw, interested, hang, certainly, absolutely, earth, loved, wonder, dark, accident, seeing, simple, turned, doin, clock, date, sweet, meeting, clean, sign, feet, handle, army, music, giving, report, cops, fucked, charlie, information, smart, yesterday, fall, fault, class, bank, month, blow, major, caught, swear, paul, road, talked, choice, boss, plane, david, paid, wear, american, worried, clothes, paper, goodbye, lord, ones, strange, terrible, mistake, given, hurry, blue, finish, murder, kept, apartment, sell, middle, nothin, hasn, careful, meant, walter, moving, changed, imagine, fair, difference, quiet, happening, near, quit, personal, marry, future, figured, rose, agent, kinda, michael, building, mama, early, private, trip, watching, busy, record, certain, jimmy, broke, longer, sake, store, stick, finally, boat, born, sitting, evening, bucks, chief, history, ought, lying, kiss, honor, lunch, darling, favor, fool, uncle, respect, rich, liked, killing, land, peter, tough, interesting, brain, problems, nick, welcome, completely, dick, honest, wake, radio, cash, dude, dance, james, bout, floor, weird, court, calls, jail, window, involved, drunk, johnny, officer, needed, asshole, books, spend, situation, relax, pain, service, dangerous, grand, security, letter, stopped, realize, table, offer, bastard, message, instead, killer, jake, nervous, deep, pass, somethin, evil, english, bought, short, ring, step, picked, likes, voice, eddie, machine, lived, upset, forgot, carry, afternoon, fear, quick, finished, count, forgive, wrote, named, decided, totally, space, team, doubt, pleasure, lawyer, suit, station, gotten, bother, prove, return, pictures, slow, bunch, strong, wearing, driving, list, join, christmas, tape, attack, church, appreciate, force, hungry, standing, college, dying, present, charge, prison, missing, truck, public, board, calm, staying, gold, ball, hardly, hadn, lead, missed, island, government, horse, cover, reach, french, joke, star, fish, mike, moved, america, surprise, soul, seconds, club, self, movies, putting, dress, cost, listening, lots, price, saved, smell, mark, peace, gives, crime, dreams, entire, single, usually, department, beer, holy, west, wall, stuck, nose, protect, ways, teach, awful, forever, type, grow, train, detective, billy, rock, planet, walking, beginning, dumb, papers, folks, park, attention, hide, card, birthday, reading, test, share, master, lieutenant, starting, field, partner, twice, enjoy, dollar, blame, film, mess, bomb, round, girlfriend, south, loves, plenty, using, gentlemen, especially, records, evidence, experience, silly, admit, normal, fired, talkin, lock, louis, fighting, mission, notice, memory, promised, crap, wedding, orders, ground, guns, glass, marriage, idiot, heaven, impossible, knock, green, wondering, spent, animal, hole, neck, drugs, press, nuts, names, broken, position, asleep, jerry, visit, boyfriend, acting, plans, feels, tells, paris, smoke, wind, sheriff, cross, holding, gimme, mention, walked, judge, code, double, brothers, writing, pardon, keeps, fellow, fell, closed, angry, lovely, cute, surprised, percent, charles, correct, agree, bathroom, address, andy, ridiculous, summer, tommy, rules, note, account, group, sleeping, learned, sing, pulled, colonel, proud, laugh, river, area, upstairs, jump, built, difficult, breakfast, bobby, bridge, dirty, betty, amazing, locked, north, definitely, alex, feelings, plus, worst, accept, kick, file, wild, seriously, grace, stories, steal, gettin, nature, advice, relationship, contact, waste, places, spot, beach, stole, apart, favorite, knowing, level, song, faith, risk, loose, patient, foot, eating, played, action, witness, washington, turns, build, obviously, begin, split, games, command, crew, decide, nurse, keeping, tight, bird, form, runs, copy, arrest, complete, scene, consider, jeffrey, insane, taste, teeth, shoes, monster, devil, henry, career, sooner, innocent, hall, showed, gift, weekend, heavy, study, greatest, comin, danger, keys, raise, destroy, track, carl, california, concerned, bruce, program, blind, suddenly, hanging, apologize, seventy, chicken, medical, forward, drinking, sweetheart, willing, guard, legs, admiral, shop, professor, suspect, tree, camp, data, ticket, goodnight, possibly, dunno, burn, paying, television, trick, murdered, losing, senator, credit, extra, dropped, sold, warm, meaning, stone, starts, hiding, lately, cheap, marty, taught, science, lookin, simply, majesty, harold, corner, jeff, queen, following, duty, training, seat, heads, cars, discuss, bear, noticed, enemy, helped, screw, richard, flight)
val topics = topicIndices.map { case (terms, termWeights) =>
terms.map(vocabList(_)).zip(termWeights)
}
topics: Array[Array[(String, Double)]] = Array(Array((just,0.0014102155338741765), (like,0.0012758924372910556), (think,0.0011214448310395873), (home,9.238914780871355E-4), (know,9.047647243869576E-4)), Array((just,0.0014443699497685366), (going,0.0012377629724506722), (like,0.0011714257476524842), (know,0.0010861657304183027), (think,8.604460434628813E-4)), Array((just,0.0014926060533533697), (like,0.0013429026076916017), (yeah,0.0013067364965238173), (know,0.0011607492289313303), (want,0.0011400804862230437)), Array((right,0.006717314446949222), (going,0.006002662754297925), (think,0.004488111770001314), (just,0.004408679383982238), (good,0.0042465917238892655)), Array((know,0.0050059173813691085), (sure,0.0029731088780905225), (want,0.0022359962463711185), (yeah,0.002193246256785973), (going,0.0019111384839030116)), Array((want,0.003714410612506209), (know,0.0017122806517390608), (come,0.0017073041827440282), (just,0.0015712232707115927), (make,0.0012303967042097022)), Array((know,0.00467483294478972), (just,0.0038641828467113268), (going,0.003328578440542597), (come,0.002867941043688811), (like,0.002532629878316373)), Array((know,0.00960017865043255), (like,0.009308573745541343), (tell,0.005704969701604644), (just,0.004085042285865179), (didn,0.004031048471919761)), Array((know,0.004550808496981245), (think,0.004122146617438838), (right,0.0019092043643137734), (fucking,0.0018255598181846045), (okay,0.001761167250972209)), Array((going,0.0016782125889211463), (like,0.0012427279906039904), (right,0.0012197157251243875), (just,0.0010635502545983016), (know,9.50137528050953E-4)), Array((like,0.003126597598330109), (just,0.0027451035751362273), (want,0.00228759303132256), (know,0.0017239166326848171), (going,0.0017047784964894794)), Array((like,0.004734133576359814), (just,0.004201386287998202), (love,0.0036983083453854372), (think,0.0025414887712607768), (want,0.002091795015523375)), Array((know,0.0035340054254694784), (right,0.002387182752907053), (just,0.0019263993964325303), (look,0.001843992584617911), (like,0.0018065489773133325)), Array((like,0.0016017017354850733), (just,0.0014834097260266685), (right,0.0014300356385979168), (mean,0.001294952229819751), (know,0.0012788947989035501)), Array((good,0.002043769246809558), (just,0.0013757478946969802), (come,0.0013208455540129331), (going,0.0012662647575091633), (like,0.0011549537488969965)), Array((know,0.022087503347588935), (just,0.01571524947937798), (like,0.012895996754133662), (want,0.01026452087962411), (think,0.009873743305368164)), Array((know,0.002204551343207476), (just,0.0016283414468010306), (want,0.0014214537687803855), (think,0.0012768751041210551), (tell,0.0011525954268574248)), Array((hell,0.0022031979750387655), (just,0.0020637622110226085), (like,0.0019281346187348387), (okay,0.0015712770524161123), (right,0.0014183600893726285)), Array((know,0.0035729889283848504), (like,0.0024215014894025766), (want,0.0018740761967851508), (right,0.001838630576321126), (yeah,0.0016262171049684524)), Array((want,0.0018098267577494882), (come,0.0015864305565599366), (doing,0.0015861983258874525), (tell,0.001331260635860306), (think,0.0012793651558771885)))
Feel free to take things apart to understand!
topicIndices(0)
res43: (Array[Int], Array[Double]) = (Array(1, 2, 4, 49, 0),Array(0.0014102155338741765, 0.0012758924372910556, 0.0011214448310395873, 9.238914780871355E-4, 9.047647243869576E-4))
topicIndices(0)._1
res44: Array[Int] = Array(1, 2, 4, 49, 0)
topicIndices(0)._1(0)
res45: Int = 1
vocabList(topicIndices(0)._1(0))
res46: String = just
Review Results of LDA model with Online Variational Bayes - Doing all four steps earlier at once.
val topicIndices = ldaModel.describeTopics(maxTermsPerTopic = 5)
val vocabList = vectorizer.vocabulary
val topics = topicIndices.map { case (terms, termWeights) =>
terms.map(vocabList(_)).zip(termWeights)
}
println(s"$numTopics topics:")
topics.zipWithIndex.foreach { case (topic, i) =>
println(s"TOPIC $i")
topic.foreach { case (term, weight) => println(s"$term\t$weight") }
println(s"==========")
}
20 topics:
TOPIC 0
just 0.0014102155338741765
like 0.0012758924372910556
think 0.0011214448310395873
home 9.238914780871355E-4
know 9.047647243869576E-4
==========
TOPIC 1
just 0.0014443699497685366
going 0.0012377629724506722
like 0.0011714257476524842
know 0.0010861657304183027
think 8.604460434628813E-4
==========
TOPIC 2
just 0.0014926060533533697
like 0.0013429026076916017
yeah 0.0013067364965238173
know 0.0011607492289313303
want 0.0011400804862230437
==========
TOPIC 3
right 0.006717314446949222
going 0.006002662754297925
think 0.004488111770001314
just 0.004408679383982238
good 0.0042465917238892655
==========
TOPIC 4
know 0.0050059173813691085
sure 0.0029731088780905225
want 0.0022359962463711185
yeah 0.002193246256785973
going 0.0019111384839030116
==========
TOPIC 5
want 0.003714410612506209
know 0.0017122806517390608
come 0.0017073041827440282
just 0.0015712232707115927
make 0.0012303967042097022
==========
TOPIC 6
know 0.00467483294478972
just 0.0038641828467113268
going 0.003328578440542597
come 0.002867941043688811
like 0.002532629878316373
==========
TOPIC 7
know 0.00960017865043255
like 0.009308573745541343
tell 0.005704969701604644
just 0.004085042285865179
didn 0.004031048471919761
==========
TOPIC 8
know 0.004550808496981245
think 0.004122146617438838
right 0.0019092043643137734
fucking 0.0018255598181846045
okay 0.001761167250972209
==========
TOPIC 9
going 0.0016782125889211463
like 0.0012427279906039904
right 0.0012197157251243875
just 0.0010635502545983016
know 9.50137528050953E-4
==========
TOPIC 10
like 0.003126597598330109
just 0.0027451035751362273
want 0.00228759303132256
know 0.0017239166326848171
going 0.0017047784964894794
==========
TOPIC 11
like 0.004734133576359814
just 0.004201386287998202
love 0.0036983083453854372
think 0.0025414887712607768
want 0.002091795015523375
==========
TOPIC 12
know 0.0035340054254694784
right 0.002387182752907053
just 0.0019263993964325303
look 0.001843992584617911
like 0.0018065489773133325
==========
TOPIC 13
like 0.0016017017354850733
just 0.0014834097260266685
right 0.0014300356385979168
mean 0.001294952229819751
know 0.0012788947989035501
==========
TOPIC 14
good 0.002043769246809558
just 0.0013757478946969802
come 0.0013208455540129331
going 0.0012662647575091633
like 0.0011549537488969965
==========
TOPIC 15
know 0.022087503347588935
just 0.01571524947937798
like 0.012895996754133662
want 0.01026452087962411
think 0.009873743305368164
==========
TOPIC 16
know 0.002204551343207476
just 0.0016283414468010306
want 0.0014214537687803855
think 0.0012768751041210551
tell 0.0011525954268574248
==========
TOPIC 17
hell 0.0022031979750387655
just 0.0020637622110226085
like 0.0019281346187348387
okay 0.0015712770524161123
right 0.0014183600893726285
==========
TOPIC 18
know 0.0035729889283848504
like 0.0024215014894025766
want 0.0018740761967851508
right 0.001838630576321126
yeah 0.0016262171049684524
==========
TOPIC 19
want 0.0018098267577494882
come 0.0015864305565599366
doing 0.0015861983258874525
tell 0.001331260635860306
think 0.0012793651558771885
==========
topicIndices: Array[(Array[Int], Array[Double])] = Array((Array(1, 2, 4, 49, 0),Array(0.0014102155338741765, 0.0012758924372910556, 0.0011214448310395873, 9.238914780871355E-4, 9.047647243869576E-4)), (Array(1, 6, 2, 0, 4),Array(0.0014443699497685366, 0.0012377629724506722, 0.0011714257476524842, 0.0010861657304183027, 8.604460434628813E-4)), (Array(1, 2, 8, 0, 3),Array(0.0014926060533533697, 0.0013429026076916017, 0.0013067364965238173, 0.0011607492289313303, 0.0011400804862230437)), (Array(5, 6, 4, 1, 7),Array(0.006717314446949222, 0.006002662754297925, 0.004488111770001314, 0.004408679383982238, 0.0042465917238892655)), (Array(0, 19, 3, 8, 6),Array(0.0050059173813691085, 0.0029731088780905225, 0.0022359962463711185, 0.002193246256785973, 0.0019111384839030116)), (Array(3, 0, 10, 1, 15),Array(0.003714410612506209, 0.0017122806517390608, 0.0017073041827440282, 0.0015712232707115927, 0.0012303967042097022)), (Array(0, 1, 6, 10, 2),Array(0.00467483294478972, 0.0038641828467113268, 0.003328578440542597, 0.002867941043688811, 0.002532629878316373)), (Array(0, 2, 9, 1, 13),Array(0.00960017865043255, 0.009308573745541343, 0.005704969701604644, 0.004085042285865179, 0.004031048471919761)), (Array(0, 4, 5, 77, 16),Array(0.004550808496981245, 0.004122146617438838, 0.0019092043643137734, 0.0018255598181846045, 0.001761167250972209)), (Array(6, 2, 5, 1, 0),Array(0.0016782125889211463, 0.0012427279906039904, 0.0012197157251243875, 0.0010635502545983016, 9.50137528050953E-4)), (Array(2, 1, 3, 0, 6),Array(0.003126597598330109, 0.0027451035751362273, 0.00228759303132256, 0.0017239166326848171, 0.0017047784964894794)), (Array(2, 1, 27, 4, 3),Array(0.004734133576359814, 0.004201386287998202, 0.0036983083453854372, 0.0025414887712607768, 0.002091795015523375)), (Array(0, 5, 1, 12, 2),Array(0.0035340054254694784, 0.002387182752907053, 0.0019263993964325303, 0.001843992584617911, 0.0018065489773133325)), (Array(2, 1, 5, 14, 0),Array(0.0016017017354850733, 0.0014834097260266685, 0.0014300356385979168, 0.001294952229819751, 0.0012788947989035501)), (Array(7, 1, 10, 6, 2),Array(0.002043769246809558, 0.0013757478946969802, 0.0013208455540129331, 0.0012662647575091633, 0.0011549537488969965)), (Array(0, 1, 2, 3, 4),Array(0.022087503347588935, 0.01571524947937798, 0.012895996754133662, 0.01026452087962411, 0.009873743305368164)), (Array(0, 1, 3, 4, 9),Array(0.002204551343207476, 0.0016283414468010306, 0.0014214537687803855, 0.0012768751041210551, 0.0011525954268574248)), (Array(46, 1, 2, 16, 5),Array(0.0022031979750387655, 0.0020637622110226085, 0.0019281346187348387, 0.0015712770524161123, 0.0014183600893726285)), (Array(0, 2, 3, 5, 8),Array(0.0035729889283848504, 0.0024215014894025766, 0.0018740761967851508, 0.001838630576321126, 0.0016262171049684524)), (Array(3, 10, 30, 9, 4),Array(0.0018098267577494882, 0.0015864305565599366, 0.0015861983258874525, 0.001331260635860306, 0.0012793651558771885)))
vocabList: Array[String] = Array(know, just, like, want, think, right, going, good, yeah, tell, come, time, look, didn, mean, make, okay, really, little, sure, gonna, thing, people, said, maybe, need, sorry, love, talk, thought, doing, life, night, things, work, money, better, told, long, help, believe, years, shit, does, away, place, hell, doesn, great, home, feel, fuck, kind, remember, dead, course, wouldn, wait, kill, guess, understand, thank, girl, wrong, leave, listen, talking, real, stop, hear, nice, happened, fine, wanted, father, gotta, mind, fucking, house, wasn, getting, world, stay, mother, left, came, care, thanks, knew, room, trying, guys, went, looking, coming, heard, friend, haven, seen, best, tonight, live, used, matter, killed, pretty, business, idea, couldn, head, miss, says, wife, called, woman, morning, tomorrow, start, stuff, saying, play, hello, baby, hard, probably, minute, days, took, somebody, today, school, meet, gone, crazy, wants, damn, forget, cause, problem, deal, case, friends, point, hope, jesus, afraid, looks, knows, year, worry, exactly, aren, half, thinking, shut, hold, wanna, face, minutes, bring, read, word, doctor, everybody, makes, supposed, story, turn, true, watch, thousand, family, brother, kids, week, happen, fuckin, working, open, happy, lost, john, hurt, town, ready, alright, late, actually, married, gave, beautiful, soon, jack, times, sleep, door, having, hand, drink, easy, gets, chance, young, trouble, different, anybody, shot, rest, hate, death, second, later, asked, phone, wish, check, quite, change, police, walk, couple, question, close, taking, heart, hours, making, comes, anymore, truth, trust, dollars, important, captain, telling, funny, person, honey, goes, eyes, inside, reason, stand, break, means, number, tried, high, white, water, suppose, body, sick, game, excuse, party, women, country, waiting, christ, answer, office, send, pick, alive, sort, blood, black, daddy, line, husband, goddamn, book, fifty, thirty, fact, million, hands, died, power, stupid, started, shouldn, months, city, boys, dinner, sense, running, hour, shoot, drive, fight, speak, george, ship, living, figure, dear, street, ahead, lady, seven, free, feeling, scared, frank, able, children, outside, moment, safe, news, president, brought, write, happens, sent, bullshit, lose, light, glad, child, girls, sounds, sister, lives, promise, till, sound, weren, save, poor, cool, asking, shall, plan, bitch, king, daughter, weeks, beat, york, cold, worth, taken, harry, needs, piece, movie, fast, possible, small, goin, straight, human, hair, tired, food, company, lucky, pull, wonderful, touch, state, looked, thinks, picture, leaving, words, control, clear, known, special, buddy, luck, order, follow, expect, mary, catch, mouth, worked, mister, learn, playing, perfect, dream, calling, questions, hospital, takes, ride, coffee, miles, parents, works, secret, explain, hotel, worse, kidding, past, outta, general, unless, felt, drop, throw, interested, hang, certainly, absolutely, earth, loved, wonder, dark, accident, seeing, simple, turned, doin, clock, date, sweet, meeting, clean, sign, feet, handle, army, music, giving, report, cops, fucked, charlie, information, smart, yesterday, fall, fault, class, bank, month, blow, major, caught, swear, paul, road, talked, choice, boss, plane, david, paid, wear, american, worried, clothes, paper, goodbye, lord, ones, strange, terrible, mistake, given, hurry, blue, finish, murder, kept, apartment, sell, middle, nothin, hasn, careful, meant, walter, moving, changed, imagine, fair, difference, quiet, happening, near, quit, personal, marry, future, figured, rose, agent, kinda, michael, building, mama, early, private, trip, watching, busy, record, certain, jimmy, broke, longer, sake, store, stick, finally, boat, born, sitting, evening, bucks, chief, history, ought, lying, kiss, honor, lunch, darling, favor, fool, uncle, respect, rich, liked, killing, land, peter, tough, interesting, brain, problems, nick, welcome, completely, dick, honest, wake, radio, cash, dude, dance, james, bout, floor, weird, court, calls, jail, window, involved, drunk, johnny, officer, needed, asshole, books, spend, situation, relax, pain, service, dangerous, grand, security, letter, stopped, realize, table, offer, bastard, message, instead, killer, jake, nervous, deep, pass, somethin, evil, english, bought, short, ring, step, picked, likes, voice, eddie, machine, lived, upset, forgot, carry, afternoon, fear, quick, finished, count, forgive, wrote, named, decided, totally, space, team, doubt, pleasure, lawyer, suit, station, gotten, bother, prove, return, pictures, slow, bunch, strong, wearing, driving, list, join, christmas, tape, attack, church, appreciate, force, hungry, standing, college, dying, present, charge, prison, missing, truck, public, board, calm, staying, gold, ball, hardly, hadn, lead, missed, island, government, horse, cover, reach, french, joke, star, fish, mike, moved, america, surprise, soul, seconds, club, self, movies, putting, dress, cost, listening, lots, price, saved, smell, mark, peace, gives, crime, dreams, entire, single, usually, department, beer, holy, west, wall, stuck, nose, protect, ways, teach, awful, forever, type, grow, train, detective, billy, rock, planet, walking, beginning, dumb, papers, folks, park, attention, hide, card, birthday, reading, test, share, master, lieutenant, starting, field, partner, twice, enjoy, dollar, blame, film, mess, bomb, round, girlfriend, south, loves, plenty, using, gentlemen, especially, records, evidence, experience, silly, admit, normal, fired, talkin, lock, louis, fighting, mission, notice, memory, promised, crap, wedding, orders, ground, guns, glass, marriage, idiot, heaven, impossible, knock, green, wondering, spent, animal, hole, neck, drugs, press, nuts, names, broken, position, asleep, jerry, visit, boyfriend, acting, plans, feels, tells, paris, smoke, wind, sheriff, cross, holding, gimme, mention, walked, judge, code, double, brothers, writing, pardon, keeps, fellow, fell, closed, angry, lovely, cute, surprised, percent, charles, correct, agree, bathroom, address, andy, ridiculous, summer, tommy, rules, note, account, group, sleeping, learned, sing, pulled, colonel, proud, laugh, river, area, upstairs, jump, built, difficult, breakfast, bobby, bridge, dirty, betty, amazing, locked, north, definitely, alex, feelings, plus, worst, accept, kick, file, wild, seriously, grace, stories, steal, gettin, nature, advice, relationship, contact, waste, places, spot, beach, stole, apart, favorite, knowing, level, song, faith, risk, loose, patient, foot, eating, played, action, witness, washington, turns, build, obviously, begin, split, games, command, crew, decide, nurse, keeping, tight, bird, form, runs, copy, arrest, complete, scene, consider, jeffrey, insane, taste, teeth, shoes, monster, devil, henry, career, sooner, innocent, hall, showed, gift, weekend, heavy, study, greatest, comin, danger, keys, raise, destroy, track, carl, california, concerned, bruce, program, blind, suddenly, hanging, apologize, seventy, chicken, medical, forward, drinking, sweetheart, willing, guard, legs, admiral, shop, professor, suspect, tree, camp, data, ticket, goodnight, possibly, dunno, burn, paying, television, trick, murdered, losing, senator, credit, extra, dropped, sold, warm, meaning, stone, starts, hiding, lately, cheap, marty, taught, science, lookin, simply, majesty, harold, corner, jeff, queen, following, duty, training, seat, heads, cars, discuss, bear, noticed, enemy, helped, screw, richard, flight)
topics: Array[Array[(String, Double)]] = Array(Array((just,0.0014102155338741765), (like,0.0012758924372910556), (think,0.0011214448310395873), (home,9.238914780871355E-4), (know,9.047647243869576E-4)), Array((just,0.0014443699497685366), (going,0.0012377629724506722), (like,0.0011714257476524842), (know,0.0010861657304183027), (think,8.604460434628813E-4)), Array((just,0.0014926060533533697), (like,0.0013429026076916017), (yeah,0.0013067364965238173), (know,0.0011607492289313303), (want,0.0011400804862230437)), Array((right,0.006717314446949222), (going,0.006002662754297925), (think,0.004488111770001314), (just,0.004408679383982238), (good,0.0042465917238892655)), Array((know,0.0050059173813691085), (sure,0.0029731088780905225), (want,0.0022359962463711185), (yeah,0.002193246256785973), (going,0.0019111384839030116)), Array((want,0.003714410612506209), (know,0.0017122806517390608), (come,0.0017073041827440282), (just,0.0015712232707115927), (make,0.0012303967042097022)), Array((know,0.00467483294478972), (just,0.0038641828467113268), (going,0.003328578440542597), (come,0.002867941043688811), (like,0.002532629878316373)), Array((know,0.00960017865043255), (like,0.009308573745541343), (tell,0.005704969701604644), (just,0.004085042285865179), (didn,0.004031048471919761)), Array((know,0.004550808496981245), (think,0.004122146617438838), (right,0.0019092043643137734), (fucking,0.0018255598181846045), (okay,0.001761167250972209)), Array((going,0.0016782125889211463), (like,0.0012427279906039904), (right,0.0012197157251243875), (just,0.0010635502545983016), (know,9.50137528050953E-4)), Array((like,0.003126597598330109), (just,0.0027451035751362273), (want,0.00228759303132256), (know,0.0017239166326848171), (going,0.0017047784964894794)), Array((like,0.004734133576359814), (just,0.004201386287998202), (love,0.0036983083453854372), (think,0.0025414887712607768), (want,0.002091795015523375)), Array((know,0.0035340054254694784), (right,0.002387182752907053), (just,0.0019263993964325303), (look,0.001843992584617911), (like,0.0018065489773133325)), Array((like,0.0016017017354850733), (just,0.0014834097260266685), (right,0.0014300356385979168), (mean,0.001294952229819751), (know,0.0012788947989035501)), Array((good,0.002043769246809558), (just,0.0013757478946969802), (come,0.0013208455540129331), (going,0.0012662647575091633), (like,0.0011549537488969965)), Array((know,0.022087503347588935), (just,0.01571524947937798), (like,0.012895996754133662), (want,0.01026452087962411), (think,0.009873743305368164)), Array((know,0.002204551343207476), (just,0.0016283414468010306), (want,0.0014214537687803855), (think,0.0012768751041210551), (tell,0.0011525954268574248)), Array((hell,0.0022031979750387655), (just,0.0020637622110226085), (like,0.0019281346187348387), (okay,0.0015712770524161123), (right,0.0014183600893726285)), Array((know,0.0035729889283848504), (like,0.0024215014894025766), (want,0.0018740761967851508), (right,0.001838630576321126), (yeah,0.0016262171049684524)), Array((want,0.0018098267577494882), (come,0.0015864305565599366), (doing,0.0015861983258874525), (tell,0.001331260635860306), (think,0.0012793651558771885)))
Going through the results, you may notice that some of the topic words returned are actually stopwords that are specific to our dataset (for eg: "writes", "article"...). Let's try improving our model.
Step 8. Model Tuning - Refilter Stopwords
We will try to improve the results of our model by identifying some stopwords that are specific to our dataset. We will filter these stopwords out and rerun our LDA model to see if we get better results.
val add_stopwords = Array("whatever") // add more stop-words like the name of your company!
add_stopwords: Array[String] = Array(whatever)
// Combine newly identified stopwords to our exising list of stopwords
val new_stopwords = stopwords.union(add_stopwords)
new_stopwords: Array[String] = Array(a, about, above, across, after, afterwards, again, against, all, almost, alone, along, already, also, although, always, am, among, amongst, amoungst, amount, an, and, another, any, anyhow, anyone, anything, anyway, anywhere, are, around, as, at, back, be, became, because, become, becomes, becoming, been, before, beforehand, behind, being, below, beside, besides, between, beyond, bill, both, bottom, but, by, call, can, cannot, cant, co, computer, con, could, couldnt, cry, de, describe, detail, do, done, down, due, during, each, eg, eight, either, eleven, else, elsewhere, empty, enough, etc, even, ever, every, everyone, everything, everywhere, except, few, fifteen, fify, fill, find, fire, first, five, for, former, formerly, forty, found, four, from, front, full, further, get, give, go, had, has, hasnt, have, he, hence, her, here, hereafter, hereby, herein, hereupon, hers, herself, him, himself, his, how, however, hundred, i, ie, if, in, inc, indeed, interest, into, is, it, its, itself, keep, last, latter, latterly, least, less, ltd, made, many, may, me, meanwhile, might, mill, mine, more, moreover, most, mostly, move, much, must, my, myself, name, namely, neither, never, nevertheless, next, nine, no, nobody, none, noone, nor, not, nothing, now, nowhere, of, off, often, on, once, one, only, onto, or, other, others, otherwise, our, ours, ourselves, out, over, own, part, per, perhaps, please, put, rather, re, same, see, seem, seemed, seeming, seems, serious, several, she, should, show, side, since, sincere, six, sixty, so, some, somehow, someone, something, sometime, sometimes, somewhere, still, such, system, take, ten, than, that, the, their, them, themselves, then, thence, there, thereafter, thereby, therefore, therein, thereupon, these, they, thick, thin, third, this, those, though, three, through, throughout, thru, thus, to, together, too, top, toward, towards, twelve, twenty, two, un, under, until, up, upon, us, very, via, was, we, well, were, what, whatever, when, whence, whenever, where, whereafter, whereas, whereby, wherein, whereupon, wherever, whether, which, while, whither, who, whoever, whole, whom, whose, why, will, with, within, without, would, yet, you, your, yours, yourself, yourselves, whatever)
import org.apache.spark.ml.feature.StopWordsRemover
// Set Params for StopWordsRemover with new_stopwords
val remover = new StopWordsRemover()
.setStopWords(new_stopwords)
.setInputCol("tokens")
.setOutputCol("filtered")
// Create new df with new list of stopwords removed
val new_filtered_df = remover.transform(tokenized_df)
import org.apache.spark.ml.feature.StopWordsRemover
remover: org.apache.spark.ml.feature.StopWordsRemover = stopWords_3d7dc1a9b2ef
new_filtered_df: org.apache.spark.sql.DataFrame = [id: bigint, corpus: string ... 4 more fields]
// Set Params for CountVectorizer
val vectorizer = new CountVectorizer()
.setInputCol("filtered")
.setOutputCol("features")
.setVocabSize(10000)
.setMinDF(5)
.fit(new_filtered_df)
// Create new df of countVectors
val new_countVectors = vectorizer.transform(new_filtered_df).select("id", "features")
vectorizer: org.apache.spark.ml.feature.CountVectorizerModel = cntVec_2fcb7a8b0dc8
new_countVectors: org.apache.spark.sql.DataFrame = [id: bigint, features: vector]
// Convert DF to RDD
val new_lda_countVector = new_countVectors.map { case Row(id: Long, countVector: MLVector) => (id, Vectors.fromML(countVector)) }.rdd
new_lda_countVector: org.apache.spark.rdd.RDD[(Long, org.apache.spark.mllib.linalg.Vector)] = MapPartitionsRDD[3955] at rdd at command-753740454082314:2
We will also increase MaxIterations to 10 to see if we get better results.
// Set LDA parameters
val new_lda = new LDA()
.setOptimizer(new OnlineLDAOptimizer().setMiniBatchFraction(0.8))
.setK(numTopics)
.setMaxIterations(10)
.setDocConcentration(-1) // use default values
.setTopicConcentration(-1) // use default values
new_lda: org.apache.spark.mllib.clustering.LDA = org.apache.spark.mllib.clustering.LDA@5fca2e4f
How to find what the default values are?
Dive into the source!!!
- Let's find the default value for
docConcentrationnow. - Got to Apache Spark package Root: https://spark.apache.org/docs/latest/api/scala/#package
-
search for 'ml' in the search box on the top left (ml is for ml library)
-
Then find the
LDAby scrolling below on the left to mllib'sclusteringmethods and click onLDA -
Then click on the source code link which should take you here:
- https://github.com/apache/spark/blob/v1.6.1/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
- Now, simply go to the right function and see the following comment block:
/** * Concentration parameter (commonly named "alpha") for the prior placed on documents' * distributions over topics ("theta"). * * This is the parameter to a Dirichlet distribution, where larger values mean more smoothing * (more regularization). * * If not set by the user, then docConcentration is set automatically. If set to * singleton vector [alpha], then alpha is replicated to a vector of length k in fitting. * Otherwise, the [[docConcentration]] vector must be length k. * (default = automatic) * * Optimizer-specific parameter settings: * - EM * - Currently only supports symmetric distributions, so all values in the vector should be * the same. * - Values should be > 1.0 * - default = uniformly (50 / k) + 1, where 50/k is common in LDA libraries and +1 follows * from Asuncion et al. (2009), who recommend a +1 adjustment for EM. * - Online * - Values should be >= 0 * - default = uniformly (1.0 / k), following the implementation from * [[https://github.com/Blei-Lab/onlineldavb]]. * @group param */
HOMEWORK: Try to find the default value for TopicConcentration.
// Create LDA model with stopwords refiltered
val new_ldaModel = new_lda.run(new_lda_countVector)
new_ldaModel: org.apache.spark.mllib.clustering.LDAModel = org.apache.spark.mllib.clustering.LocalLDAModel@3f1301a7
val topicIndices = new_ldaModel.describeTopics(maxTermsPerTopic = 5)
val vocabList = vectorizer.vocabulary
val topics = topicIndices.map { case (terms, termWeights) =>
terms.map(vocabList(_)).zip(termWeights)
}
println(s"$numTopics topics:")
topics.zipWithIndex.foreach { case (topic, i) =>
println(s"TOPIC $i")
topic.foreach { case (term, weight) => println(s"$term\t$weight") }
println(s"==========")
}
20 topics:
TOPIC 0
right 0.002368539995607174
love 0.0019026093436816463
just 0.001739005396343051
okay 0.001493567868602809
know 0.0011919944841106388
==========
TOPIC 1
like 0.012255569993473736
just 0.007532527227834193
come 0.007114873840600518
know 0.006960825682483897
think 0.006460380113586568
==========
TOPIC 2
know 0.017593342399778864
yeah 0.01729763439457538
gonna 0.014297985209693677
just 0.009395640487800467
tell 0.007112117826339655
==========
TOPIC 3
just 0.002310885836348927
know 0.0020049203493508585
better 0.001839601963450054
like 0.0016545385663972387
right 0.001505081787498549
==========
TOPIC 4
know 0.012396058201765845
didn 0.004786910731106122
like 0.004783067030382327
right 0.003733205551673614
just 0.0028592628116592403
==========
TOPIC 5
just 0.0028236500929191208
know 0.0026011344347436015
going 0.0015951009390631876
didn 0.001385667983895007
wait 0.001275555813151892
==========
TOPIC 6
going 0.00275337137203844
right 0.001685679960504387
just 0.0015380845174617235
know 0.0014818062892167352
captain 0.0013896743515293423
==========
TOPIC 7
going 0.011956735401221285
just 0.006541063462593452
know 0.005428932374204778
think 0.004308569608730405
believe 0.003696595226603709
==========
TOPIC 8
think 0.0019959039820595533
sorry 0.00198077299794292
know 0.0016723315231586236
shit 0.0015606901977245095
right 0.0013015271817698212
==========
TOPIC 9
know 0.003615862714921936
said 0.001961114693915351
sorry 0.0018595382287745752
like 0.0017819242854891695
think 0.0016468683030306027
==========
TOPIC 10
time 0.008784671423019166
want 0.00282365356227211
sure 0.0024833597381016476
know 0.0019777615447230884
right 0.0016576304456760946
==========
TOPIC 11
just 0.0021068918389201634
like 0.0020497480766035994
know 0.002022347553873645
want 0.0019500819941038825
said 0.001503771370040063
==========
TOPIC 12
look 0.00433587608823225
think 0.0025833796049907604
know 0.002007970741805987
going 0.0016840410422251017
just 0.0010661551551733228
==========
TOPIC 13
know 0.0020279945673448915
come 0.0019980250335794405
think 0.0012733121858788797
going 0.001192108885417234
okay 0.001186180285931844
==========
TOPIC 14
like 0.004262090436242644
right 0.0021537790725358777
just 0.0013683197398457016
know 0.0010911699327713488
look 0.0010869000557749361
==========
TOPIC 15
come 0.004769396664496132
know 0.0026229974920448534
like 0.0021612642420959253
just 0.0013228057897488347
right 0.001171812635848879
==========
TOPIC 16
know 0.025323543461007635
just 0.018361261941348715
like 0.01574431601713426
want 0.014855701536091734
think 0.011957607420818889
==========
TOPIC 17
like 0.004346004035796333
know 0.0022903208899377127
just 0.002008680613491114
little 0.0019547134832950414
maybe 0.0017287784612649724
==========
TOPIC 18
know 0.003217184151682409
think 0.003063734585623867
just 0.0018328245079520728
want 0.0017709019452594528
like 0.0016903614729120188
==========
TOPIC 19
hello 0.008911727886543675
stop 0.0025143616929346174
just 0.0023958078165974795
like 0.00184251815055585
come 0.0018199130672157007
==========
topicIndices: Array[(Array[Int], Array[Double])] = Array((Array(5, 27, 1, 16, 0),Array(0.002368539995607174, 0.0019026093436816463, 0.001739005396343051, 0.001493567868602809, 0.0011919944841106388)), (Array(2, 1, 10, 0, 4),Array(0.012255569993473736, 0.007532527227834193, 0.007114873840600518, 0.006960825682483897, 0.006460380113586568)), (Array(0, 8, 20, 1, 9),Array(0.017593342399778864, 0.01729763439457538, 0.014297985209693677, 0.009395640487800467, 0.007112117826339655)), (Array(1, 0, 36, 2, 5),Array(0.002310885836348927, 0.0020049203493508585, 0.001839601963450054, 0.0016545385663972387, 0.001505081787498549)), (Array(0, 13, 2, 5, 1),Array(0.012396058201765845, 0.004786910731106122, 0.004783067030382327, 0.003733205551673614, 0.0028592628116592403)), (Array(1, 0, 6, 13, 57),Array(0.0028236500929191208, 0.0026011344347436015, 0.0015951009390631876, 0.001385667983895007, 0.001275555813151892)), (Array(6, 5, 1, 0, 233),Array(0.00275337137203844, 0.001685679960504387, 0.0015380845174617235, 0.0014818062892167352, 0.0013896743515293423)), (Array(6, 1, 0, 4, 40),Array(0.011956735401221285, 0.006541063462593452, 0.005428932374204778, 0.004308569608730405, 0.003696595226603709)), (Array(4, 26, 0, 42, 5),Array(0.0019959039820595533, 0.00198077299794292, 0.0016723315231586236, 0.0015606901977245095, 0.0013015271817698212)), (Array(0, 23, 26, 2, 4),Array(0.003615862714921936, 0.001961114693915351, 0.0018595382287745752, 0.0017819242854891695, 0.0016468683030306027)), (Array(11, 3, 19, 0, 5),Array(0.008784671423019166, 0.00282365356227211, 0.0024833597381016476, 0.0019777615447230884, 0.0016576304456760946)), (Array(1, 2, 0, 3, 23),Array(0.0021068918389201634, 0.0020497480766035994, 0.002022347553873645, 0.0019500819941038825, 0.001503771370040063)), (Array(12, 4, 0, 6, 1),Array(0.00433587608823225, 0.0025833796049907604, 0.002007970741805987, 0.0016840410422251017, 0.0010661551551733228)), (Array(0, 10, 4, 6, 16),Array(0.0020279945673448915, 0.0019980250335794405, 0.0012733121858788797, 0.001192108885417234, 0.001186180285931844)), (Array(2, 5, 1, 0, 12),Array(0.004262090436242644, 0.0021537790725358777, 0.0013683197398457016, 0.0010911699327713488, 0.0010869000557749361)), (Array(10, 0, 2, 1, 5),Array(0.004769396664496132, 0.0026229974920448534, 0.0021612642420959253, 0.0013228057897488347, 0.001171812635848879)), (Array(0, 1, 2, 3, 4),Array(0.025323543461007635, 0.018361261941348715, 0.01574431601713426, 0.014855701536091734, 0.011957607420818889)), (Array(2, 0, 1, 18, 24),Array(0.004346004035796333, 0.0022903208899377127, 0.002008680613491114, 0.0019547134832950414, 0.0017287784612649724)), (Array(0, 4, 1, 3, 2),Array(0.003217184151682409, 0.003063734585623867, 0.0018328245079520728, 0.0017709019452594528, 0.0016903614729120188)), (Array(121, 68, 1, 2, 10),Array(0.008911727886543675, 0.0025143616929346174, 0.0023958078165974795, 0.00184251815055585, 0.0018199130672157007)))
vocabList: Array[String] = Array(know, just, like, want, think, right, going, good, yeah, tell, come, time, look, didn, mean, make, okay, really, little, sure, gonna, thing, people, said, maybe, need, sorry, love, talk, thought, doing, life, night, things, work, money, better, told, long, help, believe, years, shit, does, away, place, hell, doesn, great, home, feel, fuck, kind, remember, dead, course, wouldn, wait, kill, guess, understand, thank, girl, wrong, leave, listen, talking, real, stop, hear, nice, happened, fine, wanted, father, gotta, mind, fucking, house, wasn, getting, world, stay, mother, left, came, care, thanks, knew, room, trying, guys, went, looking, coming, heard, friend, haven, seen, best, tonight, live, used, matter, killed, pretty, business, idea, couldn, head, miss, says, wife, called, woman, morning, tomorrow, start, stuff, saying, play, hello, baby, hard, probably, minute, days, took, somebody, school, today, meet, gone, crazy, wants, damn, forget, cause, problem, deal, case, friends, point, hope, jesus, afraid, looks, knows, year, worry, exactly, aren, half, thinking, shut, hold, wanna, face, minutes, bring, word, read, doctor, everybody, makes, supposed, story, turn, true, watch, thousand, family, brother, kids, week, happen, fuckin, working, happy, open, lost, john, hurt, town, ready, alright, late, actually, gave, married, beautiful, soon, jack, times, sleep, door, having, drink, hand, easy, gets, chance, young, trouble, different, anybody, shot, rest, hate, death, second, later, asked, phone, wish, check, quite, walk, change, police, couple, question, close, taking, heart, hours, making, comes, anymore, truth, trust, dollars, important, captain, telling, funny, person, honey, goes, eyes, reason, inside, stand, break, number, tried, means, high, white, water, suppose, body, sick, game, excuse, party, women, country, answer, waiting, christ, office, send, pick, alive, sort, blood, black, daddy, line, husband, goddamn, book, fifty, thirty, million, fact, hands, died, power, started, stupid, shouldn, months, boys, city, sense, dinner, running, hour, shoot, drive, fight, speak, george, living, ship, figure, dear, street, ahead, lady, seven, scared, free, feeling, frank, able, children, outside, safe, moment, news, president, brought, write, happens, sent, bullshit, lose, light, glad, child, girls, sister, sounds, lives, till, promise, sound, weren, save, poor, cool, asking, shall, plan, king, bitch, daughter, beat, weeks, york, cold, worth, taken, harry, needs, piece, movie, fast, possible, small, goin, straight, human, hair, food, tired, company, lucky, pull, wonderful, touch, looked, state, thinks, picture, words, leaving, control, clear, known, special, buddy, luck, follow, order, expect, mary, catch, mouth, worked, mister, learn, playing, perfect, dream, calling, questions, hospital, coffee, takes, ride, parents, miles, works, secret, hotel, explain, worse, kidding, past, outta, general, unless, felt, drop, throw, hang, interested, certainly, absolutely, earth, loved, wonder, dark, accident, seeing, doin, turned, simple, clock, date, sweet, meeting, clean, sign, feet, handle, army, music, giving, report, cops, fucked, charlie, information, yesterday, smart, fall, fault, class, bank, month, blow, swear, caught, major, paul, road, talked, choice, boss, plane, david, paid, wear, american, worried, clothes, ones, lord, goodbye, paper, terrible, strange, mistake, given, kept, finish, blue, murder, hurry, apartment, sell, middle, nothin, careful, hasn, meant, walter, moving, changed, fair, imagine, difference, quiet, happening, near, quit, personal, marry, figured, rose, future, building, kinda, agent, early, mama, michael, watching, trip, private, busy, record, certain, jimmy, broke, longer, sake, store, finally, boat, stick, born, sitting, evening, bucks, history, chief, lying, ought, honor, kiss, darling, lunch, uncle, fool, favor, respect, rich, land, liked, killing, peter, tough, brain, interesting, completely, welcome, nick, problems, wake, radio, dick, honest, cash, dance, dude, james, bout, floor, weird, court, jail, calls, window, involved, drunk, johnny, officer, needed, asshole, spend, situation, books, relax, pain, grand, dangerous, service, letter, stopped, security, realize, offer, table, message, bastard, killer, instead, jake, deep, nervous, pass, somethin, evil, english, bought, short, step, ring, picked, likes, machine, voice, eddie, upset, carry, forgot, lived, afternoon, fear, finished, quick, count, forgive, wrote, named, decided, totally, space, team, lawyer, pleasure, doubt, suit, station, gotten, bother, return, prove, slow, pictures, bunch, strong, list, wearing, driving, join, tape, christmas, force, church, attack, appreciate, college, standing, hungry, present, dying, charge, prison, missing, truck, board, public, staying, calm, gold, ball, hardly, hadn, lead, missed, island, government, cover, horse, reach, joke, french, fish, star, america, moved, soul, surprise, mike, putting, seconds, club, self, movies, dress, cost, lots, price, listening, saved, smell, mark, peace, dreams, crime, gives, entire, department, usually, single, holy, west, beer, nose, wall, stuck, protect, ways, teach, train, grow, awful, type, forever, rock, detective, billy, dumb, papers, walking, beginning, planet, folks, park, attention, card, hide, birthday, master, share, lieutenant, starting, test, reading, field, partner, twice, enjoy, film, bomb, mess, blame, dollar, loves, girlfriend, south, round, records, especially, using, plenty, gentlemen, evidence, silly, admit, experience, fired, normal, talkin, lock, mission, memory, louis, fighting, notice, crap, wedding, promised, ground, idiot, orders, marriage, guns, glass, impossible, heaven, knock, spent, neck, wondering, green, animal, hole, press, drugs, nuts, position, broken, names, asleep, jerry, acting, feels, visit, plans, boyfriend, smoke, paris, wind, tells, gimme, holding, cross, sheriff, walked, mention, judge, code, writing, double, brothers, keeps, pardon, fellow, fell, closed, lovely, angry, cute, percent, surprised, charles, agree, bathroom, correct, address, ridiculous, summer, andy, rules, tommy, group, account, note, learned, colonel, pulled, sing, laugh, proud, sleeping, area, built, jump, upstairs, difficult, river, bobby, dirty, breakfast, bridge, betty, locked, amazing, north, alex, definitely, plus, feelings, accept, kick, worst, grace, gettin, wild, stories, steal, seriously, file, relationship, advice, nature, places, waste, contact, spot, apart, knowing, stole, beach, favorite, loose, level, song, faith, risk, played, eating, foot, patient, witness, turns, washington, action, build, obviously, begin, split, crew, command, games, decide, tight, nurse, keeping, bird, form, runs, copy, scene, jeffrey, arrest, complete, taste, consider, insane, teeth, shoes, henry, career, sooner, monster, devil, hall, innocent, showed, study, gift, weekend, heavy, keys, greatest, comin, destroy, danger, track, raise, suddenly, hanging, bruce, carl, california, apologize, concerned, blind, program, medical, chicken, sweetheart, drinking, forward, seventy, willing, shop, guard, legs, suspect, professor, admiral, data, ticket, camp, tree, goodnight, paying, burn, losing, possibly, dunno, television, senator, trick, murdered, dropped, extra, credit, starts, warm, stone, sold, hiding, meaning, taught, marty, cheap, lately, simply, science, lookin, following, harold, queen, majesty, jeff, corner, cars, heads, training, seat, duty, noticed, helped, bear, enemy, discuss, responsible, trial, dave)
topics: Array[Array[(String, Double)]] = Array(Array((right,0.002368539995607174), (love,0.0019026093436816463), (just,0.001739005396343051), (okay,0.001493567868602809), (know,0.0011919944841106388)), Array((like,0.012255569993473736), (just,0.007532527227834193), (come,0.007114873840600518), (know,0.006960825682483897), (think,0.006460380113586568)), Array((know,0.017593342399778864), (yeah,0.01729763439457538), (gonna,0.014297985209693677), (just,0.009395640487800467), (tell,0.007112117826339655)), Array((just,0.002310885836348927), (know,0.0020049203493508585), (better,0.001839601963450054), (like,0.0016545385663972387), (right,0.001505081787498549)), Array((know,0.012396058201765845), (didn,0.004786910731106122), (like,0.004783067030382327), (right,0.003733205551673614), (just,0.0028592628116592403)), Array((just,0.0028236500929191208), (know,0.0026011344347436015), (going,0.0015951009390631876), (didn,0.001385667983895007), (wait,0.001275555813151892)), Array((going,0.00275337137203844), (right,0.001685679960504387), (just,0.0015380845174617235), (know,0.0014818062892167352), (captain,0.0013896743515293423)), Array((going,0.011956735401221285), (just,0.006541063462593452), (know,0.005428932374204778), (think,0.004308569608730405), (believe,0.003696595226603709)), Array((think,0.0019959039820595533), (sorry,0.00198077299794292), (know,0.0016723315231586236), (shit,0.0015606901977245095), (right,0.0013015271817698212)), Array((know,0.003615862714921936), (said,0.001961114693915351), (sorry,0.0018595382287745752), (like,0.0017819242854891695), (think,0.0016468683030306027)), Array((time,0.008784671423019166), (want,0.00282365356227211), (sure,0.0024833597381016476), (know,0.0019777615447230884), (right,0.0016576304456760946)), Array((just,0.0021068918389201634), (like,0.0020497480766035994), (know,0.002022347553873645), (want,0.0019500819941038825), (said,0.001503771370040063)), Array((look,0.00433587608823225), (think,0.0025833796049907604), (know,0.002007970741805987), (going,0.0016840410422251017), (just,0.0010661551551733228)), Array((know,0.0020279945673448915), (come,0.0019980250335794405), (think,0.0012733121858788797), (going,0.001192108885417234), (okay,0.001186180285931844)), Array((like,0.004262090436242644), (right,0.0021537790725358777), (just,0.0013683197398457016), (know,0.0010911699327713488), (look,0.0010869000557749361)), Array((come,0.004769396664496132), (know,0.0026229974920448534), (like,0.0021612642420959253), (just,0.0013228057897488347), (right,0.001171812635848879)), Array((know,0.025323543461007635), (just,0.018361261941348715), (like,0.01574431601713426), (want,0.014855701536091734), (think,0.011957607420818889)), Array((like,0.004346004035796333), (know,0.0022903208899377127), (just,0.002008680613491114), (little,0.0019547134832950414), (maybe,0.0017287784612649724)), Array((know,0.003217184151682409), (think,0.003063734585623867), (just,0.0018328245079520728), (want,0.0017709019452594528), (like,0.0016903614729120188)), Array((hello,0.008911727886543675), (stop,0.0025143616929346174), (just,0.0023958078165974795), (like,0.00184251815055585), (come,0.0018199130672157007)))
Step 9. Create LDA model with Expectation Maximization
Let's try creating an LDA model with Expectation Maximization on the data that has been refiltered for additional stopwords. We will also increase MaxIterations here to 100 to see if that improves results. See:
import org.apache.spark.mllib.clustering.EMLDAOptimizer
// Set LDA parameters
val em_lda = new LDA()
.setOptimizer(new EMLDAOptimizer())
.setK(numTopics)
.setMaxIterations(100)
.setDocConcentration(-1) // use default values
.setTopicConcentration(-1) // use default values
import org.apache.spark.mllib.clustering.EMLDAOptimizer
em_lda: org.apache.spark.mllib.clustering.LDA = org.apache.spark.mllib.clustering.LDA@7c84d0ae
val em_ldaModel = em_lda.run(new_lda_countVector) // takes a long long time 22 minutes
em_ldaModel: org.apache.spark.mllib.clustering.LDAModel = org.apache.spark.mllib.clustering.DistributedLDAModel@188f58bf
import org.apache.spark.mllib.clustering.DistributedLDAModel;
val em_DldaModel = em_ldaModel.asInstanceOf[DistributedLDAModel]
import org.apache.spark.mllib.clustering.DistributedLDAModel
em_DldaModel: org.apache.spark.mllib.clustering.DistributedLDAModel = org.apache.spark.mllib.clustering.DistributedLDAModel@188f58bf
val top10ConversationsPerTopic = em_DldaModel.topDocumentsPerTopic(10)
top10ConversationsPerTopic: Array[(Array[Long], Array[Double])] = Array((Array(39677, 39693, 39680, 39679, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.03185722402229515, 0.03185722402229515, 0.03185722402229515, 0.03185722402229515, 0.03185722402229515, 0.03185722402229515, 0.031196200176884056, 0.020282154018599348, 0.01099645315549, 0.01099645315549)), (Array(39677, 39693, 39680, 39679, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.035359403020952286, 0.035359403020952286, 0.035359403020952286, 0.035359403020952286, 0.035359403020952286, 0.035359403020952286, 0.03471449892991739, 0.022506359024306477, 0.0112575667750105, 0.0112575667750105)), (Array(39677, 39693, 39680, 39679, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.02454380082221751, 0.02454380082221751, 0.02454380082221751, 0.02454380082221751, 0.02454380082221751, 0.02454380082221751, 0.02390214852968437, 0.01563567947724491, 0.01037864738296468, 0.01037864738296468)), (Array(69318, 15221, 15149, 23167, 59606, 51632, 51639, 64470, 67338, 66968),Array(0.9999514001066685, 0.999945172626603, 0.9999406008121946, 0.9999406008121946, 0.9999406008121946, 0.9999406008121946, 0.9999406008121946, 0.9999406008121946, 0.9999406008121946, 0.9999406008121946)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.05060524129300216, 0.05060524129300216, 0.05060524129300216, 0.05060524129300216, 0.05060524129300216, 0.05060524129300216, 0.05027652950865874, 0.032180017393406625, 0.011630224445618545, 0.011630224445618545)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.043670158470644385, 0.043670158470644385, 0.043670158470644385, 0.043670158470644385, 0.043670158470644385, 0.043670158470644385, 0.04313069897321676, 0.027782187731151566, 0.01176138814023006, 0.01176138814023006)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.04718260291563998, 0.04718260291563998, 0.04718260291563998, 0.04718260291563998, 0.04718260291563998, 0.04718260291563998, 0.046744086701796375, 0.030009654606021424, 0.011639894189919175, 0.011639894189919175)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.04675497162050312, 0.04675497162050312, 0.04675497162050312, 0.04675497162050312, 0.04675497162050312, 0.04675497162050312, 0.04630740194891603, 0.02973828460805704, 0.011613267488918038, 0.011613267488918038)), (Array(39677, 39693, 39680, 39679, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.02922347731698308, 0.02922347731698308, 0.02922347731698308, 0.02922347731698308, 0.02922347731698308, 0.02922347731698308, 0.02856137645845995, 0.01860910369309368, 0.010781174428638705, 0.010781174428638705)), (Array(39677, 39693, 39680, 39674, 39682, 39679, 39681, 39676, 41932, 41967),Array(0.05098977390451263, 0.05098977390451263, 0.05098977390451263, 0.05098977390451263, 0.05098977390451263, 0.05098977390451263, 0.05065478074966324, 0.032424728159182487, 0.011804021891259129, 0.011804021891259129)), (Array(39677, 39693, 39680, 39674, 39682, 39679, 39681, 39676, 41932, 41967),Array(0.05458945924257291, 0.05458945924257291, 0.05458945924257291, 0.05458945924257291, 0.05458945924257291, 0.05458945924257291, 0.054382561204063574, 0.03470709039767808, 0.011833044488991173, 0.011833044488991173)), (Array(39677, 39693, 39680, 39679, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.02757805103727522, 0.02757805103727522, 0.02757805103727522, 0.02757805103727522, 0.02757805103727522, 0.02757805103727522, 0.026914567318365282, 0.017564142155031864, 0.010769649833867787, 0.010769649833867787)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.04651386476447619, 0.04651386476447619, 0.04651386476447619, 0.04651386476447619, 0.04651386476447619, 0.04651386476447619, 0.046074356990568124, 0.029584640688217628, 0.011466200232752964, 0.011466200232752964)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.05592908452117546, 0.05592908452117546, 0.05592908452117546, 0.05592908452117546, 0.05592908452117546, 0.05592908452117546, 0.055741511067049866, 0.03555776746162745, 0.01211237486139149, 0.01211237486139149)), (Array(39681, 39674, 39677, 39693, 39680, 39682, 39679, 39676, 41932, 41967),Array(0.06048967215526035, 0.060441959490269634, 0.060441959490269634, 0.060441959490269634, 0.060441959490269634, 0.060441959490269634, 0.060441959490269634, 0.03841642394224334, 0.011870824949431247, 0.011870824949431247)), (Array(39681, 39679, 39677, 39693, 39680, 39674, 39682, 39676, 41932, 41967),Array(0.06567035036792095, 0.06540012688944775, 0.06540012688944775, 0.06540012688944775, 0.06540012688944775, 0.06540012688944775, 0.06540012688944775, 0.041559067084071775, 0.012094068526188358, 0.012094068526188358)), (Array(39681, 39674, 39677, 39693, 39680, 39682, 39679, 39676, 41932, 41967),Array(0.07103855727399273, 0.07041257887551527, 0.07041257887551527, 0.07041257887551527, 0.07041257887551527, 0.07041257887551527, 0.07041257887551527, 0.04473147169952923, 0.011812341727214322, 0.011812341727214322)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.04379464422302749, 0.04379464422302749, 0.04379464422302749, 0.04379464422302749, 0.04379464422302749, 0.04379464422302749, 0.04327692350658171, 0.027860179926002853, 0.01152922335209424, 0.01152922335209424)), (Array(39677, 39693, 39680, 39679, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.04365837465882906, 0.04365837465882906, 0.04365837465882906, 0.04365837465882906, 0.04365837465882906, 0.04365837465882906, 0.04316378247947061, 0.02777238708460723, 0.011237658307104376, 0.011237658307104376)), (Array(39679, 39677, 39693, 39680, 39674, 39682, 39681, 39676, 41932, 41967),Array(0.04674235176753242, 0.04674235176753242, 0.04674235176753242, 0.04674235176753242, 0.04674235176753242, 0.04674235176753242, 0.0463101731327656, 0.02972951504690979, 0.011452462524389802, 0.011452462524389802)))
top10ConversationsPerTopic.length // number of topics
res52: Int = 20
//em_DldaModel.topicDistributions.take(10).foreach(println)
Note that the EMLDAOptimizer produces a DistributedLDAModel, which stores not only the inferred topics but also the full training corpus and topic distributions for each document in the training corpus.
val topicIndices = em_ldaModel.describeTopics(maxTermsPerTopic = 5)
topicIndices: Array[(Array[Int], Array[Double])] = Array((Array(6435, 9153, 2611, 9555, 9235),Array(1.0844350865928232E-5, 1.4037356622456141E-6, 1.0198257636937534E-6, 1.010016392533973E-6, 9.877489659219E-7)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.2201894817101623E-5, 1.4560010186049552E-6, 1.0547580487281058E-6, 1.0446104695648421E-6, 1.0214202904824573E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(8.080320102037276E-6, 1.2828806625265042E-6, 9.387148884503143E-7, 9.296944883594565E-7, 9.095512260026888E-7)), (Array(0, 1, 2, 3, 4),Array(0.4097048012129488, 0.2966641691130405, 0.28104437242573427, 0.2068481221090779, 0.20178462784115517)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.7791420865642426E-5, 1.5285401934315644E-6, 1.1022151610359566E-6, 1.0916092052333647E-6, 1.0671154286074535E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.5488156532652564E-5, 1.5613578155095174E-6, 1.1250530213722066E-6, 1.1142275765190935E-6, 1.0891766415036671E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.66201985348282E-5, 1.5337088341752489E-6, 1.1062252459821718E-6, 1.0955808549686414E-6, 1.0710096202234095E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.6434785283305463E-5, 1.527062738898831E-6, 1.1015632294086975E-6, 1.0909636379478556E-6, 1.066504587082138E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(9.82890555944203E-6, 1.360381381982805E-6, 9.90695338703216E-7, 9.811686105969582E-7, 9.596620143926599E-7)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.8098274080649888E-5, 1.5662560052135424E-6, 1.127571968783498E-6, 1.1167221871321394E-6, 1.0915664277968502E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.9443267750173392E-5, 1.5746049595955017E-6, 1.1333735056120856E-6, 1.1224679386855895E-6, 1.0971718558358495E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(9.292996004992278E-6, 1.3619125930485615E-6, 9.924672219451632E-7, 9.82924355173023E-7, 9.614096002911668E-7)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.619330796598465E-5, 1.4932367796221485E-6, 1.0785114269963956E-6, 1.06813378362302E-6, 1.0442595139466752E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(2.0195445781462442E-5, 1.6338598744234947E-6, 1.1726861776132844E-6, 1.1614034519421386E-6, 1.1350541791534873E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(2.1543159186970775E-5, 1.5791785506830092E-6, 1.1358076217376717E-6, 1.1248786573437884E-6, 1.099486352793341E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(2.3565018803229148E-5, 1.6252544688003071E-6, 1.16608206417593E-6, 1.1548627950846766E-6, 1.1286452359926982E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(2.498926755354901E-5, 1.5618937315237142E-6, 1.1234358831022108E-6, 1.1126257210374892E-6, 1.0875181953216021E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.5342892698391062E-5, 1.5117065677915513E-6, 1.0917779017440848E-6, 1.0812727863583168E-6, 1.057095929328646E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.5018034022313325E-5, 1.4466343222454145E-6, 1.04735014561389E-6, 1.0372732437703543E-6, 1.0142213705569144E-6)), (Array(6435, 9153, 2611, 9555, 9235),Array(1.627670929533595E-5, 1.4917359134556584E-6, 1.0776961757775105E-6, 1.067326467379095E-6, 1.043483836251971E-6)))
val vocabList = vectorizer.vocabulary
vocabList: Array[String] = Array(know, just, like, want, think, right, going, good, yeah, tell, come, time, look, didn, mean, make, okay, really, little, sure, gonna, thing, people, said, maybe, need, sorry, love, talk, thought, doing, life, night, things, work, money, better, told, long, help, believe, years, shit, does, away, place, hell, doesn, great, home, feel, fuck, kind, remember, dead, course, wouldn, wait, kill, guess, understand, thank, girl, wrong, leave, listen, talking, real, hear, stop, nice, happened, fine, wanted, father, gotta, mind, fucking, house, wasn, getting, world, stay, mother, left, came, care, thanks, knew, room, trying, guys, went, looking, coming, heard, friend, haven, seen, best, tonight, live, used, matter, killed, pretty, business, idea, couldn, head, miss, says, wife, called, woman, morning, tomorrow, start, stuff, saying, play, hello, baby, hard, probably, minute, days, took, somebody, today, school, meet, gone, crazy, wants, damn, forget, problem, cause, deal, case, friends, point, hope, jesus, afraid, looks, knows, year, worry, exactly, aren, half, thinking, shut, hold, wanna, face, minutes, bring, word, read, doctor, everybody, supposed, makes, story, turn, true, watch, thousand, family, brother, kids, week, happen, fuckin, working, open, happy, lost, john, hurt, town, ready, alright, late, actually, married, gave, beautiful, soon, jack, times, sleep, door, having, drink, hand, easy, gets, chance, young, trouble, different, anybody, shot, rest, hate, death, second, later, asked, phone, wish, check, quite, walk, change, police, couple, question, close, taking, heart, hours, making, comes, anymore, truth, trust, dollars, important, captain, telling, funny, person, honey, goes, eyes, reason, inside, stand, break, means, number, tried, high, white, water, suppose, body, sick, game, excuse, party, women, country, answer, christ, waiting, office, send, pick, alive, sort, blood, black, daddy, line, husband, goddamn, book, fifty, thirty, fact, million, died, hands, power, stupid, started, shouldn, months, boys, city, sense, dinner, running, hour, shoot, fight, drive, speak, george, ship, living, figure, dear, street, ahead, lady, seven, scared, free, feeling, frank, able, children, safe, moment, outside, news, president, brought, write, happens, sent, bullshit, lose, light, glad, child, girls, sounds, sister, promise, lives, till, sound, weren, save, poor, cool, shall, asking, plan, king, bitch, daughter, weeks, beat, york, cold, worth, taken, harry, needs, piece, movie, fast, possible, small, goin, straight, human, hair, company, food, tired, lucky, pull, wonderful, touch, looked, thinks, state, picture, leaving, words, control, clear, known, special, buddy, luck, order, follow, expect, mary, catch, mouth, worked, mister, learn, playing, perfect, dream, calling, questions, hospital, takes, ride, coffee, miles, parents, works, secret, hotel, explain, kidding, worse, past, outta, general, felt, drop, unless, throw, interested, hang, certainly, absolutely, earth, loved, dark, wonder, accident, seeing, turned, clock, simple, doin, date, sweet, meeting, clean, sign, feet, handle, music, report, giving, army, fucked, cops, charlie, smart, yesterday, information, fall, fault, bank, class, month, blow, swear, caught, major, paul, road, talked, choice, plane, boss, david, paid, wear, american, worried, lord, paper, goodbye, clothes, ones, terrible, strange, given, mistake, finish, kept, blue, murder, hurry, apartment, sell, middle, nothin, careful, hasn, meant, walter, moving, changed, imagine, fair, difference, quiet, happening, near, quit, personal, marry, figured, future, rose, building, mama, michael, early, agent, kinda, watching, private, trip, record, certain, busy, jimmy, broke, sake, longer, store, boat, stick, finally, born, evening, sitting, bucks, ought, chief, lying, history, kiss, honor, darling, lunch, favor, fool, uncle, respect, rich, land, liked, killing, peter, tough, brain, interesting, completely, problems, welcome, nick, wake, honest, radio, dick, cash, dance, dude, james, bout, floor, weird, court, calls, jail, drunk, window, involved, johnny, officer, needed, asshole, situation, spend, books, relax, pain, service, grand, dangerous, letter, security, stopped, offer, realize, table, bastard, message, instead, killer, jake, deep, nervous, somethin, pass, evil, english, bought, short, step, ring, picked, likes, machine, eddie, voice, upset, forgot, carry, lived, afternoon, fear, quick, finished, count, forgive, wrote, named, decided, totally, space, team, pleasure, doubt, lawyer, station, gotten, suit, bother, prove, return, slow, pictures, bunch, strong, list, wearing, driving, join, tape, christmas, attack, appreciate, force, church, college, hungry, standing, present, dying, prison, missing, charge, board, truck, public, calm, gold, staying, ball, hardly, hadn, missed, lead, island, government, horse, cover, french, reach, joke, fish, star, mike, surprise, america, moved, soul, dress, seconds, club, self, putting, movies, lots, cost, listening, price, saved, smell, mark, peace, dreams, entire, crime, gives, usually, single, department, holy, beer, west, protect, stuck, wall, nose, ways, teach, forever, grow, train, type, awful, rock, detective, billy, walking, dumb, papers, beginning, planet, folks, park, attention, birthday, hide, card, master, share, reading, test, starting, lieutenant, field, partner, enjoy, twice, film, dollar, bomb, mess, blame, south, loves, girlfriend, round, records, using, plenty, especially, gentlemen, evidence, silly, experience, admit, fired, normal, talkin, mission, louis, memory, fighting, lock, notice, crap, wedding, promised, marriage, ground, guns, glass, idiot, orders, impossible, heaven, knock, hole, neck, animal, spent, green, wondering, nuts, press, drugs, broken, position, names, asleep, jerry, visit, boyfriend, acting, feels, plans, paris, smoke, tells, wind, cross, holding, sheriff, gimme, walked, mention, writing, double, brothers, code, judge, pardon, keeps, fellow, fell, closed, lovely, angry, cute, charles, surprised, percent, correct, bathroom, agree, address, andy, ridiculous, summer, tommy, rules, group, account, note, pulled, sleeping, sing, learned, proud, laugh, colonel, upstairs, river, difficult, built, jump, area, dirty, betty, bridge, breakfast, bobby, locked, amazing, north, feelings, alex, plus, definitely, worst, accept, kick, seriously, grace, steal, wild, stories, file, gettin, relationship, advice, nature, contact, spot, places, waste, knowing, beach, stole, apart, favorite, faith, level, loose, risk, song, eating, foot, played, patient, washington, turns, witness, action, build, obviously, begin, split, crew, command, games, tight, decide, nurse, keeping, runs, form, bird, copy, insane, complete, arrest, consider, taste, scene, jeffrey, teeth, shoes, career, henry, sooner, devil, monster, showed, weekend, gift, innocent, study, heavy, hall, comin, danger, greatest, track, keys, raise, destroy, concerned, program, carl, blind, apologize, suddenly, hanging, bruce, california, chicken, seventy, forward, drinking, sweetheart, medical, suspect, admiral, guard, shop, professor, legs, willing, camp, data, ticket, tree, goodnight, television, losing, senator, murdered, burn, dunno, paying, possibly, trick, dropped, credit, extra, starts, warm, hiding, meaning, sold, stone, taught, marty, lately, cheap, lookin, science, simply, jeff, corner, harold, following, majesty, queen, duty, cars, training, heads, seat, discuss, bear, enemy, helped, noticed, common, screw, dave)
vocabList.size
res32: Int = 10000
val topics = topicIndices.map { case (terms, termWeights) =>
terms.map(vocabList(_)).zip(termWeights)
}
topics: Array[Array[(String, Double)]] = Array(Array((just,0.030515134931284552), (like,0.02463563559747823), (want,0.022529385381465025), (damn,0.02094828832824297), (going,0.0203407289886203)), Array((yeah,0.10787301090151602), (look,0.0756831002291994), (know,0.04815746564274915), (wait,0.03897182014529944), (night,0.0341458394828345)), Array((gonna,0.08118584492034046), (money,0.051736711600637544), (shit,0.04620430294274594), (fuck,0.0399843125556081), (kill,0.03672740843080258)), Array((people,0.020091372023286612), (know,0.018613400462887356), (work,0.016775643603287843), (does,0.015522555458447744), (think,0.012161168331925723)), Array((know,0.031956573561538214), (just,0.030674598809934856), (want,0.027663491240851962), (tell,0.025727217382788027), (right,0.02300853167338119)), Array((love,0.05932570200934131), (father,0.030080735900045442), (life,0.01769248067468245), (true,0.016281752071881345), (young,0.014927950883812253)), Array((remember,0.03998401809663685), (went,0.01737965538107633), (lost,0.016916065536574213), (called,0.016443441316683228), (story,0.014849882671062261)), Array((house,0.028911209424810257), (miss,0.025669944694943093), (right,0.02091105252727788), (family,0.017862939987512365), (important,0.013959164390834044)), Array((saying,0.022939827090645636), (know,0.021335083902970984), (idea,0.017628999871937747), (business,0.017302568063786224), (police,0.012284217866942303)), Array((know,0.051876601466269136), (like,0.03828159069993671), (maybe,0.03754385940676905), (just,0.031938551661426284), (want,0.02876693222824349)), Array((years,0.032537676027398765), (going,0.030596831997667568), (case,0.02049555392502822), (doctor,0.018671171294737107), (working,0.017672067172167016)), Array((stuff,0.02236582778896705), (school,0.020057798194969816), (john,0.017134198006217606), (week,0.017075852415410653), (thousand,0.017013413435021035)), Array((little,0.08663446368316245), (girl,0.035120377589734936), (like,0.02992080326340266), (woman,0.0240813719635157), (baby,0.022471517953608963)), Array((know,0.0283115823590395), (leave,0.02744935904744228), (time,0.02050833156294194), (want,0.020124145131863225), (just,0.019466336438890477)), Array((didn,0.08220031921979461), (like,0.05062323326717784), (real,0.03087838046777391), (guess,0.02452989702353384), (says,0.022815035397008333)), Array((minutes,0.018541518543996716), (time,0.014737962244588431), (captain,0.012594614743931537), (thirty,0.01193707771669708), (ship,0.011260576815409516)), Array((okay,0.08153575328080886), (just,0.050004142902999975), (right,0.03438984898476042), (know,0.02821327795933634), (home,0.023397063860326372)), Array((country,0.011270500385627474), (power,0.010428408353623762), (president,0.009392162067926028), (fight,0.00799742811584178), (possible,0.007597974486019279)), Array((know,0.09541058020800194), (think,0.0698707939786508), (really,0.06881812755565207), (mean,0.02909700228968688), (just,0.028699687473471538)), Array((dead,0.03833642117149438), (like,0.017873711992106994), (hand,0.015280854355409379), (white,0.013718491413582671), (blood,0.012699265888344448)))
vocabList(47) // 47 is the index of the term 'university' or the first term in topics - this may change due to randomness in algorithm
res33: String = doesn
This is just doing it all at once.
val topicIndices = em_ldaModel.describeTopics(maxTermsPerTopic = 5)
val vocabList = vectorizer.vocabulary
val topics = topicIndices.map { case (terms, termWeights) =>
terms.map(vocabList(_)).zip(termWeights)
}
println(s"$numTopics topics:")
topics.zipWithIndex.foreach { case (topic, i) =>
println(s"TOPIC $i")
topic.foreach { case (term, weight) => println(s"$term\t$weight") }
println(s"==========")
}
20 topics:
TOPIC 0
just 0.030515134931284552
like 0.02463563559747823
want 0.022529385381465025
damn 0.02094828832824297
going 0.0203407289886203
==========
TOPIC 1
yeah 0.10787301090151602
look 0.0756831002291994
know 0.04815746564274915
wait 0.03897182014529944
night 0.0341458394828345
==========
TOPIC 2
gonna 0.08118584492034046
money 0.051736711600637544
shit 0.04620430294274594
fuck 0.0399843125556081
kill 0.03672740843080258
==========
TOPIC 3
people 0.020091372023286612
know 0.018613400462887356
work 0.016775643603287843
does 0.015522555458447744
think 0.012161168331925723
==========
TOPIC 4
know 0.031956573561538214
just 0.030674598809934856
want 0.027663491240851962
tell 0.025727217382788027
right 0.02300853167338119
==========
TOPIC 5
love 0.05932570200934131
father 0.030080735900045442
life 0.01769248067468245
true 0.016281752071881345
young 0.014927950883812253
==========
TOPIC 6
remember 0.03998401809663685
went 0.01737965538107633
lost 0.016916065536574213
called 0.016443441316683228
story 0.014849882671062261
==========
TOPIC 7
house 0.028911209424810257
miss 0.025669944694943093
right 0.02091105252727788
family 0.017862939987512365
important 0.013959164390834044
==========
TOPIC 8
saying 0.022939827090645636
know 0.021335083902970984
idea 0.017628999871937747
business 0.017302568063786224
police 0.012284217866942303
==========
TOPIC 9
know 0.051876601466269136
like 0.03828159069993671
maybe 0.03754385940676905
just 0.031938551661426284
want 0.02876693222824349
==========
TOPIC 10
years 0.032537676027398765
going 0.030596831997667568
case 0.02049555392502822
doctor 0.018671171294737107
working 0.017672067172167016
==========
TOPIC 11
stuff 0.02236582778896705
school 0.020057798194969816
john 0.017134198006217606
week 0.017075852415410653
thousand 0.017013413435021035
==========
TOPIC 12
little 0.08663446368316245
girl 0.035120377589734936
like 0.02992080326340266
woman 0.0240813719635157
baby 0.022471517953608963
==========
TOPIC 13
know 0.0283115823590395
leave 0.02744935904744228
time 0.02050833156294194
want 0.020124145131863225
just 0.019466336438890477
==========
TOPIC 14
didn 0.08220031921979461
like 0.05062323326717784
real 0.03087838046777391
guess 0.02452989702353384
says 0.022815035397008333
==========
TOPIC 15
minutes 0.018541518543996716
time 0.014737962244588431
captain 0.012594614743931537
thirty 0.01193707771669708
ship 0.011260576815409516
==========
TOPIC 16
okay 0.08153575328080886
just 0.050004142902999975
right 0.03438984898476042
know 0.02821327795933634
home 0.023397063860326372
==========
TOPIC 17
country 0.011270500385627474
power 0.010428408353623762
president 0.009392162067926028
fight 0.00799742811584178
possible 0.007597974486019279
==========
TOPIC 18
know 0.09541058020800194
think 0.0698707939786508
really 0.06881812755565207
mean 0.02909700228968688
just 0.028699687473471538
==========
TOPIC 19
dead 0.03833642117149438
like 0.017873711992106994
hand 0.015280854355409379
white 0.013718491413582671
blood 0.012699265888344448
==========
topicIndices: Array[(Array[Int], Array[Double])] = Array((Array(1, 2, 3, 135, 6),Array(0.030515134931284552, 0.02463563559747823, 0.022529385381465025, 0.02094828832824297, 0.0203407289886203)), (Array(8, 12, 0, 57, 32),Array(0.10787301090151602, 0.0756831002291994, 0.04815746564274915, 0.03897182014529944, 0.0341458394828345)), (Array(20, 35, 42, 51, 58),Array(0.08118584492034046, 0.051736711600637544, 0.04620430294274594, 0.0399843125556081, 0.03672740843080258)), (Array(22, 0, 34, 43, 4),Array(0.020091372023286612, 0.018613400462887356, 0.016775643603287843, 0.015522555458447744, 0.012161168331925723)), (Array(0, 1, 3, 9, 5),Array(0.031956573561538214, 0.030674598809934856, 0.027663491240851962, 0.025727217382788027, 0.02300853167338119)), (Array(27, 74, 31, 168, 202),Array(0.05932570200934131, 0.030080735900045442, 0.01769248067468245, 0.016281752071881345, 0.014927950883812253)), (Array(53, 92, 180, 113, 166),Array(0.03998401809663685, 0.01737965538107633, 0.016916065536574213, 0.016443441316683228, 0.014849882671062261)), (Array(78, 110, 5, 171, 232),Array(0.028911209424810257, 0.025669944694943093, 0.02091105252727788, 0.017862939987512365, 0.013959164390834044)), (Array(119, 0, 107, 106, 219),Array(0.022939827090645636, 0.021335083902970984, 0.017628999871937747, 0.017302568063786224, 0.012284217866942303)), (Array(0, 2, 24, 1, 3),Array(0.051876601466269136, 0.03828159069993671, 0.03754385940676905, 0.031938551661426284, 0.02876693222824349)), (Array(41, 6, 140, 162, 177),Array(0.032537676027398765, 0.030596831997667568, 0.02049555392502822, 0.018671171294737107, 0.017672067172167016)), (Array(118, 130, 181, 174, 170),Array(0.02236582778896705, 0.020057798194969816, 0.017134198006217606, 0.017075852415410653, 0.017013413435021035)), (Array(18, 62, 2, 114, 122),Array(0.08663446368316245, 0.035120377589734936, 0.02992080326340266, 0.0240813719635157, 0.022471517953608963)), (Array(0, 64, 11, 3, 1),Array(0.0283115823590395, 0.02744935904744228, 0.02050833156294194, 0.020124145131863225, 0.019466336438890477)), (Array(13, 2, 67, 59, 111),Array(0.08220031921979461, 0.05062323326717784, 0.03087838046777391, 0.02452989702353384, 0.022815035397008333)), (Array(158, 11, 233, 274, 295),Array(0.018541518543996716, 0.014737962244588431, 0.012594614743931537, 0.01193707771669708, 0.011260576815409516)), (Array(16, 1, 5, 0, 49),Array(0.08153575328080886, 0.050004142902999975, 0.03438984898476042, 0.02821327795933634, 0.023397063860326372)), (Array(257, 279, 313, 291, 351),Array(0.011270500385627474, 0.010428408353623762, 0.009392162067926028, 0.00799742811584178, 0.007597974486019279)), (Array(0, 4, 17, 14, 1),Array(0.09541058020800194, 0.0698707939786508, 0.06881812755565207, 0.02909700228968688, 0.028699687473471538)), (Array(54, 2, 198, 248, 266),Array(0.03833642117149438, 0.017873711992106994, 0.015280854355409379, 0.013718491413582671, 0.012699265888344448)))
vocabList: Array[String] = Array(know, just, like, want, think, right, going, good, yeah, tell, come, time, look, didn, mean, make, okay, really, little, sure, gonna, thing, people, said, maybe, need, sorry, love, talk, thought, doing, life, night, things, work, money, better, told, long, help, believe, years, shit, does, away, place, hell, doesn, great, home, feel, fuck, kind, remember, dead, course, wouldn, wait, kill, guess, understand, thank, girl, wrong, leave, listen, talking, real, hear, stop, nice, happened, fine, wanted, father, gotta, mind, fucking, house, wasn, getting, world, stay, mother, left, came, care, thanks, knew, room, trying, guys, went, looking, coming, heard, friend, haven, seen, best, tonight, live, used, matter, killed, pretty, business, idea, couldn, head, miss, says, wife, called, woman, morning, tomorrow, start, stuff, saying, play, hello, baby, hard, probably, minute, days, took, somebody, today, school, meet, gone, crazy, wants, damn, forget, problem, cause, deal, case, friends, point, hope, jesus, afraid, looks, knows, year, worry, exactly, aren, half, thinking, shut, hold, wanna, face, minutes, bring, word, read, doctor, everybody, supposed, makes, story, turn, true, watch, thousand, family, brother, kids, week, happen, fuckin, working, open, happy, lost, john, hurt, town, ready, alright, late, actually, married, gave, beautiful, soon, jack, times, sleep, door, having, drink, hand, easy, gets, chance, young, trouble, different, anybody, shot, rest, hate, death, second, later, asked, phone, wish, check, quite, walk, change, police, couple, question, close, taking, heart, hours, making, comes, anymore, truth, trust, dollars, important, captain, telling, funny, person, honey, goes, eyes, reason, inside, stand, break, means, number, tried, high, white, water, suppose, body, sick, game, excuse, party, women, country, answer, christ, waiting, office, send, pick, alive, sort, blood, black, daddy, line, husband, goddamn, book, fifty, thirty, fact, million, died, hands, power, stupid, started, shouldn, months, boys, city, sense, dinner, running, hour, shoot, fight, drive, speak, george, ship, living, figure, dear, street, ahead, lady, seven, scared, free, feeling, frank, able, children, safe, moment, outside, news, president, brought, write, happens, sent, bullshit, lose, light, glad, child, girls, sounds, sister, promise, lives, till, sound, weren, save, poor, cool, shall, asking, plan, king, bitch, daughter, weeks, beat, york, cold, worth, taken, harry, needs, piece, movie, fast, possible, small, goin, straight, human, hair, company, food, tired, lucky, pull, wonderful, touch, looked, thinks, state, picture, leaving, words, control, clear, known, special, buddy, luck, order, follow, expect, mary, catch, mouth, worked, mister, learn, playing, perfect, dream, calling, questions, hospital, takes, ride, coffee, miles, parents, works, secret, hotel, explain, kidding, worse, past, outta, general, felt, drop, unless, throw, interested, hang, certainly, absolutely, earth, loved, dark, wonder, accident, seeing, turned, clock, simple, doin, date, sweet, meeting, clean, sign, feet, handle, music, report, giving, army, fucked, cops, charlie, smart, yesterday, information, fall, fault, bank, class, month, blow, swear, caught, major, paul, road, talked, choice, plane, boss, david, paid, wear, american, worried, lord, paper, goodbye, clothes, ones, terrible, strange, given, mistake, finish, kept, blue, murder, hurry, apartment, sell, middle, nothin, careful, hasn, meant, walter, moving, changed, imagine, fair, difference, quiet, happening, near, quit, personal, marry, figured, future, rose, building, mama, michael, early, agent, kinda, watching, private, trip, record, certain, busy, jimmy, broke, sake, longer, store, boat, stick, finally, born, evening, sitting, bucks, ought, chief, lying, history, kiss, honor, darling, lunch, favor, fool, uncle, respect, rich, land, liked, killing, peter, tough, brain, interesting, completely, problems, welcome, nick, wake, honest, radio, dick, cash, dance, dude, james, bout, floor, weird, court, calls, jail, drunk, window, involved, johnny, officer, needed, asshole, situation, spend, books, relax, pain, service, grand, dangerous, letter, security, stopped, offer, realize, table, bastard, message, instead, killer, jake, deep, nervous, somethin, pass, evil, english, bought, short, step, ring, picked, likes, machine, eddie, voice, upset, forgot, carry, lived, afternoon, fear, quick, finished, count, forgive, wrote, named, decided, totally, space, team, pleasure, doubt, lawyer, station, gotten, suit, bother, prove, return, slow, pictures, bunch, strong, list, wearing, driving, join, tape, christmas, attack, appreciate, force, church, college, hungry, standing, present, dying, prison, missing, charge, board, truck, public, calm, gold, staying, ball, hardly, hadn, missed, lead, island, government, horse, cover, french, reach, joke, fish, star, mike, surprise, america, moved, soul, dress, seconds, club, self, putting, movies, lots, cost, listening, price, saved, smell, mark, peace, dreams, entire, crime, gives, usually, single, department, holy, beer, west, protect, stuck, wall, nose, ways, teach, forever, grow, train, type, awful, rock, detective, billy, walking, dumb, papers, beginning, planet, folks, park, attention, birthday, hide, card, master, share, reading, test, starting, lieutenant, field, partner, enjoy, twice, film, dollar, bomb, mess, blame, south, loves, girlfriend, round, records, using, plenty, especially, gentlemen, evidence, silly, experience, admit, fired, normal, talkin, mission, louis, memory, fighting, lock, notice, crap, wedding, promised, marriage, ground, guns, glass, idiot, orders, impossible, heaven, knock, hole, neck, animal, spent, green, wondering, nuts, press, drugs, broken, position, names, asleep, jerry, visit, boyfriend, acting, feels, plans, paris, smoke, tells, wind, cross, holding, sheriff, gimme, walked, mention, writing, double, brothers, code, judge, pardon, keeps, fellow, fell, closed, lovely, angry, cute, charles, surprised, percent, correct, bathroom, agree, address, andy, ridiculous, summer, tommy, rules, group, account, note, pulled, sleeping, sing, learned, proud, laugh, colonel, upstairs, river, difficult, built, jump, area, dirty, betty, bridge, breakfast, bobby, locked, amazing, north, feelings, alex, plus, definitely, worst, accept, kick, seriously, grace, steal, wild, stories, file, gettin, relationship, advice, nature, contact, spot, places, waste, knowing, beach, stole, apart, favorite, faith, level, loose, risk, song, eating, foot, played, patient, washington, turns, witness, action, build, obviously, begin, split, crew, command, games, tight, decide, nurse, keeping, runs, form, bird, copy, insane, complete, arrest, consider, taste, scene, jeffrey, teeth, shoes, career, henry, sooner, devil, monster, showed, weekend, gift, innocent, study, heavy, hall, comin, danger, greatest, track, keys, raise, destroy, concerned, program, carl, blind, apologize, suddenly, hanging, bruce, california, chicken, seventy, forward, drinking, sweetheart, medical, suspect, admiral, guard, shop, professor, legs, willing, camp, data, ticket, tree, goodnight, television, losing, senator, murdered, burn, dunno, paying, possibly, trick, dropped, credit, extra, starts, warm, hiding, meaning, sold, stone, taught, marty, lately, cheap, lookin, science, simply, jeff, corner, harold, following, majesty, queen, duty, cars, training, heads, seat, discuss, bear, enemy, helped, noticed, common, screw, dave)
topics: Array[Array[(String, Double)]] = Array(Array((just,0.030515134931284552), (like,0.02463563559747823), (want,0.022529385381465025), (damn,0.02094828832824297), (going,0.0203407289886203)), Array((yeah,0.10787301090151602), (look,0.0756831002291994), (know,0.04815746564274915), (wait,0.03897182014529944), (night,0.0341458394828345)), Array((gonna,0.08118584492034046), (money,0.051736711600637544), (shit,0.04620430294274594), (fuck,0.0399843125556081), (kill,0.03672740843080258)), Array((people,0.020091372023286612), (know,0.018613400462887356), (work,0.016775643603287843), (does,0.015522555458447744), (think,0.012161168331925723)), Array((know,0.031956573561538214), (just,0.030674598809934856), (want,0.027663491240851962), (tell,0.025727217382788027), (right,0.02300853167338119)), Array((love,0.05932570200934131), (father,0.030080735900045442), (life,0.01769248067468245), (true,0.016281752071881345), (young,0.014927950883812253)), Array((remember,0.03998401809663685), (went,0.01737965538107633), (lost,0.016916065536574213), (called,0.016443441316683228), (story,0.014849882671062261)), Array((house,0.028911209424810257), (miss,0.025669944694943093), (right,0.02091105252727788), (family,0.017862939987512365), (important,0.013959164390834044)), Array((saying,0.022939827090645636), (know,0.021335083902970984), (idea,0.017628999871937747), (business,0.017302568063786224), (police,0.012284217866942303)), Array((know,0.051876601466269136), (like,0.03828159069993671), (maybe,0.03754385940676905), (just,0.031938551661426284), (want,0.02876693222824349)), Array((years,0.032537676027398765), (going,0.030596831997667568), (case,0.02049555392502822), (doctor,0.018671171294737107), (working,0.017672067172167016)), Array((stuff,0.02236582778896705), (school,0.020057798194969816), (john,0.017134198006217606), (week,0.017075852415410653), (thousand,0.017013413435021035)), Array((little,0.08663446368316245), (girl,0.035120377589734936), (like,0.02992080326340266), (woman,0.0240813719635157), (baby,0.022471517953608963)), Array((know,0.0283115823590395), (leave,0.02744935904744228), (time,0.02050833156294194), (want,0.020124145131863225), (just,0.019466336438890477)), Array((didn,0.08220031921979461), (like,0.05062323326717784), (real,0.03087838046777391), (guess,0.02452989702353384), (says,0.022815035397008333)), Array((minutes,0.018541518543996716), (time,0.014737962244588431), (captain,0.012594614743931537), (thirty,0.01193707771669708), (ship,0.011260576815409516)), Array((okay,0.08153575328080886), (just,0.050004142902999975), (right,0.03438984898476042), (know,0.02821327795933634), (home,0.023397063860326372)), Array((country,0.011270500385627474), (power,0.010428408353623762), (president,0.009392162067926028), (fight,0.00799742811584178), (possible,0.007597974486019279)), Array((know,0.09541058020800194), (think,0.0698707939786508), (really,0.06881812755565207), (mean,0.02909700228968688), (just,0.028699687473471538)), Array((dead,0.03833642117149438), (like,0.017873711992106994), (hand,0.015280854355409379), (white,0.013718491413582671), (blood,0.012699265888344448)))
top10ConversationsPerTopic(2)
res54: (Array[Long], Array[Double]) = (Array(22243, 39967, 18136, 18149, 59043, 61513, 34087, 75874, 66270, 68876),Array(0.9986758340945384, 0.99866200816902, 0.9982983538060165, 0.9982983538060165, 0.9982983538060165, 0.9982983538060165, 0.9982983538060165, 0.9982983538060165, 0.9982983538060165, 0.9982983538060165))
top10ConversationsPerTopic(2)._1
res55: Array[Long] = Array(22243, 39967, 18136, 18149, 59043, 61513, 34087, 75874, 66270, 68876)
val scenesForTopic2 = sc.parallelize(top10ConversationsPerTopic(2)._1).toDF("id")
scenesForTopic2: org.apache.spark.sql.DataFrame = [id: bigint]
display(scenesForTopic2.join(corpusDF,"id"))
| id | corpus | movieTitle | movieYear |
|---|---|---|---|
| 22243.0 | Fuck him. :-()-: Don't. :-()-: Fuck her too. | panic room | 2002 |
| 59043.0 | Are you ok? :-()-: Fuck no. | magnolia | 1999 |
| 66270.0 | Hey now... what the fuck... ? :-()-: Again. | red white black & blue | 2006 |
| 75874.0 | What about Moliere? :-()-: Fuck off. | the beach | 2000/I |
| 68876.0 | What the fuck is that? :-()-: A switchblade. | seven | 1979 |
| 34087.0 | Fuck me! Yes! :-()-: Uh... | american pie | 1999 |
| 61513.0 | What the fuck is that?! :-()-: Screamer. | arcade | 1993 |
| 18136.0 | What the fuck was that about? :-()-: She was jonesing for me. | made | 2001 |
| 18149.0 | C'mon... :-()-: Fuck... | made | 2001 |
| 39967.0 | Shit, shit, shit... :-()-: You're almost there, you can do it -- can do -- can do. | broadcast news | 1987 |
sc.parallelize(top10ConversationsPerTopic(2)._1).toDF("id").join(corpusDF,"id").show(10,false)
+-----+----------------------------------------------------------------------------------+----------------------+---------+
|id |corpus |movieTitle |movieYear|
+-----+----------------------------------------------------------------------------------+----------------------+---------+
|22243|Fuck him. :-()-: Don't. :-()-: Fuck her too. |panic room |2002 |
|59043|Are you ok? :-()-: Fuck no. |magnolia |1999 |
|66270|Hey now... what the fuck... ? :-()-: Again. |red white black & blue|2006 |
|75874|What about Moliere? :-()-: Fuck off. |the beach |2000/I |
|68876|What the fuck is that? :-()-: A switchblade. |seven |1979 |
|34087|Fuck me! Yes! :-()-: Uh... |american pie |1999 |
|61513|What the fuck is that?! :-()-: Screamer. |arcade |1993 |
|18136|What the fuck was that about? :-()-: She was jonesing for me. |made |2001 |
|18149|C'mon... :-()-: Fuck... |made |2001 |
|39967|Shit, shit, shit... :-()-: You're almost there, you can do it -- can do -- can do.|broadcast news |1987 |
+-----+----------------------------------------------------------------------------------+----------------------+---------+
sc.parallelize(top10ConversationsPerTopic(5)._1).toDF("id").join(corpusDF,"id").show(10,false)
+-----+---------------------------------------------------------+-----------------+---------+
|id |corpus |movieTitle |movieYear|
+-----+---------------------------------------------------------+-----------------+---------+
|68250|I love you man :-()-: I love you too. |say anything... |1989 |
|31256|I love you. :-()-: I love you. |total recall |1990 |
|868 |I love you. :-()-: I love you. |8mm |1999 |
|17285|Do me. :-()-: I love you. :-()-: I love you. |little nicky |2000 |
|56529|Why do you love me? :-()-: Why do you love me? |jerry maguire |1996 |
|67529|I love you, too. :-()-: I love you. I love you. |runaway bride |1999 |
|82132|Why did you say that? :-()-: Say what? :-()-: I love you.|willow |1988 |
|50163|I love you, Bud. :-()-: I love you more. |frequency |2000 |
|39173|I love you. :-()-: I love you too, Dad. |body of evidence |1993 |
|57385|Yes? :-()-: I love you... |kramer vs. kramer|1979 |
+-----+---------------------------------------------------------+-----------------+---------+
corpusDF.show(5)
+-----+--------------------+------------+---------+
| id| corpus| movieTitle|movieYear|
+-----+--------------------+------------+---------+
|17668|This would be fun...|lost horizon| 1937|
|17598|Cave, eh? Where? ...|lost horizon| 1937|
|17663|Something grand a...|lost horizon| 1937|
|17593|You see? You get ...|lost horizon| 1937|
|17658|Let me up! Let me...|lost horizon| 1937|
+-----+--------------------+------------+---------+
only showing top 5 rows
We've managed to get some good results here. For example, we can easily infer that Topic 2 is about space, Topic 3 is about israel, etc.
We still get some ambiguous results like Topic 0.
To improve our results further, we could employ some of the below methods:
- Refilter data for additional data-specific stopwords
- Use Stemming or Lemmatization to preprocess data
- Experiment with a smaller number of topics, since some of these topics in the 20 Newsgroups are pretty similar
- Increase model's MaxIterations
Visualize Results
We will try visualizing the results obtained from the EM LDA model with a d3 bubble chart.
// Zip topic terms with topic IDs
val termArray = topics.zipWithIndex
termArray: Array[(Array[(String, Double)], Int)] = Array((Array((just,0.030515134931284552), (like,0.02463563559747823), (want,0.022529385381465025), (damn,0.02094828832824297), (going,0.0203407289886203)),0), (Array((yeah,0.10787301090151602), (look,0.0756831002291994), (know,0.04815746564274915), (wait,0.03897182014529944), (night,0.0341458394828345)),1), (Array((gonna,0.08118584492034046), (money,0.051736711600637544), (shit,0.04620430294274594), (fuck,0.0399843125556081), (kill,0.03672740843080258)),2), (Array((people,0.020091372023286612), (know,0.018613400462887356), (work,0.016775643603287843), (does,0.015522555458447744), (think,0.012161168331925723)),3), (Array((know,0.031956573561538214), (just,0.030674598809934856), (want,0.027663491240851962), (tell,0.025727217382788027), (right,0.02300853167338119)),4), (Array((love,0.05932570200934131), (father,0.030080735900045442), (life,0.01769248067468245), (true,0.016281752071881345), (young,0.014927950883812253)),5), (Array((remember,0.03998401809663685), (went,0.01737965538107633), (lost,0.016916065536574213), (called,0.016443441316683228), (story,0.014849882671062261)),6), (Array((house,0.028911209424810257), (miss,0.025669944694943093), (right,0.02091105252727788), (family,0.017862939987512365), (important,0.013959164390834044)),7), (Array((saying,0.022939827090645636), (know,0.021335083902970984), (idea,0.017628999871937747), (business,0.017302568063786224), (police,0.012284217866942303)),8), (Array((know,0.051876601466269136), (like,0.03828159069993671), (maybe,0.03754385940676905), (just,0.031938551661426284), (want,0.02876693222824349)),9), (Array((years,0.032537676027398765), (going,0.030596831997667568), (case,0.02049555392502822), (doctor,0.018671171294737107), (working,0.017672067172167016)),10), (Array((stuff,0.02236582778896705), (school,0.020057798194969816), (john,0.017134198006217606), (week,0.017075852415410653), (thousand,0.017013413435021035)),11), (Array((little,0.08663446368316245), (girl,0.035120377589734936), (like,0.02992080326340266), (woman,0.0240813719635157), (baby,0.022471517953608963)),12), (Array((know,0.0283115823590395), (leave,0.02744935904744228), (time,0.02050833156294194), (want,0.020124145131863225), (just,0.019466336438890477)),13), (Array((didn,0.08220031921979461), (like,0.05062323326717784), (real,0.03087838046777391), (guess,0.02452989702353384), (says,0.022815035397008333)),14), (Array((minutes,0.018541518543996716), (time,0.014737962244588431), (captain,0.012594614743931537), (thirty,0.01193707771669708), (ship,0.011260576815409516)),15), (Array((okay,0.08153575328080886), (just,0.050004142902999975), (right,0.03438984898476042), (know,0.02821327795933634), (home,0.023397063860326372)),16), (Array((country,0.011270500385627474), (power,0.010428408353623762), (president,0.009392162067926028), (fight,0.00799742811584178), (possible,0.007597974486019279)),17), (Array((know,0.09541058020800194), (think,0.0698707939786508), (really,0.06881812755565207), (mean,0.02909700228968688), (just,0.028699687473471538)),18), (Array((dead,0.03833642117149438), (like,0.017873711992106994), (hand,0.015280854355409379), (white,0.013718491413582671), (blood,0.012699265888344448)),19))
// Transform data into the form (term, probability, topicId)
val termRDD = sc.parallelize(termArray)
val termRDD2 =termRDD.flatMap( (x: (Array[(String, Double)], Int)) => {
val arrayOfTuple = x._1
val topicId = x._2
arrayOfTuple.map(el => (el._1, el._2, topicId))
})
termRDD: org.apache.spark.rdd.RDD[(Array[(String, Double)], Int)] = ParallelCollectionRDD[3066] at parallelize at <console>:109
termRDD2: org.apache.spark.rdd.RDD[(String, Double, Int)] = MapPartitionsRDD[3067] at flatMap at <console>:110
// Create DF with proper column names
val termDF = termRDD2.toDF.withColumnRenamed("_1", "term").withColumnRenamed("_2", "probability").withColumnRenamed("_3", "topicId")
termDF: org.apache.spark.sql.DataFrame = [term: string, probability: double, topicId: int]
display(termDF)
| term | probability | topicId |
|---|---|---|
| just | 3.0515134931284552e-2 | 0.0 |
| like | 2.463563559747823e-2 | 0.0 |
| want | 2.2529385381465025e-2 | 0.0 |
| damn | 2.094828832824297e-2 | 0.0 |
| going | 2.03407289886203e-2 | 0.0 |
| yeah | 0.10787301090151602 | 1.0 |
| look | 7.56831002291994e-2 | 1.0 |
| know | 4.815746564274915e-2 | 1.0 |
| wait | 3.897182014529944e-2 | 1.0 |
| night | 3.41458394828345e-2 | 1.0 |
| gonna | 8.118584492034046e-2 | 2.0 |
| money | 5.1736711600637544e-2 | 2.0 |
| shit | 4.620430294274594e-2 | 2.0 |
| fuck | 3.99843125556081e-2 | 2.0 |
| kill | 3.672740843080258e-2 | 2.0 |
| people | 2.0091372023286612e-2 | 3.0 |
| know | 1.8613400462887356e-2 | 3.0 |
| work | 1.6775643603287843e-2 | 3.0 |
| does | 1.5522555458447744e-2 | 3.0 |
| think | 1.2161168331925723e-2 | 3.0 |
| know | 3.1956573561538214e-2 | 4.0 |
| just | 3.0674598809934856e-2 | 4.0 |
| want | 2.7663491240851962e-2 | 4.0 |
| tell | 2.5727217382788027e-2 | 4.0 |
| right | 2.300853167338119e-2 | 4.0 |
| love | 5.932570200934131e-2 | 5.0 |
| father | 3.0080735900045442e-2 | 5.0 |
| life | 1.769248067468245e-2 | 5.0 |
| true | 1.6281752071881345e-2 | 5.0 |
| young | 1.4927950883812253e-2 | 5.0 |
| remember | 3.998401809663685e-2 | 6.0 |
| went | 1.737965538107633e-2 | 6.0 |
| lost | 1.6916065536574213e-2 | 6.0 |
| called | 1.6443441316683228e-2 | 6.0 |
| story | 1.4849882671062261e-2 | 6.0 |
| house | 2.8911209424810257e-2 | 7.0 |
| miss | 2.5669944694943093e-2 | 7.0 |
| right | 2.091105252727788e-2 | 7.0 |
| family | 1.7862939987512365e-2 | 7.0 |
| important | 1.3959164390834044e-2 | 7.0 |
| saying | 2.2939827090645636e-2 | 8.0 |
| know | 2.1335083902970984e-2 | 8.0 |
| idea | 1.7628999871937747e-2 | 8.0 |
| business | 1.7302568063786224e-2 | 8.0 |
| police | 1.2284217866942303e-2 | 8.0 |
| know | 5.1876601466269136e-2 | 9.0 |
| like | 3.828159069993671e-2 | 9.0 |
| maybe | 3.754385940676905e-2 | 9.0 |
| just | 3.1938551661426284e-2 | 9.0 |
| want | 2.876693222824349e-2 | 9.0 |
| years | 3.2537676027398765e-2 | 10.0 |
| going | 3.0596831997667568e-2 | 10.0 |
| case | 2.049555392502822e-2 | 10.0 |
| doctor | 1.8671171294737107e-2 | 10.0 |
| working | 1.7672067172167016e-2 | 10.0 |
| stuff | 2.236582778896705e-2 | 11.0 |
| school | 2.0057798194969816e-2 | 11.0 |
| john | 1.7134198006217606e-2 | 11.0 |
| week | 1.7075852415410653e-2 | 11.0 |
| thousand | 1.7013413435021035e-2 | 11.0 |
| little | 8.663446368316245e-2 | 12.0 |
| girl | 3.5120377589734936e-2 | 12.0 |
| like | 2.992080326340266e-2 | 12.0 |
| woman | 2.40813719635157e-2 | 12.0 |
| baby | 2.2471517953608963e-2 | 12.0 |
| know | 2.83115823590395e-2 | 13.0 |
| leave | 2.744935904744228e-2 | 13.0 |
| time | 2.050833156294194e-2 | 13.0 |
| want | 2.0124145131863225e-2 | 13.0 |
| just | 1.9466336438890477e-2 | 13.0 |
| didn | 8.220031921979461e-2 | 14.0 |
| like | 5.062323326717784e-2 | 14.0 |
| real | 3.087838046777391e-2 | 14.0 |
| guess | 2.452989702353384e-2 | 14.0 |
| says | 2.2815035397008333e-2 | 14.0 |
| minutes | 1.8541518543996716e-2 | 15.0 |
| time | 1.4737962244588431e-2 | 15.0 |
| captain | 1.2594614743931537e-2 | 15.0 |
| thirty | 1.193707771669708e-2 | 15.0 |
| ship | 1.1260576815409516e-2 | 15.0 |
| okay | 8.153575328080886e-2 | 16.0 |
| just | 5.0004142902999975e-2 | 16.0 |
| right | 3.438984898476042e-2 | 16.0 |
| know | 2.821327795933634e-2 | 16.0 |
| home | 2.3397063860326372e-2 | 16.0 |
| country | 1.1270500385627474e-2 | 17.0 |
| power | 1.0428408353623762e-2 | 17.0 |
| president | 9.392162067926028e-3 | 17.0 |
| fight | 7.99742811584178e-3 | 17.0 |
| possible | 7.597974486019279e-3 | 17.0 |
| know | 9.541058020800194e-2 | 18.0 |
| think | 6.98707939786508e-2 | 18.0 |
| really | 6.881812755565207e-2 | 18.0 |
| mean | 2.909700228968688e-2 | 18.0 |
| just | 2.8699687473471538e-2 | 18.0 |
| dead | 3.833642117149438e-2 | 19.0 |
| like | 1.7873711992106994e-2 | 19.0 |
| hand | 1.5280854355409379e-2 | 19.0 |
| white | 1.3718491413582671e-2 | 19.0 |
| blood | 1.2699265888344448e-2 | 19.0 |
We will convert the DataFrame into a JSON format, which will be passed into d3.
// Create JSON data
val rawJson = termDF.toJSON.collect().mkString(",\n")
rawJson: String =
{"term":"just","probability":0.030515134931284552,"topicId":0},
{"term":"like","probability":0.02463563559747823,"topicId":0},
{"term":"want","probability":0.022529385381465025,"topicId":0},
{"term":"damn","probability":0.02094828832824297,"topicId":0},
{"term":"going","probability":0.0203407289886203,"topicId":0},
{"term":"yeah","probability":0.10787301090151602,"topicId":1},
{"term":"look","probability":0.0756831002291994,"topicId":1},
{"term":"know","probability":0.04815746564274915,"topicId":1},
{"term":"wait","probability":0.03897182014529944,"topicId":1},
{"term":"night","probability":0.0341458394828345,"topicId":1},
{"term":"gonna","probability":0.08118584492034046,"topicId":2},
{"term":"money","probability":0.051736711600637544,"topicId":2},
{"term":"shit","probability":0.04620430294274594,"topicId":2},
{"term":"fuck","probability":0.0399843125556081,"topicId":2},
{"term":"kill","probability":0.03672740843080258,"topicId":2},
{"term":"people","probability":0.020091372023286612,"topicId":3},
{"term":"know","probability":0.018613400462887356,"topicId":3},
{"term":"work","probability":0.016775643603287843,"topicId":3},
{"term":"does","probability":0.015522555458447744,"topicId":3},
{"term":"think","probability":0.012161168331925723,"topicId":3},
{"term":"know","probability":0.031956573561538214,"topicId":4},
{"term":"just","probability":0.030674598809934856,"topicId":4},
{"term":"want","probability":0.027663491240851962,"topicId":4},
{"term":"tell","probability":0.025727217382788027,"topicId":4},
{"term":"right","probability":0.02300853167338119,"topicId":4},
{"term":"love","probability":0.05932570200934131,"topicId":5},
{"term":"father","probability":0.030080735900045442,"topicId":5},
{"term":"life","probability":0.01769248067468245,"topicId":5},
{"term":"true","probability":0.016281752071881345,"topicId":5},
{"term":"young","probability":0.014927950883812253,"topicId":5},
{"term":"remember","probability":0.03998401809663685,"topicId":6},
{"term":"went","probability":0.01737965538107633,"topicId":6},
{"term":"lost","probability":0.016916065536574213,"topicId":6},
{"term":"called","probability":0.016443441316683228,"topicId":6},
{"term":"story","probability":0.014849882671062261,"topicId":6},
{"term":"house","probability":0.028911209424810257,"topicId":7},
{"term":"miss","probability":0.025669944694943093,"topicId":7},
{"term":"right","probability":0.02091105252727788,"topicId":7},
{"term":"family","probability":0.017862939987512365,"topicId":7},
{"term":"important","probability":0.013959164390834044,"topicId":7},
{"term":"saying","probability":0.022939827090645636,"topicId":8},
{"term":"know","probability":0.021335083902970984,"topicId":8},
{"term":"idea","probability":0.017628999871937747,"topicId":8},
{"term":"business","probability":0.017302568063786224,"topicId":8},
{"term":"police","probability":0.012284217866942303,"topicId":8},
{"term":"know","probability":0.051876601466269136,"topicId":9},
{"term":"like","probability":0.03828159069993671,"topicId":9},
{"term":"maybe","probability":0.03754385940676905,"topicId":9},
{"term":"just","probability":0.031938551661426284,"topicId":9},
{"term":"want","probability":0.02876693222824349,"topicId":9},
{"term":"years","probability":0.032537676027398765,"topicId":10},
{"term":"going","probability":0.030596831997667568,"topicId":10},
{"term":"case","probability":0.02049555392502822,"topicId":10},
{"term":"doctor","probability":0.018671171294737107,"topicId":10},
{"term":"working","probability":0.017672067172167016,"topicId":10},
{"term":"stuff","probability":0.02236582778896705,"topicId":11},
{"term":"school","probability":0.020057798194969816,"topicId":11},
{"term":"john","probability":0.017134198006217606,"topicId":11},
{"term":"week","probability":0.017075852415410653,"topicId":11},
{"term":"thousand","probability":0.017013413435021035,"topicId":11},
{"term":"little","probability":0.08663446368316245,"topicId":12},
{"term":"girl","probability":0.035120377589734936,"topicId":12},
{"term":"like","probability":0.02992080326340266,"topicId":12},
{"term":"woman","probability":0.0240813719635157,"topicId":12},
{"term":"baby","probability":0.022471517953608963,"topicId":12},
{"term":"know","probability":0.0283115823590395,"topicId":13},
{"term":"leave","probability":0.02744935904744228,"topicId":13},
{"term":"time","probability":0.02050833156294194,"topicId":13},
{"term":"want","probability":0.020124145131863225,"topicId":13},
{"term":"just","probability":0.019466336438890477,"topicId":13},
{"term":"didn","probability":0.08220031921979461,"topicId":14},
{"term":"like","probability":0.05062323326717784,"topicId":14},
{"term":"real","probability":0.03087838046777391,"topicId":14},
{"term":"guess","probability":0.02452989702353384,"topicId":14},
{"term":"says","probability":0.022815035397008333,"topicId":14},
{"term":"minutes","probability":0.018541518543996716,"topicId":15},
{"term":"time","probability":0.014737962244588431,"topicId":15},
{"term":"captain","probability":0.012594614743931537,"topicId":15},
{"term":"thirty","probability":0.01193707771669708,"topicId":15},
{"term":"ship","probability":0.011260576815409516,"topicId":15},
{"term":"okay","probability":0.08153575328080886,"topicId":16},
{"term":"just","probability":0.050004142902999975,"topicId":16},
{"term":"right","probability":0.03438984898476042,"topicId":16},
{"term":"know","probability":0.02821327795933634,"topicId":16},
{"term":"home","probability":0.023397063860326372,"topicId":16},
{"term":"country","probability":0.011270500385627474,"topicId":17},
{"term":"power","probability":0.010428408353623762,"topicId":17},
{"term":"president","probability":0.009392162067926028,"topicId":17},
{"term":"fight","probability":0.00799742811584178,"topicId":17},
{"term":"possible","probability":0.007597974486019279,"topicId":17},
{"term":"know","probability":0.09541058020800194,"topicId":18},
{"term":"think","probability":0.0698707939786508,"topicId":18},
{"term":"really","probability":0.06881812755565207,"topicId":18},
{"term":"mean","probability":0.02909700228968688,"topicId":18},
{"term":"just","probability":0.028699687473471538,"topicId":18},
{"term":"dead","probability":0.03833642117149438,"topicId":19},
{"term":"like","probability":0.017873711992106994,"topicId":19},
{"term":"hand","probability":0.015280854355409379,"topicId":19},
{"term":"white","probability":0.013718491413582671,"topicId":19},
{"term":"blood","probability":0.012699265888344448,"topicId":19}
We are now ready to use D3 on the rawJson data.
Step 1. Downloading and Loading Data into DBFS
Here are the steps taken for downloading and saving data to the distributed file system. Uncomment them for repeating this process on your databricks cluster or for downloading a new source of data.
Unfortunately, the original data at:
is not suited for manipulation and loading into dbfs easily. So the data has been downloaded, directory renamed without white spaces, superfluous OS-specific files removed, dos2unix'd, tar -zcvf'd and uploaded to the following URL for an easily dbfs-loadable download:
wget http://lamastex.org/datasets/public/nlp/cornell_movie_dialogs_corpus.tgz
--2019-05-17 15:34:09-- http://lamastex.org/datasets/public/nlp/cornell_movie_dialogs_corpus.tgz
Resolving lamastex.org (lamastex.org)... 166.62.28.100
Connecting to lamastex.org (lamastex.org)|166.62.28.100|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 9914415 (9.5M) [application/x-tar]
Saving to: ‘cornell_movie_dialogs_corpus.tgz’
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2019-05-17 15:34:11 (4.61 MB/s) - ‘cornell_movie_dialogs_corpus.tgz’ saved [9914415/9914415]
Untar the file.
tar zxvf cornell_movie_dialogs_corpus.tgz
cornell_movie_dialogs_corpus/
cornell_movie_dialogs_corpus/movie_lines.txt
cornell_movie_dialogs_corpus/movie_characters_metadata.txt
cornell_movie_dialogs_corpus/README.txt
cornell_movie_dialogs_corpus/raw_script_urls.txt
cornell_movie_dialogs_corpus/movie_titles_metadata.txt
cornell_movie_dialogs_corpus/movie_conversations.txt
cornell_movie_dialogs_corpus/chameleons.pdf
Let us list and load all the files into dbfs after dbfs.fs.mkdirs(...) to create the directory dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/.
pwd && ls -al cornell_movie_dialogs_corpus
/databricks/driver
total 41552
drwxr-xr-x 2 ubuntu ubuntu 4096 Jan 11 2017 .
drwxr-xr-x 1 root root 4096 May 17 15:34 ..
-rw-r--r-- 1 ubuntu ubuntu 290691 May 9 2011 chameleons.pdf
-rw-r--r-- 1 ubuntu ubuntu 705695 Jan 11 2017 movie_characters_metadata.txt
-rw-r--r-- 1 ubuntu ubuntu 6760930 Jan 11 2017 movie_conversations.txt
-rw-r--r-- 1 ubuntu ubuntu 34641919 Jan 11 2017 movie_lines.txt
-rw-r--r-- 1 ubuntu ubuntu 67289 Jan 11 2017 movie_titles_metadata.txt
-rw-r--r-- 1 ubuntu ubuntu 56177 Jan 11 2017 raw_script_urls.txt
-rw-r--r-- 1 ubuntu ubuntu 4181 Jan 11 2017 README.txt
dbutils.fs.rm("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/",true)
res4: Boolean = true
dbutils.fs.mkdirs("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/")
res5: Boolean = true
dbutils.fs.cp("file:///databricks/driver/cornell_movie_dialogs_corpus/movie_characters_metadata.txt","dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_characters_metadata.txt")
dbutils.fs.cp("file:///databricks/driver/cornell_movie_dialogs_corpus/movie_conversations.txt","dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_conversations.txt")
dbutils.fs.cp("file:///databricks/driver/cornell_movie_dialogs_corpus/movie_lines.txt","dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_lines.txt")
dbutils.fs.cp("file:///databricks/driver/cornell_movie_dialogs_corpus/movie_titles_metadata.txt","dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_titles_metadata.txt")
dbutils.fs.cp("file:///databricks/driver/cornell_movie_dialogs_corpus/raw_script_urls.txt","dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/raw_script_urls.txt")
dbutils.fs.cp("file:///databricks/driver/cornell_movie_dialogs_corpus/README.txt","dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/README.txt")
res6: Boolean = true
display(dbutils.fs.ls("dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/"))
| path | name | size |
|---|---|---|
| dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/README.txt | README.txt | 4181.0 |
| dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_characters_metadata.txt | movie_characters_metadata.txt | 705695.0 |
| dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_conversations.txt | movie_conversations.txt | 6760930.0 |
| dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_lines.txt | movie_lines.txt | 3.4641919e7 |
| dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/movie_titles_metadata.txt | movie_titles_metadata.txt | 67289.0 |
| dbfs:/datasets/sds/nlp/cornell_movie_dialogs_corpus/raw_script_urls.txt | raw_script_urls.txt | 56177.0 |
Movie Recommender using Alternating Least Squares
Most of the content below is just a scarification of from Module 5 of Anthony Joseph's edX course CS100-1x from the Community Edition of databricks.
SOURCE: Module 5 of AJ's course.
Collaborative Filtering
(watch now 61 seconds - from 18 to 79 seconds):
Let us use MLlib to make personalized movie recommendations.
We are going to use a technique called collaborative filtering. Collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue x than to have the opinion on x of a person chosen randomly. You can read more about collaborative filtering here.
The image below (from Wikipedia) shows an example of predicting of the user's rating using collaborative filtering. At first, people rate different items (like videos, images, games). After that, the system is making predictions about a user's rating for an item, which the user has not rated yet. These predictions are built upon the existing ratings of other users, who have similar ratings with the active user. For instance, in the image below the system has made a prediction, that the active user will not like the video.

Resources:
For movie recommendations, we start with a matrix whose entries are movie ratings by users (shown in red in the diagram below). Each row represents a user and each column represents a particular movie. Thus the entry \(r_{ij}\) represents the rating of user \(i\) for movie \(j\).
Since not all users have rated all movies, we do not know all of the entries in this matrix, which is precisely why we need collaborative filtering. For each user, we have ratings for only a subset of the movies. With collaborative filtering, the idea is to approximate the ratings matrix by factorizing it as the product of two matrices: one that describes properties of each user (shown in green), and one that describes properties of each movie (shown in blue).

We want to select these two matrices such that the error for the users/movie pairs where we know the correct ratings is minimized. The Alternating Least Squares algorithm expands on the least squares method by:
- first randomly filling the users matrix with values and then
- optimizing the value of the movies such that the error is minimized.
- Then, it holds the movies matrix constant and optimizes the value of the users matrix.
This alternation between which matrix to optimize is the reason for the "alternating" in the name.
This optimization is what's being shown on the right in the image above. Given a fixed set of user factors (i.e., values in the users matrix), we use the known ratings to find the best values for the movie factors using the optimization written at the bottom of the figure. Then we "alternate" and pick the best user factors given fixed movie factors.
For a simple example of what the users and movies matrices might look like, check out the videos from Lecture 8 or the slides from Lecture 8 of AJ's Introduction to Data Science course.
display(dbutils.fs.ls("/databricks-datasets/cs100/lab4/data-001/")) // The data is already here
| path | name | size |
|---|---|---|
| dbfs:/databricks-datasets/cs100/lab4/data-001/movies.dat | movies.dat | 171308.0 |
| dbfs:/databricks-datasets/cs100/lab4/data-001/ratings.dat.gz | ratings.dat.gz | 2837683.0 |
Import
Let us import the relevant libraries for mllib.
import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
import org.apache.spark.mllib.recommendation.Rating
import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
import org.apache.spark.mllib.recommendation.Rating
Preliminaries
We read in each of the files and create an RDD consisting of parsed lines. Each line in the ratings dataset (ratings.dat.gz) is formatted as: UserID::MovieID::Rating::Timestamp Each line in the movies (movies.dat) dataset is formatted as: MovieID::Title::Genres The Genres field has the format Genres1|Genres2|Genres3|... The format of these files is uniform and simple, so we can use split().
Parsing the two files yields two RDDs
- For each line in the ratings dataset, we create a tuple of (UserID, MovieID, Rating). We drop the timestamp because we do not need it for this exercise.
- For each line in the movies dataset, we create a tuple of (MovieID, Title). We drop the Genres because we do not need them for this exercise.
// take a peek at what's in the rating file
sc.textFile("/databricks-datasets/cs100/lab4/data-001/ratings.dat.gz").map { line => line.split("::") }.take(5)
res1: Array[Array[String]] = Array(Array(1, 1193, 5, 978300760), Array(1, 661, 3, 978302109), Array(1, 914, 3, 978301968), Array(1, 3408, 4, 978300275), Array(1, 2355, 5, 978824291))
val timedRatingsRDD = sc.textFile("/databricks-datasets/cs100/lab4/data-001/ratings.dat.gz").map { line =>
val fields = line.split("::")
// format: (timestamp % 10, Rating(userId, movieId, rating))
(fields(3).toLong % 10, Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble))
}
timedRatingsRDD: org.apache.spark.rdd.RDD[(Long, org.apache.spark.mllib.recommendation.Rating)] = MapPartitionsRDD[47324] at map at command-2972105651607143:1
We have a look at the first 10 entries in the Ratings RDD to check it's ok
timedRatingsRDD.take(10).map(println)
(0,Rating(1,1193,5.0))
(9,Rating(1,661,3.0))
(8,Rating(1,914,3.0))
(5,Rating(1,3408,4.0))
(1,Rating(1,2355,5.0))
(8,Rating(1,1197,3.0))
(9,Rating(1,1287,5.0))
(9,Rating(1,2804,5.0))
(8,Rating(1,594,4.0))
(8,Rating(1,919,4.0))
res2: Array[Unit] = Array((), (), (), (), (), (), (), (), (), ())
The timestamp is unused here so we want to remove it
val ratingsRDD = sc.textFile("/databricks-datasets/cs100/lab4/data-001/ratings.dat.gz").map { line =>
val fields = line.split("::")
// format: Rating(userId, movieId, rating)
Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble)
}
ratingsRDD: org.apache.spark.rdd.RDD[org.apache.spark.mllib.recommendation.Rating] = MapPartitionsRDD[47327] at map at command-2972105651607147:1
Now our final ratings RDD looks as follows:
ratingsRDD.take(10).map(println)
Rating(1,1193,5.0)
Rating(1,661,3.0)
Rating(1,914,3.0)
Rating(1,3408,4.0)
Rating(1,2355,5.0)
Rating(1,1197,3.0)
Rating(1,1287,5.0)
Rating(1,2804,5.0)
Rating(1,594,4.0)
Rating(1,919,4.0)
res3: Array[Unit] = Array((), (), (), (), (), (), (), (), (), ())
A similar command is used to format the movies. We ignore the genres in this recommender
val movies = sc.textFile("/databricks-datasets/cs100/lab4/data-001/movies.dat").map { line =>
val fields = line.split("::")
// format: (movieId, movieName)
(fields(0).toInt, fields(1))
}.collect.toMap
movies: scala.collection.immutable.Map[Int,String] = Map(2163 -> Attack of the Killer Tomatoes! (1980), 645 -> Nelly & Monsieur Arnaud (1995), 892 -> Twelfth Night (1996), 69 -> Friday (1995), 2199 -> Phoenix (1998), 3021 -> Funhouse, The (1981), 1322 -> Amityville 1992: It's About Time (1992), 1665 -> Bean (1997), 1036 -> Die Hard (1988), 2822 -> Medicine Man (1992), 2630 -> Besieged (L' Assedio) (1998), 3873 -> Cat Ballou (1965), 1586 -> G.I. Jane (1997), 1501 -> Keys to Tulsa (1997), 2452 -> Gate II: Trespassers, The (1990), 809 -> Fled (1996), 1879 -> Hanging Garden, The (1997), 1337 -> Body Snatcher, The (1945), 1718 -> Stranger in the House (1997), 2094 -> Rocketeer, The (1991), 3944 -> Bootmen (2000), 1411 -> Hamlet (1996), 629 -> Rude (1995), 3883 -> Catfish in Black Bean Sauce (2000), 2612 -> Mildred Pierce (1945), 1024 -> Three Caballeros, The (1945), 365 -> Little Buddha (1993), 2744 -> Otello (1986), 1369 -> I Can't Sleep (J'ai pas sommeil) (1994), 138 -> Neon Bible, The (1995), 2889 -> Mystery, Alaska (1999), 1190 -> Tie Me Up! Tie Me Down! (1990), 1168 -> Bad Moon (1996), 2295 -> Impostors, The (1998), 2306 -> Holy Man (1998), 3053 -> Messenger: The Story of Joan of Arc, The (1999), 3345 -> Charlie, the Lonesome Cougar (1967), 760 -> Stalingrad (1993), 2341 -> Dancing at Lughnasa (1998), 101 -> Bottle Rocket (1996), 2336 -> Elizabeth (1998), 3008 -> Last Night (1998), 2109 -> Jerk, The (1979), 2131 -> Autumn Sonata (H�stsonaten ) (1978), 1454 -> SubUrbia (1997), 2031 -> $1,000,000 Duck (1971), 1633 -> Ulee's Gold (1997), 2778 -> Never Talk to Strangers (1995), 2072 -> 'burbs, The (1989), 3661 -> Puppet Master II (1990), 1767 -> Music From Another Room (1998), 3399 -> Sesame Street Presents Follow That Bird (1985), 1995 -> Poltergeist II: The Other Side (1986), 2263 -> Seventh Sign, The (1988), 3930 -> Creature From the Black Lagoon, The (1954), 479 -> Judgment Night (1993), 3798 -> What Lies Beneath (2000), 1559 -> Next Step, The (1995), 1105 -> Children of the Corn IV: The Gathering (1996), 347 -> Bitter Moon (1992), 3666 -> Retro Puppetmaster (1999), 1729 -> Jackie Brown (1997), 3434 -> Death Wish V: The Face of Death (1994), 3167 -> Carnal Knowledge (1971), 2412 -> Rocky V (1990), 2876 -> Thumbelina (1994), 1237 -> Seventh Seal, The (Sjunde inseglet, Det) (1957), 846 -> Flirt (1995), 909 -> Apartment, The (1960), 2921 -> High Plains Drifter (1972), 3477 -> Empire Records (1995), 3698 -> Running Man, The (1987), 333 -> Tommy Boy (1995), 628 -> Primal Fear (1996), 1031 -> Bedknobs and Broomsticks (1971), 249 -> Immortal Beloved (1994), 2463 -> Ruthless People (1986), 3397 -> Great Muppet Caper, The (1981), 1899 -> Passion in the Desert (1998), 893 -> Mother Night (1996), 1840 -> He Got Game (1998), 3581 -> Human Traffic (1999), 1315 -> Paris Was a Woman (1995), 3863 -> Cell, The (2000), 3830 -> Psycho Beach Party (2000), 2787 -> Cat's Eye (1985), 2595 -> Photographer (Fotoamator) (1998), 518 -> Road to Wellville, The (1994), 1850 -> I Love You, Don't Touch Me! (1998), 2499 -> God Said 'Ha!' (1998), 2427 -> Thin Red Line, The (1998), 2480 -> Dry Cleaning (Nettoyage � sec) (1997), 1083 -> Great Race, The (1965), 962 -> They Made Me a Criminal (1939), 1982 -> Halloween (1978), 468 -> Englishman Who Went Up a Hill, But Came Down a Mountain, The (1995), 2559 -> King and I, The (1999), 3449 -> Good Mother, The (1988), 234 -> Exit to Eden (1994), 2544 -> School of Flesh, The (L' �cole de la chair) (1998), 941 -> Mark of Zorro, The (1940), 3927 -> Fantastic Voyage (1966), 1179 -> Grifters, The (1990), 2331 -> Living Out Loud (1998), 777 -> Pharaoh's Army (1995), 3566 -> Big Kahuna, The (2000), 555 -> True Romance (1993), 666 -> All Things Fair (1996), 1295 -> Unbearable Lightness of Being, The (1988), 1956 -> Ordinary People (1980), 1950 -> In the Heat of the Night (1967), 88 -> Black Sheep (1996), 1549 -> Rough Magic (1995), 2280 -> Clay Pigeons (1998), 1554 -> Pillow Book, The (1995), 1110 -> Bird of Prey (1996), 3172 -> Ulysses (Ulisse) (1954), 1686 -> Red Corner (1997), 481 -> Kalifornia (1993), 352 -> Crooklyn (1994), 2250 -> Men Don't Leave (1990), 2363 -> Godzilla (Gojira) (1954), 1855 -> Krippendorf's Tribe (1998), 3680 -> Decline of Western Civilization Part II: The Metal Years, The (1988), 1200 -> Aliens (1986), 2077 -> Journey of Natty Gann, The (1985), 3534 -> 28 Days (2000), 1750 -> Star Kid (1997), 3185 -> Snow Falling on Cedars (1999), 408 -> 8 Seconds (1994), 977 -> Moonlight Murder (1936), 170 -> Hackers (1995), 3681 -> For a Few Dollars More (1965), 1211 -> Wings of Desire (Der Himmel �ber Berlin) (1987), 523 -> Ruby in Paradise (1993), 1158 -> Here Comes Cookie (1935), 2309 -> Inheritors, The (Die Siebtelbauern) (1998), 2512 -> Ballad of Narayama, The (Narayama Bushiko) (1982), 582 -> Metisse (Caf� au Lait) (1993), 2976 -> Bringing Out the Dead (1999), 762 -> Striptease (1996), 3072 -> Moonstruck (1987), 1924 -> Plan 9 from Outer Space (1958), 1005 -> D3: The Mighty Ducks (1996), 2210 -> Sabotage (1936), 2117 -> Nineteen Eighty-Four (1984), 2940 -> Gilda (1946), 1596 -> Career Girls (1997), 1406 -> C�r�monie, La (1995), 115 -> Happiness Is in the Field (1995), 2104 -> Tex (1982), 3317 -> Wonder Boys (2000), 683 -> Eye of Vichy, The (Oeil de Vichy, L') (1993), 730 -> Low Life, The (1994), 1290 -> Some Kind of Wonderful (1987), 1882 -> Godzilla (1998), 217 -> Babysitter, The (1995), 276 -> Milk Money (1994), 2231 -> Rounders (1998), 2622 -> Midsummer Night's Dream, A (1999), 1068 -> Crossfire (1947), 3858 -> Cecil B. Demented (2000), 2808 -> Universal Soldier (1992), 3905 -> Specials, The (2000), 1522 -> Ripe (1996), 3762 -> Daughter of Dr. Jeckyll (1957), 2644 -> Dracula (1931), 3813 -> Interiors (1978), 2495 -> Fantastic Planet, The (La Plan�te sauvage) (1973), 3230 -> Odessa File, The (1974), 2381 -> Police Academy 4: Citizens on Patrol (1987), 2395 -> Rushmore (1998), 2908 -> Boys Don't Cry (1999), 2062 -> Governess, The (1998), 3549 -> Guys and Dolls (1955), 1443 -> Tickle in the Heart, A (1996), 2776 -> Marcello Mastroianni: I Remember Yes, I Remember (1997), 2659 -> It Came from Hollywood (1982), 3417 -> Crimson Pirate, The (1952), 3509 -> Black and White (1999), 3285 -> Beach, The (2000), 3377 -> Hangmen Also Die (1943), 994 -> Big Night (1996), 2527 -> Westworld (1973), 3040 -> Meatballs (1979), 3153 -> 7th Voyage of Sinbad, The (1958), 2953 -> Home Alone 2: Lost in New York (1992), 2590 -> Hideous Kinky (1998), 1401 -> Ghosts of Mississippi (1996), 3460 -> Hillbillys in a Haunted House (1967), 1422 -> Murder at 1600 (1997), 308 -> Three Colors: White (1994), 2947 -> Goldfinger (1964), 1569 -> My Best Friend's Wedding (1997), 1939 -> Best Years of Our Lives, The (1946), 2248 -> Say Anything... (1989), 3912 -> Beautiful (2000), 3098 -> Natural, The (1984), 741 -> Ghost in the Shell (Kokaku kidotai) (1995), 1073 -> Willy Wonka and the Chocolate Factory (1971), 2671 -> Notting Hill (1999), 1544 -> Lost World: Jurassic Park, The (1997), 2676 -> Instinct (1999), 2014 -> Freaky Friday (1977), 3910 -> Dancer in the Dark (2000), 5 -> Father of the Bride Part II (1995), 1728 -> Winter Guest, The (1997), 3108 -> Fisher King, The (1991), 873 -> Shadow of Angels (Schatten der Engel) (1976), 3012 -> Battling Butler (1926), 1205 -> Transformers: The Movie, The (1986), 449 -> Fear of a Black Hat (1993), 120 -> Race the Sun (1996), 2099 -> Song of the South (1946), 2282 -> Pecker (1998), 247 -> Heavenly Creatures (1994), 1591 -> Spawn (1997), 2114 -> Outsiders, The (1983), 2837 -> Bedrooms & Hallways (1998), 1142 -> Get Over It (1996), 379 -> Timecop (1994), 1269 -> Arsenic and Old Lace (1944), 878 -> Bye-Bye (1995), 440 -> Dave (1993), 655 -> Mutters Courage (1995), 3498 -> Midnight Express (1978), 511 -> Program, The (1993), 2380 -> Police Academy 3: Back in Training (1986), 1971 -> Nightmare on Elm Street 4: The Dream Master, A (1988), 1793 -> Welcome to Woop-Woop (1997), 3402 -> Turtle Diary (1985), 2854 -> Don't Look in the Basement! (1973), 1533 -> Promise, The (La Promesse) (1996), 614 -> Loaded (1994), 1692 -> Alien Escape (1995), 269 -> My Crazy Life (Mi vida loca) (1993), 1305 -> Paris, Texas (1984), 202 -> Total Eclipse (1995), 597 -> Pretty Woman (1990), 1437 -> Cement Garden, The (1993), 1041 -> Secrets & Lies (1996), 861 -> Supercop (1992), 3382 -> Song of Freedom (1936), 1173 -> Cook the Thief His Wife & Her Lover, The (1989), 1486 -> Quiet Room, The (1996), 3848 -> Silent Fall (1994), 1497 -> Double Team (1997), 10 -> GoldenEye (1995), 2195 -> Dirty Work (1998), 1705 -> Guy (1996), 3017 -> Creepshow 2 (1987), 1078 -> Bananas (1971), 1788 -> Men With Guns (1997), 1426 -> Zeus and Roxanne (1997), 3217 -> Star Is Born, A (1937), 3530 -> Smoking/No Smoking (1993), 1671 -> Deceiver (1997), 3794 -> Chuck & Buck (2000), 1608 -> Air Force One (1997), 3439 -> Teenage Mutant Ninja Turtles II: The Secret of the Ooze (1991), 385 -> Man of No Importance, A (1994), 384 -> Bad Company (1995), 56 -> Kids of the Round Table (1995), 1655 -> Phantoms (1998), 3120 -> Distinguished Gentleman, The (1992), 3429 -> Creature Comforts (1990), 3049 -> How I Won the War (1967), 3745 -> Titan A.E. (2000), 1137 -> Hustler White (1996), 2607 -> Get Real (1998), 2686 -> Red Violin, The (Le Violon rouge) (1998), 1756 -> Prophecy II, The (1998), 1310 -> Hype! (1996), 533 -> Shadow, The (1994), 3004 -> Bachelor, The (1999), 2035 -> Blackbeard's Ghost (1968), 3332 -> Legend of Lobo, The (1962), 3367 -> Devil's Brigade, The (1968), 3648 -> Abominable Snowman, The (1957), 3135 -> Great Santini, The (1979), 3942 -> Sorority House Massacre II (1990), 550 -> Threesome (1994), 3649 -> American Gigolo (1980), 3365 -> Searchers, The (1956), 142 -> Shadows (Cienie) (1988), 2740 -> Kindred, The (1986), 2918 -> Ferris Bueller's Day Off (1986), 3044 -> Dead Again (1991), 1735 -> Great Expectations (1998), 1867 -> Tarzan and the Lost City (1998), 500 -> Mrs. Doubtfire (1993), 2184 -> Trouble with Harry, The (1955), 3175 -> Galaxy Quest (1999), 1164 -> Two or Three Things I Know About Her (1966), 1999 -> Exorcist III, The (1990), 797 -> Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991), 2316 -> Practical Magic (1998), 3446 -> Funny Bones (1995), 715 -> Horseman on the Roof, The (Hussard sur le toit, Le) (1995), 2448 -> Virus (1999), 3338 -> For All Mankind (1989), 2933 -> Fire Within, The (Le Feu Follet) (1963), 1275 -> Highlander (1986), 2141 -> American Tail, An (1986), 2434 -> Down in the Delta (1998), 1872 -> Go Now (1995), 2712 -> Eyes Wide Shut (1999), 472 -> I'll Do Anything (1994), 3616 -> Loser (2000), 1233 -> Boat, The (Das Boot) (1981), 3781 -> Shaft in Africa (1973), 814 -> Boy Called Hate, A (1995), 1327 -> Amityville Horror, The (1979), 3693 -> Toxic Avenger, The (1985), 2476 -> Heartbreak Ridge (1986), 2627 -> Endurance (1998), 2168 -> Dance with Me (1998), 1260 -> M (1931), 698 -> Delta of Venus (1994), 1919 -> Madeline (1998), 3253 -> Wayne's World (1992), 1988 -> Hello Mary Lou: Prom Night II (1987), 3826 -> Hollow Man (2000), 2459 -> Texas Chainsaw Massacre, The (1974), 3414 -> Love Is a Many-Splendored Thing (1955), 1342 -> Candyman (1992), 3121 -> Hitch-Hiker, The (1953), 747 -> Stupids, The (1996), 913 -> Maltese Falcon, The (1941), 3481 -> High Fidelity (2000), 3562 -> Committed (2000), 1640 -> How to Be a Player (1997), 2580 -> Go (1999), 2901 -> Phantasm (1979), 945 -> Top Hat (1935), 3313 -> Class Reunion (1982), 1063 -> Johns (1996), 1954 -> Rocky (1976), 2844 -> Minus Man, The (1999), 340 -> War, The (1994), 3577 -> Two Moon Juction (1988), 2042 -> D2: The Mighty Ducks (1994), 538 -> Six Degrees of Separation (1993), 1354 -> Breaking the Waves (1996), 153 -> Batman Forever (1995), 2146 -> St. Elmo's Fire (1985), 1507 -> Paradise Road (1997), 1222 -> Full Metal Jacket (1987), 930 -> Notorious (1946), 3895 -> Watcher, The (2000), 3717 -> Gone in 60 Seconds (2000), 2360 -> Celebration, The (Festen) (1998), 1458 -> Touch (1997), 670 -> World of Apu, The (Apur Sansar) (1959), 3276 -> Gun Shy (2000), 2575 -> Dreamlife of Angels, The (La Vie r�v�e des anges) (1998), 829 -> Joe's Apartment (1996), 3370 -> Betrayed (1988), 174 -> Jury Duty (1995), 1095 -> Glengarry Glen Ross (1992), 404 -> Brother Minister: The Assassination of Malcolm X (1994), 3335 -> Jail Bait (1954), 1196 -> Star Wars: Episode V - The Empire Strikes Back (1980), 1746 -> Senseless (1998), 3103 -> Stanley & Iris (1990), 898 -> Philadelphia Story, The (1940), 185 -> Net, The (1995), 1835 -> City of Angels (1998), 3836 -> Kelly's Heroes (1970), 2216 -> Skin Game, The (1931), 3236 -> Zachariah (1971), 3629 -> Gold Rush, The (1925), 2348 -> Sid and Nancy (1986), 3350 -> Raisin in the Sun, A (1961), 1001 -> Associate, The (L'Associe)(1982), 3545 -> Cabaret (1972), 2723 -> Mystery Men (1999), 2972 -> Red Sorghum (Hong Gao Liang) (1987), 3634 -> Seven Days in May (1964), 2046 -> Flight of the Navigator (1986), 3766 -> Missing in Action (1984), 2432 -> Stepmom (1998), 1914 -> Smoke Signals (1998), 2491 -> Simply Irresistible (1999), 1243 -> Rosencrantz and Guildenstern Are Dead (1990), 1127 -> Abyss, The (1989), 2763 -> Thomas Crown Affair, The (1999), 3915 -> Girlfight (2000), 2548 -> Rage: Carrie 2, The (1999), 1782 -> Little City (1998), 3502 -> My Life (1993), 42 -> Dead Presidents (1995), 2985 -> Robocop (1987), 2227 -> Lodger, The (1926), 1391 -> Mars Attacks! (1996), 3249 -> Hand That Rocks the Cradle, The (1992), 782 -> Fan, The (1996), 1441 -> Benny & Joon (1993), 709 -> Oliver & Company (1988), 2020 -> Dangerous Liaisons (1988), 841 -> Eyes Without a Face (1959), 3513 -> Rules of Engagement (2000), 417 -> Barcelona (1994), 24 -> Powder (1995), 973 -> Meet John Doe (1941), 885 -> Bogus (1996), 3088 -> Harvey (1950), 3777 -> Nekromantik (1987), 1046 -> Beautiful Thing (1996), 288 -> Natural Born Killers (1994), 1613 -> Star Maps (1997), 3308 -> Flamingo Kid, The (1984), 2010 -> Metropolis (1926), 1935 -> How Green Was My Valley (1941), 3947 -> Get Carter (1971), 1650 -> Washington Square (1997), 3730 -> Conversation, The (1974), 1645 -> Devil's Advocate, The (1997), 1921 -> Pi (1998), 2886 -> Adventures of Elmo in Grouchland, The (1999), 1359 -> Jingle All the Way (1996), 1601 -> Hoodlum (1997), 1386 -> Terror in a Texas Town (1958), 3281 -> Brandon Teena Story, The (1998), 3466 -> Heart and Souls (1993), 301 -> Picture Bride (1995), 3898 -> Bait (2000), 2082 -> Mighty Ducks, The (1992), 3140 -> Three Ages, The (1923), 1724 -> Full Speed (1996), 1475 -> Kama Sutra: A Tale of Love (1996), 320 -> Suture (1993), 3809 -> What About Bob? (1991), 2173 -> Navigator: A Mediaeval Odyssey, The (1988), 565 -> Cronos (1992), 1366 -> Crucible, The (1996), 2516 -> Children of the Corn III (1994), 2400 -> Prancer (1989), 2067 -> Doctor Zhivago (1965), 2825 -> Rosie (1998), 1529 -> Nowhere (1997), 1967 -> Labyrinth (1986), 2819 -> Three Days of the Condor (1975), 436 -> Color of Night (1994), 3880 -> Ballad of Ramblin' Jack, The (2000), 3878 -> X: The Unknown (1956), 2136 -> Nutty Professor, The (1963), 37 -> Across the Sea of Time (1995), 1904 -> Henry Fool (1997), 1518 -> Breakdown (1997), 1265 -> Groundhog Day (1993), 1703 -> For Richer or Poorer (1997), 2531 -> Battle for the Planet of the Apes (1973), 2405 -> Jewel of the Nile, The (1985), 1228 -> Raging Bull (1980), 1623 -> Wishmaster (1997), 1482 -> Van, The (1996), 3597 -> Whipped (2000), 25 -> Leaving Las Vegas (1995), 1254 -> Treasure of the Sierra Madre, The (1948), 3688 -> Porky's (1981), 2869 -> Separation, The (La S�paration) (1994), 1887 -> Almost Heroes (1998), 651 -> Superweib, Das (1996), 2957 -> Sparrows (1926), 257 -> Just Cause (1995), 3820 -> Thomas and the Magic Railroad (2000), 3891 -> Turn It Up (2000), 2654 -> Wolf Man, The (1941), 389 -> Colonel Chabert, Le (1994), 1628 -> Locusts, The (1997), 3248 -> Sister Act 2: Back in the Habit (1993), 52 -> Mighty Aphrodite (1995), 1055 -> Shadow Conspiracy (1997), 1409 -> Michael (1996), 724 -> Craft, The (1996), 3204 -> Boys from Brazil, The (1978), 3442 -> Band of the Hand (1986), 3263 -> White Men Can't Jump (1992), 1287 -> Ben-Hur (1959), 3221 -> Draughtsman's Contract, The (1982), 14 -> Nixon (1995), 1709 -> Legal Deceit (1997), 2603 -> N� (1998), 2328 -> Vampires (1998), 2916 -> Total Recall (1990), 3938 -> Slumber Party Massacre, The (1982), 3195 -> Tess of the Storm Country (1922), 3029 -> Nighthawks (1981), 570 -> Slingshot, The (K�disbellan ) (1993), 3425 -> Mo' Better Blues (1990), 1792 -> U.S. Marshalls (1998), 1430 -> Underworld (1997), 3644 -> Dark Command (1940), 2735 -> Golden Child, The (1986), 2814 -> Bat, The (1959), 1985 -> Halloween 4: The Return of Michael Myers (1988), 2039 -> Cheetah (1989), 3275 -> Boondock Saints, The (1999), 184 -> Nadja (1994), 2698 -> Zone 39 (1997), 1760 -> Spice World (1997), 3131 -> Broadway Damage (1997), 1660 -> Eve's Bayou (1997), 1298 -> Pink Floyd - The Wall (1982), 3116 -> Miss Julie (1999), 2667 -> Mole People, The (1956), 719 -> Multiplicity (1996), 2339 -> I'll Be Home For Christmas (1998), 785 -> Kingpin (1996), 3046 -> Incredibly True Adventure of Two Girls in Love, The (1995), 3689 -> Porky's II: The Next Day (1983), 3342 -> Birdy (1984), 2269 -> Indecent Proposal (1993), 2755 -> Light of Day (1987), 372 -> Reality Bites (1994), 3495 -> Roadside Prophets (1992), 504 -> No Escape (1994), 1871 -> Friend of the Deceased, A (1997), 3574 -> Carnosaur 3: Primal Species (1996), 3061 -> Holiday Inn (1942), 110 -> Braveheart (1995), 2708 -> Autumn Tale, An (Conte d'automne) (1998), 3821 -> Nutty Professor II: The Klumps (2000), 1907 -> Mulan (1998), 2501 -> October Sky (1999), 3617 -> Road Trip (2000), 1330 -> April Fool's Day (1986), 2444 -> 24 7: Twenty Four Seven (1997), 1860 -> Character (Karakter) (1997), 1264 -> Diva (1981), 2217 -> Elstree Calling (1930), 587 -> Ghost (1990), 1323 -> Amityville 3-D (1983), 3612 -> Slipper and the Rose, The (1976), 3148 -> Cider House Rules, The (1999), 3653 -> Endless Summer, The (1966), 619 -> Ed (1996), 838 -> Emma (1996), 1511 -> A Chef in Love (1996), 2274 -> Lilian's Story (1995), 917 -> Little Princess, The (1939), 702 -> Faces (1968), 751 -> Careful (1992), 3485 -> Autopsy (Macchie Solari) (1975), 802 -> Phenomenon (1996), 125 -> Flirting With Disaster (1996), 344 -> Ace Ventura: Pet Detective (1994), 3776 -> Melody Time (1948), 2682 -> Limbo (1999), 3125 -> End of the Affair, The (1999), 1826 -> Barney's Great Adventure (1998), 3542 -> Coming Apart (1969), 1313 -> Mad Dog Time (1996), 1279 -> Night on Earth (1991), 3410 -> Soft Fruit (1999), 2185 -> I Confess (1953), 1577 -> Mondo (1996), 1455 -> Hotel de Love (1996), 3280 -> Baby, The (1973), 3749 -> Time Regained (Le Temps Retrouv�) (1999), 3845 -> And God Created Woman (Et Dieu…Cr�a la Femme) (1956), 2562 -> Bandits (1997), 3089 -> Bicycle Thief, The (Ladri di biciclette) (1948), 2488 -> Peeping Tom (1960), 1832 -> Heaven's Burning (1997), 2897 -> And the Ship Sails On (E la nave va) (1984), 934 -> Father of the Bride (1950), 357 -> Four Weddings and a Funeral (1994), 1191 -> Madonna: Truth or Dare (1991), 3744 -> Shaft (2000), 1992 -> Child's Play 2 (1990), 3353 -> Closer You Get, The (2000), 3803 -> Greaser's Palace (1972), 2466 -> Belizaire the Cajun (1986), 2126 -> Snake Eyes (1998), 196 -> Species (1995), 1462 -> Unforgotten: Twenty-Five Years After Willowbrook (1996), 1059 -> William Shakespeare's Romeo and Juliet (1996), 1132 -> Manon of the Spring (Manon des sources) (1986), 3043 -> Meatballs 4 (1992), 3855 -> Affair of Love, An (Une Liaison Pornographique) (1999), 949 -> East of Eden (1955), 2745 -> Mission, The (1986), 2571 -> Matrix, The (1999), 3713 -> Long Walk Home, The (1990), 2356 -> Celebrity (1998), 542 -> Son in Law (1993), 460 -> Getting Even with Dad (1994), 157 -> Canadian Bacon (1994), 2703 -> Broken Vessels (1998), 1545 -> Ponette (1996), 2420 -> Karate Kid, The (1984), 1922 -> Whatever (1998), 2691 -> Legend of 1900, The (Leggenda del pianista sull'oceano) (1998), 902 -> Breakfast at Tiffany's (1961), 559 -> Paris, France (1993), 3099 -> Shampoo (1975), 2713 -> Lake Placid (1999), 3517 -> Bells, The (1926), 638 -> Jack and Sarah (1995), 853 -> Dingo (1992), 3840 -> Pumpkinhead (1988), 1892 -> Perfect Murder, A (1998), 3463 -> Last Resort (1994), 1379 -> Young Guns II (1990), 3374 -> Daughters of the Dust (1992), 3385 -> Volunteers (1985), 2857 -> Yellow Submarine (1968), 2169 -> Dead Man on Campus (1998), 2403 -> First Blood (1982), 3638 -> Moonraker (1979), 1087 -> Madame Butterfly (1995), 1514 -> Temptress Moon (Feng Yue) (1996), 189 -> Reckless (1995), 20 -> Money Train (1995), 1147 -> When We Were Kings (1996), 1247 -> Graduate, The (1967), 2049 -> Happiest Millionaire, The (1967), 2552 -> My Boyfriend's Back (1993), 1319 -> Kids of Survival (1993), 1704 -> Good Will Hunting (1997), 421 -> Black Beauty (1994), 870 -> Gone Fishin' (1997), 1890 -> Little Boy Blue (1997), 2767 -> Illuminata (1998), 1479 -> Saint, The (1997), 46 -> How to Make an American Quilt (1995), 1609 -> 187 (1997), 969 -> African Queen, The (1951), 93 -> Vampire in Brooklyn (1995), 2373 -> Red Sonja (1985), 606 -> Candyman: Farewell to the Flesh (1995), 1347 -> Nightmare on Elm Street, A (1984), 1572 -> Contempt (Le M�pris) (1963), 3919 -> Hellraiser III: Hell on Earth (1992), 1013 -> Parent Trap, The (1961), 3216 -> Vampyros Lesbos (Las Vampiras) (1970), 284 -> New York Cop (1996), 770 -> Costa Brava (1946), 1741 -> Midaq Alley (Callej�n de los milagros, El) (1995), 1398 -> In Love and War (1996), 881 -> First Kid (1996), 3084 -> Home Page (1999), 416 -> Bad Girls (1994), 1115 -> Sleepover (1995), 2635 -> Mummy's Curse, The (1944), 2929 -> Reds (1981), 325 -> National Lampoon's Senior Trip (1995), 3470 -> Dersu Uzala (1974), 3312 -> McCullochs, The (1975), 3902 -> Goya in Bordeaux (Goya en Bodeos) (1999), 3657 -> Pandora and the Flying Dutchman (1951), 1931 -> Mutiny on the Bounty (1935), 152 -> Addiction, The (1995), 1568 -> MURDER and murder (1996), 3734 -> Prince of the City (1981), 3014 -> Bustin' Loose (1981), 2520 -> Airport (1970), 2730 -> Barry Lyndon (1975), 228 -> Destiny Turns on the Radio (1995), 3951 -> Two Family House (2000), 2158 -> Henry: Portrait of a Serial Killer, Part 2 (1996), 2024 -> Rapture, The (1991), 1641 -> Full Monty, The (1997), 3835 -> Crush, The (1993), 3708 -> Firestarter (1984), 289 -> Only You (1994), 1773 -> Tokyo Fist (1995), 448 -> Fearless (1993), 2301 -> History of the World: Part I (1981), 1815 -> Eden (1997), 1494 -> Sixth Man, The (1997), 57 -> Home for the Holidays (1995), 2989 -> For Your Eyes Only (1981), 316 -> Stargate (1994), 1362 -> Garden of Finzi-Contini, The (Giardino dei Finzi-Contini, Il) (1970), 1963 -> Take the Money and Run (1969), 3180 -> Play it to the Bone (1999), 3602 -> G. I. Blues (1960), 3557 -> Jennifer 8 (1992), 78 -> Crossing Guard, The (1995), 1875 -> Clockwatchers (1997), 2852 -> Soldier's Story, A (1984), 3231 -> Saphead, The (1920), 2388 -> Steam: The Turkish Bath (Hamam) (1997), 3866 -> Sunset Strip (2000), 2533 -> Escape from the Planet of the Apes (1971), 1100 -> Days of Thunder (1990), 261 -> Little Women (1994), 1232 -> Stalker (1979), 3268 -> Stop! Or My Mom Will Shoot (1992), 3258 -> Death Becomes Her (1992), 1847 -> Ratchet (1996), 29 -> City of Lost Children, The (1995), 2829 -> Muse, The (1999), 3788 -> Blowup (1966), 2456 -> Fly II, The (1989), 2007 -> Polish Wedding (1998), 2178 -> Frenzy (1972), 2222 -> Champagne (1928), 216 -> Billy Madison (1995), 1489 -> Cats Don't Dance (1997), 2950 -> Blue Lagoon, The (1980), 2237 -> Without Limits (1998), 1415 -> Thieves (Voleurs, Les) (1996), 475 -> In the Name of the Father (1993), 924 -> 2001: A Space Odyssey (1968), 3322 -> 3 Strikes (2000), 2616 -> Dick Tracy (1990), 1946 -> Marty (1955), 492 -> Manhattan Murder Mystery (1993), 164 -> Devil in a Blue Dress (1995), 1465 -> Rosewood (1997), 3524 -> Arthur (1981), 3431 -> Death Wish II (1982), 1302 -> Field of Dreams (1989), 756 -> Carmen Miranda: Bananas Is My Business (1994), 1664 -> N�nette et Boni (1996), 2794 -> European Vacation (1985), 3887 -> Went to Coney Island on a Mission From God... Be Back by Five (1998), 2324 -> Life Is Beautiful (La Vita � bella) (1997), 1714 -> Never Met Picasso (1996), 2027 -> Mafia! (1998), 3199 -> Pal Joey (1957), 789 -> I, Worst of All (Yo, la peor de todas) (1990), 3934 -> Kronos (1957), 2105 -> Tron (1982), 3075 -> Repulsion (1965), 1811 -> Niagara, Niagara (1997), 179 -> Mad Love (1995), 1450 -> Prisoner of the Mountains (Kavkazsky Plennik) (1996), 2090 -> Rescuers, The (1977), 591 -> Tough and Deadly (1995), 3393 -> Date with an Angel (1987), 1373 -> Star Trek V: The Final Frontier (1989), 2996 -> Music of the Heart (1999), 443 -> Endless Summer 2, The (1994), 3724 -> Coming Home (1978), 1911 -> Doctor Dolittle (1998), 3295 -> Raining Stones (1993), 2782 -> Pit and the Pendulum (1961), 1040 -> Secret Agent, The (1996), 1109 -> Charm's Incidents (1996), 2893 -> Plunkett & MaCleane (1999), 1632 -> Smile Like Yours, A (1997), 321 -> Strawberry and Chocolate (Fresa y chocolate) (1993), 2469 -> Peggy Sue Got Married (1986), 3670 -> Story of G.I. Joe, The (1945), 376 -> River Wild, The (1994), 2259 -> Blame It on Rio (1984), 3163 -> Topsy-Turvy (1999), 2650 -> Ghost of Frankenstein, The (1942), 3453 -> Here on Earth (2000), 2451 -> Gate, The (1987), 2236 -> Simon Birch (1998), 623 -> Modern Affair, A (1995), 1334 -> Blob, The (1958), 211 -> Browning Version, The (1994), 3527 -> Predator (1987), 3473 -> Jonah Who Will Be 25 in the Year 2000 (1976), 3702 -> Mad Max (1979), 253 -> Interview with the Vampire (1994), 2880 -> Operation Condor 2 (Longxiong hudi) (1990), 1682 -> Truman Show, The (1998), 485 -> Last Action Hero (1993), 1136 -> Monty Python and the Holy Grail (1974), 834 -> Phat Beach (1996), 1858 -> Mr. Nice Guy (1997), 3438 -> Teenage Mutant Ninja Turtles (1990), 3525 -> Bachelor Party (1984), 1864 -> Sour Grapes (1998), 2122 -> Children of the Corn (1984), 106 -> Nobody Loves Me (Keiner liebt mich) (1994), 238 -> Far From Home: The Adventures of Yellow Dog (1995), 3621 -> Possession (1981), 2925 -> Conformist, The (Il Conformista) (1970), 1978 -> Friday the 13th Part V: A New Beginning (1985), 121 -> Boys of St. Vincent, The (1993), 514 -> Ref, The (1994), 1151 -> Faust (1994), 937 -> Love in the Afternoon (1957), 2291 -> Edward Scissorhands (1990), 1383 -> Adrenalin: Fear the Rush (1996), 1020 -> Cool Runnings (1993), 348 -> Bullets Over Broadway (1994), 3357 -> East-West (Est-ouest) (1999), 574 -> Spanking the Monkey (1994), 2254 -> Choices (1981), 3569 -> Idiots, The (Idioterne) (1998), 1677 -> Critical Care (1997), 2540 -> Corruptor, The (1999), 2034 -> Black Hole, The (1979), 3923 -> Return of the Fly (1959), 1960 -> Last Emperor, The (1987), 2555 -> Baby Geniuses (1999), 3290 -> Soft Toilet Seats (1999), 806 -> American Buffalo (1996), 84 -> Last Summer in the Hamptons (1995), 3300 -> Pitch Black (2000), 3093 -> McCabe & Mrs. Miller (1971), 1581 -> Out to Sea (1997), 353 -> Crow, The (1994), 2335 -> Waterboy, The (1998), 966 -> Walk in the Sun, A (1945), 2777 -> Cobra (1925), 3152 -> Last Picture Show, The (1971), 1183 -> English Patient, The (1996), 1283 -> High Noon (1952), 2073 -> Fandango (1985), 1051 -> Trees Lounge (1996), 3771 -> Golden Voyage of Sinbad, The (1974), 821 -> Crude Oasis, The (1995), 3851 -> I'm the One That I Want (2000), 2017 -> Babes in Toyland (1961), 905 -> It Happened One Night (1934), 480 -> Jurassic Park (1993), 3389 -> Let's Get Harry (1986), 602 -> Great Day in Harlem, A (1994), 2508 -> Breaks, The (1999), 981 -> Dangerous Ground (1997), 766 -> I Shot Andy Warhol (1996), 2964 -> Julien Donkey-Boy (1999), 147 -> Basketball Diaries, The (1995), 1843 -> Slappy and the Stinkers (1998), 2058 -> Negotiator, The (1998), 1169 -> American Dream (1990), 397 -> Fear, The (1995), 687 -> Country Life (1994), 2367 -> King Kong (1976), 2437 -> Wilde (1997), 3537 -> Where the Money Is (2000), 1928 -> Cimarron (1931), 3068 -> Verdict, The (1982), 1377 -> Batman Returns (1992), 280 -> Murder in the First (1995), 2618 -> Castle, The (1997), 2833 -> Lucie Aubrac (1997), 3189 -> My Dog Skip (1999), 3739 -> Trouble in Paradise (1932), 2281 -> Monument Ave. (1998), 2861 -> For Love of the Game (1999), 1215 -> Army of Darkness (1993), 61 -> Eye for an Eye (1996), 634 -> Theodore Rex (1995), 1896 -> Cousin Bette (1998), 877 -> Girls Town (1996), 692 -> Solo (1996), 2190 -> Why Do Fools Fall In Love? (1998), 3421 -> Animal House (1978), 734 -> Getting Away With Murder (1996), 3712 -> Soapdish (1991), 1447 -> Gridlock'd (1997), 2483 -> Day of the Beast, The (El D�a de la bestia) (1995), 293 -> Professional, The (a.k.a. Leon: The Professional) (1994), 956 -> Penny Serenade (1941), 3505 -> No Way Out (1987), 3685 -> Prizzi's Honor (1985), 866 -> Bound (1996), 1886 -> I Got the Hook Up (1998), 3808 -> Two Women (La Ciociara) (1961), 2523 -> Rollercoaster (1977), 1796 -> In God's Hands (1998), 3036 -> Quest for Fire (1981), 2530 -> Beneath the Planet of the Apes (1970), 453 -> For Love or Money (1993), 1119 -> Drunks (1997), 132 -> Jade (1995), 1622 -> Kicked in the Head (1997), 2424 -> You've Got Mail (1998), 774 -> Wend Kuuni (God's Gift) (1982), 2371 -> Fletch (1985), 2979 -> Body Shots (1999), 1394 -> Raising Arizona (1987), 2772 -> Detroit Rock City (1999), 3157 -> Stuart Little (1999), 396 -> Fall Time (1995), 1526 -> Fathers' Day (1997), 3676 -> Eraserhead (1977), 2137 -> Charlotte's Web (1973), 89 -> Nick of Time (1995), 1351 -> Blood & Wine (1997), 133 -> Nueba Yol (1995), 660 -> August (1996), 3361 -> Bull Durham (1988), 2002 -> Lethal Weapon 3 (1992), 998 -> Set It Off (1996), 3756 -> Golden Bowl, The (2000), 3243 -> Encino Man (1992), 2809 -> Love Stinks (1999), 2242 -> Grandview, U.S.A. (1984), 411 -> You So Crazy (1994), 1345 -> Carrie (1976), 988 -> Grace of My Heart (1996), 2799 -> Problem Child 2 (1991), 2286 -> Fiendish Plot of Dr. Fu Manchu, The (1980), 2639 -> Mommie Dearest (1981), 3327 -> Beyond the Mat (2000), 1433 -> Machine, The (1994), 116 -> Anne Frank Remembered (1995), 2103 -> Tall Tale (1994), 2750 -> Radio Days (1987), 243 -> Gordy (1995), 1405 -> Beavis and Butt-head Do America (1996), 3226 -> Hellhounds on My Trail (1999), 1943 -> Greatest Show on Earth, The (1952), 428 -> Bronx Tale, A (1993), 1201 -> Good, The Bad and The Ugly, The (1966), 2804 -> Christmas Story, A (1983), 2965 -> Omega Code, The (1999), 1 -> Toy Story (1995), 1600 -> She's So Lovely (1997), 3870 -> Our Town (1940), 3588 -> King of Marvin Gardens, The (1972), 1104 -> Streetcar Named Desire, A (1951), 1697 -> Big Bang Theory, The (1994), 1242 -> Glory (1989), 265 -> Like Water for Chocolate (Como agua para chocolate) (1992), 3559 -> Limelight (1952), 849 -> Escape from L.A. (1996), 3606 -> On the Town (1949), 3211 -> Cry in the Dark, A (1988), 2392 -> Jack Frost (1998), 2154 -> How Stella Got Her Groove Back (1998), 3906 -> Under Suspicion (2000), 507 -> Perfect World, A (1993), 527 -> Schindler's List (1993), 312 -> Stuart Saves His Family (1995), 74 -> Bed of Roses (1996), 3492 -> Son of the Sheik, The (1926), 2718 -> Drop Dead Gorgeous (1999), 3406 -> Captain Horatio Hornblower (1951), 2205 -> Mr. & Mrs. Smith (1941), 1696 -> Bent (1997), 3184 -> Montana (1998), 206 -> Unzipped (1995), 2586 -> Goodbye, Lover (1999), 2982 -> Guardian, The (1990), 2884 -> Dog Park (1998), 1975 -> Friday the 13th Part 2 (1981), 3510 -> Frequency (2000), 1564 -> Roseanna's Grave (For Roseanna) (1997), 2762 -> Sixth Sense, The (1999), 3378 -> Ogre, The (Der Unhold) (1996), 1863 -> Major League: Back to the Minors (1998), 3435 -> Double Indemnity (1944), 2954 -> Penitentiary (1979), 307 -> Three Colors: Blue (1993), 1651 -> Telling Lies in America (1997), 1604 -> Money Talks (1997), 3899 -> Circus (2000), 2922 -> Hang 'em High (1967), 3151 -> Bat Whispers, The (1930), 2011 -> Back to the Future Part II (1989), 3097 -> Shop Around the Corner, The (1940), 3582 -> Jails, Hospitals & Hip-Hop (2000), 292 -> Outbreak (1995), 3229 -> Another Man's Poison (1952), 3011 -> They Shoot Horses, Don't They? (1969), 1206 -> Clockwork Orange, A (1971), 233 -> Exotica (1994), 452 -> Widows' Peak (1994), 6 -> Heat (1995), 920 -> Gone with the Wind (1939), 248 -> Houseguest (1994), 3857 -> Bless the Child (2000), 60 -> Indian in the Cupboard, The (1995), 380 -> True Lies (1994), 3450 -> Grumpy Old Men (1993), 3503 -> Solaris (Solyaris) (1972), 3459 -> Gothic (1986), 117 -> Young Poisoner's Handbook, The (1995), 512 -> Robert A. Heinlein's The Puppet Masters (1994), 942 -> Laura (1944), 439 -> Dangerous Game (1993), 3591 -> Mr. Mom (1983), 2910 -> Ennui, L' (1998), 3301 -> Whole Nine Yards, The (2000), 2204 -> Saboteur (1942), 3303 -> Black Tar Heroin: The Dark End of the Street (1999), 678 -> Some Folks Call It a Sling Blade (1993), 2473 -> Soul Man (1986), 270 -> Love Affair (1994), 529 -> Searching for Bobby Fischer (1993), 3567 -> Bossa Nova (1999), 2574 -> Out-of-Towners, The (1999), 1438 -> Dante's Peak (1997), 3720 -> Sunshine (1999), 661 -> James and the Giant Peach (1996), 546 -> Super Mario Bros. (1993), 793 -> My Life and Times With Antonin Artaud (En compagnie d'Antonin Artaud) (1993), 3169 -> Falcon and the Snowman, The (1984), 3265 -> Hard-Boiled (Lashou shentan) (1992), 2646 -> House of Dracula (1945), 925 -> Golden Earrings (1947), 3852 -> Tao of Steve, The (2000), 85 -> Angels and Insects (1995), 1306 -> Until the End of the World (Bis ans Ende der Welt) (1991), 201 -> Three Wishes (1995), 3531 -> All the Vermeers in New York (1990))
Let's make a data frame to visually explore the data next.
sc.textFile("/databricks-datasets/cs100/lab4/data-001/ratings.dat.gz").map { line => line.split("::") }.take(5)
res4: Array[Array[String]] = Array(Array(1, 1193, 5, 978300760), Array(1, 661, 3, 978302109), Array(1, 914, 3, 978301968), Array(1, 3408, 4, 978300275), Array(1, 2355, 5, 978824291))
val timedRatingsDF = sc.textFile("/databricks-datasets/cs100/lab4/data-001/ratings.dat.gz").map { line =>
val fields = line.split("::")
// format: (timestamp % 10, Rating(userId, movieId, rating))
(fields(3).toLong, fields(0).toInt, fields(1).toInt, fields(2).toDouble)
}.toDF("timestamp", "userId", "movieId", "rating")
timedRatingsDF: org.apache.spark.sql.DataFrame = [timestamp: bigint, userId: int ... 2 more fields]
display(timedRatingsDF)
| timestamp | userId | movieId | rating |
|---|---|---|---|
| 9.7830076e8 | 1.0 | 1193.0 | 5.0 |
| 9.78302109e8 | 1.0 | 661.0 | 3.0 |
| 9.78301968e8 | 1.0 | 914.0 | 3.0 |
| 9.78300275e8 | 1.0 | 3408.0 | 4.0 |
| 9.78824291e8 | 1.0 | 2355.0 | 5.0 |
| 9.78302268e8 | 1.0 | 1197.0 | 3.0 |
| 9.78302039e8 | 1.0 | 1287.0 | 5.0 |
| 9.78300719e8 | 1.0 | 2804.0 | 5.0 |
| 9.78302268e8 | 1.0 | 594.0 | 4.0 |
| 9.78301368e8 | 1.0 | 919.0 | 4.0 |
| 9.78824268e8 | 1.0 | 595.0 | 5.0 |
| 9.78301752e8 | 1.0 | 938.0 | 4.0 |
| 9.78302281e8 | 1.0 | 2398.0 | 4.0 |
| 9.78302124e8 | 1.0 | 2918.0 | 4.0 |
| 9.78301753e8 | 1.0 | 1035.0 | 5.0 |
| 9.78302188e8 | 1.0 | 2791.0 | 4.0 |
| 9.78824268e8 | 1.0 | 2687.0 | 3.0 |
| 9.78301777e8 | 1.0 | 2018.0 | 4.0 |
| 9.78301713e8 | 1.0 | 3105.0 | 5.0 |
| 9.78302039e8 | 1.0 | 2797.0 | 4.0 |
| 9.78302205e8 | 1.0 | 2321.0 | 3.0 |
| 9.7830076e8 | 1.0 | 720.0 | 3.0 |
| 9.78300055e8 | 1.0 | 1270.0 | 5.0 |
| 9.78824195e8 | 1.0 | 527.0 | 5.0 |
| 9.78300103e8 | 1.0 | 2340.0 | 3.0 |
| 9.78824351e8 | 1.0 | 48.0 | 5.0 |
| 9.78301953e8 | 1.0 | 1097.0 | 4.0 |
| 9.78300055e8 | 1.0 | 1721.0 | 4.0 |
| 9.78824139e8 | 1.0 | 1545.0 | 4.0 |
| 9.78824268e8 | 1.0 | 745.0 | 3.0 |
| 9.78824291e8 | 1.0 | 2294.0 | 4.0 |
| 9.78300019e8 | 1.0 | 3186.0 | 4.0 |
| 9.7882433e8 | 1.0 | 1566.0 | 4.0 |
| 9.78824268e8 | 1.0 | 588.0 | 4.0 |
| 9.7882433e8 | 1.0 | 1907.0 | 4.0 |
| 9.78824291e8 | 1.0 | 783.0 | 4.0 |
| 9.78300172e8 | 1.0 | 1836.0 | 5.0 |
| 9.78300055e8 | 1.0 | 1022.0 | 5.0 |
| 9.78302091e8 | 1.0 | 2762.0 | 4.0 |
| 9.78301777e8 | 1.0 | 150.0 | 5.0 |
| 9.78824268e8 | 1.0 | 1.0 | 5.0 |
| 9.7830159e8 | 1.0 | 1961.0 | 5.0 |
| 9.78301753e8 | 1.0 | 1962.0 | 4.0 |
| 9.7830157e8 | 1.0 | 2692.0 | 4.0 |
| 9.7830076e8 | 1.0 | 260.0 | 4.0 |
| 9.78301777e8 | 1.0 | 1028.0 | 5.0 |
| 9.78302205e8 | 1.0 | 1029.0 | 5.0 |
| 9.78300719e8 | 1.0 | 1207.0 | 4.0 |
| 9.78301619e8 | 1.0 | 2028.0 | 5.0 |
| 9.78302149e8 | 1.0 | 531.0 | 4.0 |
| 9.78302174e8 | 1.0 | 3114.0 | 4.0 |
| 9.78301398e8 | 1.0 | 608.0 | 4.0 |
| 9.78302091e8 | 1.0 | 1246.0 | 4.0 |
| 9.78298709e8 | 2.0 | 1357.0 | 5.0 |
| 9.78299e8 | 2.0 | 3068.0 | 4.0 |
| 9.7829962e8 | 2.0 | 1537.0 | 4.0 |
| 9.78299351e8 | 2.0 | 647.0 | 3.0 |
| 9.78299297e8 | 2.0 | 2194.0 | 4.0 |
| 9.78299913e8 | 2.0 | 648.0 | 4.0 |
| 9.78299297e8 | 2.0 | 2268.0 | 5.0 |
| 9.78300051e8 | 2.0 | 2628.0 | 3.0 |
| 9.78298905e8 | 2.0 | 1103.0 | 3.0 |
| 9.78299809e8 | 2.0 | 2916.0 | 3.0 |
| 9.78298542e8 | 2.0 | 3468.0 | 5.0 |
| 9.78298151e8 | 2.0 | 1210.0 | 4.0 |
| 9.78299941e8 | 2.0 | 1792.0 | 3.0 |
| 9.78300174e8 | 2.0 | 1687.0 | 3.0 |
| 9.78298458e8 | 2.0 | 1213.0 | 2.0 |
| 9.78298958e8 | 2.0 | 3578.0 | 5.0 |
| 9.78300002e8 | 2.0 | 2881.0 | 3.0 |
| 9.78298434e8 | 2.0 | 3030.0 | 4.0 |
| 9.78298151e8 | 2.0 | 1217.0 | 3.0 |
| 9.78298673e8 | 2.0 | 3105.0 | 4.0 |
| 9.78300174e8 | 2.0 | 434.0 | 2.0 |
| 9.78300123e8 | 2.0 | 2126.0 | 3.0 |
| 9.78300002e8 | 2.0 | 3107.0 | 2.0 |
| 9.78299712e8 | 2.0 | 3108.0 | 3.0 |
| 9.78298625e8 | 2.0 | 3035.0 | 4.0 |
| 9.7829912e8 | 2.0 | 1253.0 | 3.0 |
| 9.78299809e8 | 2.0 | 1610.0 | 5.0 |
| 9.78300123e8 | 2.0 | 292.0 | 3.0 |
| 9.7829922e8 | 2.0 | 2236.0 | 5.0 |
| 9.7829912e8 | 2.0 | 3071.0 | 4.0 |
| 9.78298905e8 | 2.0 | 902.0 | 2.0 |
| 9.78300002e8 | 2.0 | 368.0 | 4.0 |
| 9.78298841e8 | 2.0 | 1259.0 | 5.0 |
| 9.78298652e8 | 2.0 | 3147.0 | 5.0 |
| 9.78300174e8 | 2.0 | 1544.0 | 4.0 |
| 9.78298261e8 | 2.0 | 1293.0 | 5.0 |
| 9.7829962e8 | 2.0 | 1188.0 | 4.0 |
| 9.78299321e8 | 2.0 | 3255.0 | 4.0 |
| 9.78299839e8 | 2.0 | 3256.0 | 2.0 |
| 9.78300073e8 | 2.0 | 3257.0 | 3.0 |
| 9.78298625e8 | 2.0 | 110.0 | 5.0 |
| 9.78299889e8 | 2.0 | 2278.0 | 3.0 |
| 9.78299966e8 | 2.0 | 2490.0 | 3.0 |
| 9.78298813e8 | 2.0 | 1834.0 | 4.0 |
| 9.78298814e8 | 2.0 | 3471.0 | 5.0 |
| 9.78299773e8 | 2.0 | 589.0 | 4.0 |
| 9.78300051e8 | 2.0 | 1690.0 | 3.0 |
| 9.78298814e8 | 2.0 | 3654.0 | 3.0 |
| 9.78298958e8 | 2.0 | 2852.0 | 3.0 |
| 9.78298458e8 | 2.0 | 1945.0 | 5.0 |
| 9.78299269e8 | 2.0 | 982.0 | 4.0 |
| 9.78298542e8 | 2.0 | 1873.0 | 4.0 |
| 9.78298434e8 | 2.0 | 2858.0 | 4.0 |
| 9.78298391e8 | 2.0 | 1225.0 | 5.0 |
| 9.78299773e8 | 2.0 | 2028.0 | 4.0 |
| 9.78298542e8 | 2.0 | 515.0 | 5.0 |
| 9.78300025e8 | 2.0 | 442.0 | 3.0 |
| 9.78299046e8 | 2.0 | 2312.0 | 3.0 |
| 9.78299026e8 | 2.0 | 265.0 | 4.0 |
| 9.78299839e8 | 2.0 | 1408.0 | 3.0 |
| 9.78298813e8 | 2.0 | 1084.0 | 3.0 |
| 9.78299173e8 | 2.0 | 3699.0 | 2.0 |
| 9.78299809e8 | 2.0 | 480.0 | 5.0 |
| 9.78299297e8 | 2.0 | 1442.0 | 4.0 |
| 9.78298625e8 | 2.0 | 2067.0 | 5.0 |
| 9.78299712e8 | 2.0 | 1265.0 | 3.0 |
| 9.78299889e8 | 2.0 | 1370.0 | 5.0 |
| 9.78298413e8 | 2.0 | 1193.0 | 5.0 |
| 9.78300002e8 | 2.0 | 1801.0 | 3.0 |
| 9.78299941e8 | 2.0 | 1372.0 | 3.0 |
| 9.78299861e8 | 2.0 | 2353.0 | 4.0 |
| 9.78298958e8 | 2.0 | 3334.0 | 4.0 |
| 9.78299913e8 | 2.0 | 2427.0 | 2.0 |
| 9.78299083e8 | 2.0 | 590.0 | 5.0 |
| 9.7829873e8 | 2.0 | 1196.0 | 5.0 |
| 9.78299941e8 | 2.0 | 1552.0 | 3.0 |
| 9.783001e8 | 2.0 | 736.0 | 4.0 |
| 9.78298124e8 | 2.0 | 1198.0 | 4.0 |
| 9.78298517e8 | 2.0 | 593.0 | 5.0 |
| 9.78299666e8 | 2.0 | 2359.0 | 3.0 |
| 9.78300143e8 | 2.0 | 95.0 | 2.0 |
| 9.78298196e8 | 2.0 | 2717.0 | 3.0 |
| 9.78299773e8 | 2.0 | 2571.0 | 4.0 |
| 9.78300174e8 | 2.0 | 1917.0 | 3.0 |
| 9.78299641e8 | 2.0 | 2396.0 | 4.0 |
| 9.78298814e8 | 2.0 | 3735.0 | 3.0 |
| 9.78298775e8 | 2.0 | 1953.0 | 4.0 |
| 9.78300025e8 | 2.0 | 1597.0 | 3.0 |
| 9.78299712e8 | 2.0 | 3809.0 | 3.0 |
| 9.78298841e8 | 2.0 | 1954.0 | 5.0 |
| 9.782992e8 | 2.0 | 1955.0 | 4.0 |
| 9.78299351e8 | 2.0 | 235.0 | 3.0 |
| 9.78299418e8 | 2.0 | 1124.0 | 5.0 |
| 9.7829875e8 | 2.0 | 1957.0 | 5.0 |
| 9.78299809e8 | 2.0 | 163.0 | 4.0 |
| 9.78299839e8 | 2.0 | 21.0 | 1.0 |
| 9.78300002e8 | 2.0 | 165.0 | 3.0 |
| 9.78299666e8 | 2.0 | 2321.0 | 3.0 |
| 9.7829858e8 | 2.0 | 1090.0 | 2.0 |
| 9.78299809e8 | 2.0 | 380.0 | 5.0 |
| 9.782986e8 | 2.0 | 2501.0 | 5.0 |
| 9.78299839e8 | 2.0 | 349.0 | 4.0 |
| 9.78299773e8 | 2.0 | 457.0 | 4.0 |
| 9.78299386e8 | 2.0 | 1096.0 | 4.0 |
| 9.78298775e8 | 2.0 | 920.0 | 5.0 |
| 9.78300002e8 | 2.0 | 459.0 | 3.0 |
| 9.78299839e8 | 2.0 | 1527.0 | 4.0 |
| 9.78299809e8 | 2.0 | 3418.0 | 4.0 |
| 9.78299966e8 | 2.0 | 1385.0 | 3.0 |
| 9.78298924e8 | 2.0 | 3451.0 | 4.0 |
| 9.78298517e8 | 2.0 | 3095.0 | 4.0 |
| 9.78299966e8 | 2.0 | 780.0 | 3.0 |
| 9.78299418e8 | 2.0 | 498.0 | 3.0 |
| 9.78298881e8 | 2.0 | 2728.0 | 3.0 |
| 9.783001e8 | 2.0 | 2002.0 | 5.0 |
| 9.78298813e8 | 2.0 | 1962.0 | 5.0 |
| 9.78298841e8 | 2.0 | 1784.0 | 5.0 |
| 9.78298372e8 | 2.0 | 2943.0 | 4.0 |
| 9.78299861e8 | 2.0 | 2006.0 | 3.0 |
| 9.78298413e8 | 2.0 | 318.0 | 5.0 |
| 9.78298478e8 | 2.0 | 1207.0 | 4.0 |
| 9.78298881e8 | 2.0 | 1968.0 | 2.0 |
| 9.7829925e8 | 2.0 | 3678.0 | 3.0 |
| 9.78299143e8 | 2.0 | 1244.0 | 3.0 |
| 9.78299686e8 | 2.0 | 356.0 | 5.0 |
| 9.782992e8 | 2.0 | 1245.0 | 2.0 |
| 9.78299418e8 | 2.0 | 1246.0 | 5.0 |
| 9.78299535e8 | 2.0 | 3893.0 | 1.0 |
| 9.78298652e8 | 2.0 | 1247.0 | 5.0 |
| 9.78298147e8 | 3.0 | 3421.0 | 4.0 |
| 9.7829843e8 | 3.0 | 1641.0 | 2.0 |
| 9.78297867e8 | 3.0 | 648.0 | 3.0 |
| 9.78298147e8 | 3.0 | 1394.0 | 4.0 |
| 9.78297068e8 | 3.0 | 3534.0 | 3.0 |
| 9.78298486e8 | 3.0 | 104.0 | 4.0 |
| 9.78297867e8 | 3.0 | 2735.0 | 4.0 |
| 9.782976e8 | 3.0 | 1210.0 | 4.0 |
| 9.78297095e8 | 3.0 | 1431.0 | 3.0 |
| 9.78298486e8 | 3.0 | 3868.0 | 3.0 |
| 9.78298296e8 | 3.0 | 1079.0 | 5.0 |
| 9.78298147e8 | 3.0 | 2997.0 | 3.0 |
| 9.7829771e8 | 3.0 | 1615.0 | 5.0 |
| 9.782976e8 | 3.0 | 1291.0 | 4.0 |
| 9.78298296e8 | 3.0 | 1259.0 | 5.0 |
| 9.78297757e8 | 3.0 | 653.0 | 4.0 |
| 9.782976e8 | 3.0 | 2167.0 | 5.0 |
| 9.78297663e8 | 3.0 | 1580.0 | 3.0 |
| 9.78298201e8 | 3.0 | 3619.0 | 2.0 |
| 9.78297512e8 | 3.0 | 260.0 | 5.0 |
| 9.78297039e8 | 3.0 | 2858.0 | 4.0 |
| 9.78298103e8 | 3.0 | 3114.0 | 3.0 |
| 9.78297805e8 | 3.0 | 1049.0 | 4.0 |
| 9.78297663e8 | 3.0 | 1261.0 | 1.0 |
| 9.78297837e8 | 3.0 | 552.0 | 4.0 |
| 9.7829769e8 | 3.0 | 480.0 | 4.0 |
| 9.78298316e8 | 3.0 | 1265.0 | 2.0 |
| 9.78297396e8 | 3.0 | 1266.0 | 5.0 |
| 9.78297757e8 | 3.0 | 733.0 | 5.0 |
| 9.78297539e8 | 3.0 | 1196.0 | 4.0 |
| 9.78297439e8 | 3.0 | 590.0 | 4.0 |
| 9.7829843e8 | 3.0 | 2355.0 | 5.0 |
| 9.7829757e8 | 3.0 | 1197.0 | 5.0 |
| 9.7829757e8 | 3.0 | 1198.0 | 5.0 |
| 9.78297419e8 | 3.0 | 1378.0 | 5.0 |
| 9.78297018e8 | 3.0 | 593.0 | 3.0 |
| 9.78297419e8 | 3.0 | 1379.0 | 4.0 |
| 9.78298459e8 | 3.0 | 3552.0 | 5.0 |
| 9.78298166e8 | 3.0 | 1304.0 | 5.0 |
| 9.78298231e8 | 3.0 | 1270.0 | 3.0 |
| 9.78297777e8 | 3.0 | 2470.0 | 4.0 |
| 9.7829757e8 | 3.0 | 3168.0 | 4.0 |
| 9.78297837e8 | 3.0 | 2617.0 | 2.0 |
| 9.78297095e8 | 3.0 | 1961.0 | 4.0 |
| 9.78297419e8 | 3.0 | 3671.0 | 5.0 |
| 9.78297757e8 | 3.0 | 2006.0 | 4.0 |
| 9.78297539e8 | 3.0 | 2871.0 | 4.0 |
| 9.78297777e8 | 3.0 | 2115.0 | 4.0 |
| 9.78297068e8 | 3.0 | 1968.0 | 4.0 |
| 9.78298079e8 | 3.0 | 1136.0 | 5.0 |
| 9.78298504e8 | 3.0 | 2081.0 | 4.0 |
| 9.78294008e8 | 4.0 | 3468.0 | 5.0 |
| 9.78293924e8 | 4.0 | 1210.0 | 3.0 |
| 9.78294282e8 | 4.0 | 2951.0 | 4.0 |
| 9.7829426e8 | 4.0 | 1214.0 | 4.0 |
| 9.78294282e8 | 4.0 | 1036.0 | 4.0 |
| 9.78294199e8 | 4.0 | 260.0 | 5.0 |
| 9.7829423e8 | 4.0 | 2028.0 | 5.0 |
| 9.78294008e8 | 4.0 | 480.0 | 4.0 |
| 9.78294199e8 | 4.0 | 1196.0 | 2.0 |
| 9.78294199e8 | 4.0 | 1198.0 | 5.0 |
| 9.78294282e8 | 4.0 | 1954.0 | 5.0 |
| 9.78293964e8 | 4.0 | 1097.0 | 4.0 |
| 9.7829426e8 | 4.0 | 3418.0 | 4.0 |
| 9.7829426e8 | 4.0 | 3702.0 | 4.0 |
| 9.7829423e8 | 4.0 | 2366.0 | 4.0 |
| 9.78294199e8 | 4.0 | 1387.0 | 5.0 |
| 9.78294008e8 | 4.0 | 3527.0 | 1.0 |
| 9.7829423e8 | 4.0 | 1201.0 | 5.0 |
| 9.7829423e8 | 4.0 | 2692.0 | 5.0 |
| 9.7829423e8 | 4.0 | 2947.0 | 5.0 |
| 9.7829426e8 | 4.0 | 1240.0 | 5.0 |
| 9.7824317e8 | 5.0 | 2987.0 | 4.0 |
| 9.78242607e8 | 5.0 | 2333.0 | 4.0 |
| 9.78244759e8 | 5.0 | 1175.0 | 5.0 |
| 9.78245037e8 | 5.0 | 39.0 | 3.0 |
| 9.78246585e8 | 5.0 | 288.0 | 2.0 |
| 9.78243121e8 | 5.0 | 2337.0 | 5.0 |
| 9.78245513e8 | 5.0 | 1535.0 | 4.0 |
| 9.78245645e8 | 5.0 | 1392.0 | 4.0 |
| 9.78246033e8 | 5.0 | 2268.0 | 2.0 |
| 9.78245695e8 | 5.0 | 1466.0 | 3.0 |
| 9.78244493e8 | 5.0 | 860.0 | 2.0 |
| 9.78246108e8 | 5.0 | 1683.0 | 3.0 |
| 9.78245334e8 | 5.0 | 866.0 | 4.0 |
| 9.7824454e8 | 5.0 | 1684.0 | 3.0 |
| 9.78245645e8 | 5.0 | 2916.0 | 1.0 |
| 9.78241981e8 | 5.0 | 2770.0 | 4.0 |
| 9.78245422e8 | 5.0 | 215.0 | 3.0 |
| 9.78245513e8 | 5.0 | 1759.0 | 4.0 |
| 9.78244001e8 | 5.0 | 501.0 | 1.0 |
| 9.78243803e8 | 5.0 | 3578.0 | 2.0 |
| 9.78245999e8 | 5.0 | 506.0 | 4.0 |
| 9.78241112e8 | 5.0 | 1250.0 | 5.0 |
| 9.7824317e8 | 5.0 | 3793.0 | 2.0 |
| 9.78245829e8 | 5.0 | 509.0 | 4.0 |
| 9.78244692e8 | 5.0 | 41.0 | 4.0 |
| 9.78245645e8 | 5.0 | 1610.0 | 4.0 |
| 9.7824574e8 | 5.0 | 2058.0 | 1.0 |
| 9.78243937e8 | 5.0 | 3799.0 | 3.0 |
| 9.78241556e8 | 5.0 | 2997.0 | 5.0 |
| 9.78245334e8 | 5.0 | 47.0 | 3.0 |
| 9.78243085e8 | 5.0 | 2700.0 | 4.0 |
| 9.78244177e8 | 5.0 | 296.0 | 4.0 |
| 9.78244808e8 | 5.0 | 581.0 | 3.0 |
| 9.78244025e8 | 5.0 | 1617.0 | 3.0 |
| 9.78244759e8 | 5.0 | 728.0 | 4.0 |
| 9.78242934e8 | 5.0 | 299.0 | 3.0 |
| 9.78246162e8 | 5.0 | 3079.0 | 2.0 |
| 9.78242977e8 | 5.0 | 2560.0 | 4.0 |
| 9.78246479e8 | 5.0 | 1909.0 | 3.0 |
| 9.78245763e8 | 5.0 | 150.0 | 2.0 |
| 9.78245829e8 | 5.0 | 224.0 | 3.0 |
| 9.78244568e8 | 5.0 | 3728.0 | 2.0 |
| 9.78246528e8 | 5.0 | 229.0 | 3.0 |
| 9.78245916e8 | 5.0 | 6.0 | 2.0 |
| 9.78245422e8 | 5.0 | 3006.0 | 3.0 |
| 9.7824139e8 | 5.0 | 2858.0 | 4.0 |
| 9.78244114e8 | 5.0 | 1046.0 | 5.0 |
| 9.78245891e8 | 5.0 | 515.0 | 4.0 |
| 9.7824454e8 | 5.0 | 800.0 | 2.0 |
| 9.78244205e8 | 5.0 | 50.0 | 5.0 |
| 9.78246479e8 | 5.0 | 52.0 | 2.0 |
| 9.78246426e8 | 5.0 | 1191.0 | 2.0 |
| 9.78244493e8 | 5.0 | 1192.0 | 5.0 |
| 9.78245763e8 | 5.0 | 733.0 | 1.0 |
| 9.78243054e8 | 5.0 | 3081.0 | 3.0 |
| 9.78245999e8 | 5.0 | 377.0 | 4.0 |
| 9.7824579e8 | 5.0 | 2353.0 | 3.0 |
| 9.78245948e8 | 5.0 | 1268.0 | 2.0 |
| 9.78244114e8 | 5.0 | 3083.0 | 5.0 |
| 9.7824645e8 | 5.0 | 2427.0 | 5.0 |
| 9.78243896e8 | 5.0 | 3513.0 | 1.0 |
| 9.78242541e8 | 5.0 | 2428.0 | 3.0 |
| 9.78241981e8 | 5.0 | 2355.0 | 5.0 |
| 9.78244667e8 | 5.0 | 2282.0 | 3.0 |
| 9.78243869e8 | 5.0 | 3514.0 | 2.0 |
| 9.7824454e8 | 5.0 | 1554.0 | 3.0 |
| 9.78244624e8 | 5.0 | 1912.0 | 3.0 |
| 9.78244177e8 | 5.0 | 593.0 | 4.0 |
| 9.78245568e8 | 5.0 | 2359.0 | 3.0 |
| 9.78242607e8 | 5.0 | 2716.0 | 3.0 |
| 9.78246576e8 | 5.0 | 1485.0 | 3.0 |
| 9.78241072e8 | 5.0 | 2717.0 | 1.0 |
| 9.78244493e8 | 5.0 | 2571.0 | 5.0 |
| 9.78244568e8 | 5.0 | 2289.0 | 4.0 |
| 9.78244624e8 | 5.0 | 162.0 | 4.0 |
| 9.7824139e8 | 5.0 | 1127.0 | 1.0 |
| 9.78242016e8 | 5.0 | 3016.0 | 4.0 |
| 9.78243121e8 | 5.0 | 2070.0 | 2.0 |
| 9.78244517e8 | 5.0 | 1704.0 | 3.0 |
| 9.78244852e8 | 5.0 | 3163.0 | 5.0 |
| 9.78244759e8 | 5.0 | 2437.0 | 2.0 |
| 9.78244808e8 | 5.0 | 2291.0 | 5.0 |
| 9.78245314e8 | 5.0 | 1635.0 | 4.0 |
| 9.78245314e8 | 5.0 | 1279.0 | 1.0 |
| 9.78243121e8 | 5.0 | 2721.0 | 3.0 |
| 9.78242788e8 | 5.0 | 2723.0 | 4.0 |
| 9.78246162e8 | 5.0 | 1921.0 | 4.0 |
| 9.78246162e8 | 5.0 | 2725.0 | 2.0 |
| 9.78245314e8 | 5.0 | 1923.0 | 3.0 |
| 9.78242607e8 | 5.0 | 2580.0 | 4.0 |
| 9.78245948e8 | 5.0 | 3386.0 | 2.0 |
| 9.78243896e8 | 5.0 | 3744.0 | 1.0 |
| 9.78242847e8 | 5.0 | 968.0 | 3.0 |
| 9.78244493e8 | 5.0 | 896.0 | 4.0 |
| 9.78245916e8 | 5.0 | 3020.0 | 1.0 |
| 9.78244603e8 | 5.0 | 1788.0 | 3.0 |
| 9.78244177e8 | 5.0 | 318.0 | 3.0 |
| 9.78244568e8 | 5.0 | 176.0 | 4.0 |
| 9.78244893e8 | 5.0 | 461.0 | 3.0 |
| 9.78244177e8 | 5.0 | 608.0 | 4.0 |
| 9.78245829e8 | 5.0 | 1429.0 | 3.0 |
| 9.78244808e8 | 5.0 | 2159.0 | 1.0 |
| 9.78245891e8 | 5.0 | 1715.0 | 4.0 |
| 9.78245916e8 | 5.0 | 1643.0 | 3.0 |
| 9.78245916e8 | 5.0 | 3249.0 | 1.0 |
| 9.78243085e8 | 5.0 | 3176.0 | 2.0 |
| 9.78244205e8 | 5.0 | 1719.0 | 3.0 |
| 9.78243085e8 | 5.0 | 2806.0 | 2.0 |
| 9.78242788e8 | 5.0 | 2734.0 | 2.0 |
| 9.78244667e8 | 5.0 | 1649.0 | 4.0 |
| 9.78245863e8 | 5.0 | 321.0 | 3.0 |
| 9.78242934e8 | 5.0 | 2013.0 | 3.0 |
| 9.78245645e8 | 5.0 | 3100.0 | 1.0 |
| 9.7824579e8 | 5.0 | 2952.0 | 2.0 |
| 9.78244177e8 | 5.0 | 1213.0 | 5.0 |
| 9.78245966e8 | 5.0 | 1794.0 | 3.0 |
| 9.78242323e8 | 5.0 | 2599.0 | 5.0 |
| 9.78245314e8 | 5.0 | 1500.0 | 3.0 |
| 9.78246576e8 | 5.0 | 3105.0 | 2.0 |
| 9.78242541e8 | 5.0 | 2959.0 | 4.0 |
| 9.78244962e8 | 5.0 | 1509.0 | 3.0 |
| 9.78245763e8 | 5.0 | 1721.0 | 1.0 |
| 9.78246108e8 | 5.0 | 1722.0 | 2.0 |
| 9.78245314e8 | 5.0 | 1650.0 | 3.0 |
| 9.78241072e8 | 5.0 | 908.0 | 4.0 |
| 9.78245999e8 | 5.0 | 1580.0 | 4.0 |
| 9.78245891e8 | 5.0 | 1653.0 | 2.0 |
| 9.78245708e8 | 5.0 | 2384.0 | 3.0 |
| 9.78245763e8 | 5.0 | 1729.0 | 4.0 |
| 9.78245916e8 | 5.0 | 3476.0 | 3.0 |
| 9.78245445e8 | 5.0 | 2890.0 | 4.0 |
| 9.78242323e8 | 5.0 | 3113.0 | 1.0 |
| 9.78244053e8 | 5.0 | 2028.0 | 2.0 |
| 9.78245645e8 | 5.0 | 16.0 | 3.0 |
| 9.78245037e8 | 5.0 | 265.0 | 3.0 |
| 9.78246555e8 | 5.0 | 2029.0 | 4.0 |
| 9.78246108e8 | 5.0 | 194.0 | 3.0 |
| 9.78246504e8 | 5.0 | 551.0 | 4.0 |
| 9.78242977e8 | 5.0 | 1513.0 | 4.0 |
| 9.78244962e8 | 5.0 | 3046.0 | 3.0 |
| 9.7824264e8 | 5.0 | 2318.0 | 4.0 |
| 9.78245568e8 | 5.0 | 1517.0 | 4.0 |
| 9.78244205e8 | 5.0 | 1089.0 | 5.0 |
| 9.78245065e8 | 5.0 | 3260.0 | 4.0 |
| 9.7824274e8 | 5.0 | 913.0 | 5.0 |
| 9.78244603e8 | 5.0 | 1730.0 | 4.0 |
| 9.78242323e8 | 5.0 | 3408.0 | 3.0 |
| 9.78242541e8 | 5.0 | 3409.0 | 3.0 |
| 9.78242607e8 | 5.0 | 2607.0 | 2.0 |
| 9.7824483e8 | 5.0 | 1449.0 | 4.0 |
| 9.7824574e8 | 5.0 | 1732.0 | 5.0 |
| 9.7824645e8 | 5.0 | 1733.0 | 3.0 |
| 9.78242679e8 | 5.0 | 2390.0 | 4.0 |
| 9.78245334e8 | 5.0 | 1734.0 | 4.0 |
| 9.78246162e8 | 5.0 | 3266.0 | 2.0 |
| 9.78244893e8 | 5.0 | 3267.0 | 4.0 |
| 9.78241072e8 | 5.0 | 919.0 | 4.0 |
| 9.78243803e8 | 5.0 | 3624.0 | 3.0 |
| 9.78243054e8 | 5.0 | 2395.0 | 5.0 |
| 9.78245548e8 | 5.0 | 1594.0 | 1.0 |
| 9.78241434e8 | 5.0 | 2683.0 | 3.0 |
| 9.78245891e8 | 5.0 | 412.0 | 2.0 |
| 9.7824208e8 | 5.0 | 2759.0 | 3.0 |
| 9.7824454e8 | 5.0 | 994.0 | 5.0 |
| 9.78246576e8 | 5.0 | 1884.0 | 3.0 |
| 9.78245568e8 | 5.0 | 1885.0 | 4.0 |
| 9.78245487e8 | 5.0 | 272.0 | 3.0 |
| 9.78242934e8 | 5.0 | 24.0 | 1.0 |
| 9.78241434e8 | 5.0 | 3051.0 | 2.0 |
| 9.78245863e8 | 5.0 | 348.0 | 4.0 |
| 9.78245445e8 | 5.0 | 2323.0 | 4.0 |
| 9.7824208e8 | 5.0 | 1093.0 | 2.0 |
| 9.78245065e8 | 5.0 | 29.0 | 5.0 |
| 9.78244603e8 | 5.0 | 562.0 | 4.0 |
| 9.78245065e8 | 5.0 | 1095.0 | 4.0 |
| 9.78246479e8 | 5.0 | 1527.0 | 3.0 |
| 9.78245037e8 | 5.0 | 1529.0 | 4.0 |
| 9.78244962e8 | 5.0 | 3418.0 | 3.0 |
| 9.7824139e8 | 5.0 | 2188.0 | 1.0 |
| 9.78245687e8 | 5.0 | 497.0 | 3.0 |
| 9.78246033e8 | 5.0 | 202.0 | 2.0 |
| 9.78245663e8 | 5.0 | 1747.0 | 2.0 |
| 9.78241981e8 | 5.0 | 2908.0 | 4.0 |
| 9.78243054e8 | 5.0 | 2762.0 | 3.0 |
| 9.78242977e8 | 5.0 | 2692.0 | 4.0 |
| 9.78245931e8 | 5.0 | 1966.0 | 2.0 |
| 9.78245065e8 | 5.0 | 3499.0 | 3.0 |
| 9.78246504e8 | 5.0 | 353.0 | 2.0 |
| 9.78244962e8 | 5.0 | 32.0 | 4.0 |
| 9.78244001e8 | 5.0 | 1243.0 | 3.0 |
| 9.78246426e8 | 5.0 | 1897.0 | 4.0 |
| 9.78244852e8 | 5.0 | 1171.0 | 4.0 |
| 9.78241981e8 | 5.0 | 3786.0 | 3.0 |
| 9.78244603e8 | 5.0 | 34.0 | 4.0 |
| 9.78241112e8 | 5.0 | 356.0 | 1.0 |
| 9.78245829e8 | 5.0 | 357.0 | 2.0 |
| 9.78244808e8 | 5.0 | 36.0 | 3.0 |
| 9.78244493e8 | 5.0 | 714.0 | 4.0 |
| 9.7823667e8 | 6.0 | 2406.0 | 5.0 |
| 9.7823667e8 | 6.0 | 1101.0 | 4.0 |
| 9.78238371e8 | 6.0 | 3717.0 | 4.0 |
| 9.78237691e8 | 6.0 | 1030.0 | 4.0 |
| 9.7823757e8 | 6.0 | 1688.0 | 5.0 |
| 9.78237767e8 | 6.0 | 1035.0 | 5.0 |
| 9.78238195e8 | 6.0 | 3578.0 | 4.0 |
| 9.7823757e8 | 6.0 | 364.0 | 4.0 |
| 9.7823667e8 | 6.0 | 3501.0 | 5.0 |
| 9.78236075e8 | 6.0 | 3072.0 | 4.0 |
| 9.78237909e8 | 6.0 | 368.0 | 4.0 |
| 9.78237379e8 | 6.0 | 296.0 | 2.0 |
| 9.7823757e8 | 6.0 | 48.0 | 5.0 |
| 9.78238036e8 | 6.0 | 3074.0 | 5.0 |
| 9.78236771e8 | 6.0 | 1188.0 | 4.0 |
| 9.78238036e8 | 6.0 | 3508.0 | 3.0 |
| 9.78237511e8 | 6.0 | 588.0 | 4.0 |
| 9.78237008e8 | 6.0 | 1.0 | 4.0 |
| 9.78236219e8 | 6.0 | 1043.0 | 4.0 |
| 9.78236809e8 | 6.0 | 2858.0 | 1.0 |
| 9.78236383e8 | 6.0 | 377.0 | 3.0 |
| 9.78237232e8 | 6.0 | 590.0 | 3.0 |
| 9.78237511e8 | 6.0 | 595.0 | 4.0 |
| 9.78239019e8 | 6.0 | 597.0 | 5.0 |
| 9.78237909e8 | 6.0 | 383.0 | 4.0 |
| 9.78236975e8 | 6.0 | 2506.0 | 3.0 |
| 9.78236719e8 | 6.0 | 3524.0 | 3.0 |
| 9.7823757e8 | 6.0 | 1566.0 | 4.0 |
| 9.78238948e8 | 6.0 | 1569.0 | 4.0 |
| 9.78236122e8 | 6.0 | 2006.0 | 4.0 |
| 9.78236567e8 | 6.0 | 2081.0 | 4.0 |
| 9.78236975e8 | 6.0 | 2082.0 | 3.0 |
| 9.78237813e8 | 6.0 | 3600.0 | 3.0 |
| 9.78237767e8 | 6.0 | 3604.0 | 5.0 |
| 9.78236612e8 | 6.0 | 2802.0 | 4.0 |
| 9.78236219e8 | 6.0 | 3534.0 | 4.0 |
| 9.7823823e8 | 6.0 | 3536.0 | 5.0 |
| 9.78236219e8 | 6.0 | 1210.0 | 3.0 |
| 9.78238195e8 | 6.0 | 3753.0 | 5.0 |
| 9.78238036e8 | 6.0 | 3682.0 | 3.0 |
| 9.78237813e8 | 6.0 | 2017.0 | 3.0 |
| 9.78236519e8 | 6.0 | 3685.0 | 3.0 |
| 9.78237813e8 | 6.0 | 3610.0 | 3.0 |
| 9.78236519e8 | 6.0 | 1296.0 | 3.0 |
| 9.78237444e8 | 6.0 | 838.0 | 4.0 |
| 9.78238036e8 | 6.0 | 1007.0 | 3.0 |
| 9.78237767e8 | 6.0 | 1947.0 | 5.0 |
| 9.78237273e8 | 6.0 | 2966.0 | 5.0 |
| 9.78237909e8 | 6.0 | 266.0 | 4.0 |
| 9.78236383e8 | 6.0 | 17.0 | 4.0 |
| 9.78236567e8 | 6.0 | 3699.0 | 4.0 |
| 9.78236383e8 | 6.0 | 1441.0 | 4.0 |
| 9.7823667e8 | 6.0 | 1088.0 | 5.0 |
| 9.78236122e8 | 6.0 | 912.0 | 4.0 |
| 9.7823757e8 | 6.0 | 199.0 | 5.0 |
| 9.78237767e8 | 6.0 | 914.0 | 5.0 |
| 9.7823823e8 | 6.0 | 3408.0 | 5.0 |
| 9.78236876e8 | 6.0 | 1806.0 | 3.0 |
| 9.78238256e8 | 6.0 | 3624.0 | 4.0 |
| 9.7823667e8 | 6.0 | 2469.0 | 3.0 |
| 9.78236809e8 | 6.0 | 2396.0 | 4.0 |
| 9.78236567e8 | 6.0 | 2100.0 | 3.0 |
| 9.78236612e8 | 6.0 | 1959.0 | 3.0 |
| 9.78237034e8 | 6.0 | 2321.0 | 3.0 |
| 9.78237691e8 | 6.0 | 1380.0 | 5.0 |
| 9.78238851e8 | 6.0 | 920.0 | 4.0 |
| 9.78236876e8 | 6.0 | 569.0 | 4.0 |
| 9.78236567e8 | 6.0 | 1674.0 | 4.0 |
| 9.78238288e8 | 6.0 | 3565.0 | 4.0 |
| 9.78237767e8 | 6.0 | 1028.0 | 4.0 |
| 9.78237444e8 | 6.0 | 34.0 | 4.0 |
| 9.78234737e8 | 7.0 | 648.0 | 4.0 |
| 9.78234874e8 | 7.0 | 861.0 | 4.0 |
| 9.78234842e8 | 7.0 | 2916.0 | 5.0 |
| 9.78234737e8 | 7.0 | 3578.0 | 3.0 |
| 9.78234737e8 | 7.0 | 3793.0 | 3.0 |
| 9.78234786e8 | 7.0 | 1610.0 | 5.0 |
| 9.78234786e8 | 7.0 | 589.0 | 5.0 |
| 9.78234842e8 | 7.0 | 6.0 | 4.0 |
| 9.78234632e8 | 7.0 | 442.0 | 4.0 |
| 9.78234842e8 | 7.0 | 733.0 | 5.0 |
| 9.7823481e8 | 7.0 | 377.0 | 3.0 |
| 9.78234874e8 | 7.0 | 2353.0 | 5.0 |
| 9.78234632e8 | 7.0 | 1196.0 | 5.0 |
| 9.78234786e8 | 7.0 | 2571.0 | 5.0 |
| 9.78234874e8 | 7.0 | 380.0 | 5.0 |
| 9.78234737e8 | 7.0 | 1997.0 | 5.0 |
| 9.78234581e8 | 7.0 | 1270.0 | 4.0 |
| 9.78234786e8 | 7.0 | 457.0 | 5.0 |
| 9.78234874e8 | 7.0 | 1573.0 | 4.0 |
| 9.78234737e8 | 7.0 | 3753.0 | 4.0 |
| 9.78234898e8 | 7.0 | 3107.0 | 3.0 |
| 9.78234842e8 | 7.0 | 474.0 | 5.0 |
| 9.78234874e8 | 7.0 | 1722.0 | 4.0 |
| 9.78234874e8 | 7.0 | 3256.0 | 5.0 |
| 9.78234842e8 | 7.0 | 1580.0 | 4.0 |
| 9.78234786e8 | 7.0 | 110.0 | 5.0 |
| 9.78234659e8 | 7.0 | 1221.0 | 4.0 |
| 9.78234786e8 | 7.0 | 2028.0 | 5.0 |
| 9.78234607e8 | 7.0 | 480.0 | 4.0 |
| 9.78234874e8 | 7.0 | 349.0 | 5.0 |
| 9.7823481e8 | 7.0 | 3418.0 | 3.0 |
| 9.78229571e8 | 8.0 | 39.0 | 3.0 |
| 9.7823012e8 | 8.0 | 2336.0 | 3.0 |
| 9.78229391e8 | 8.0 | 288.0 | 5.0 |
| 9.78231982e8 | 8.0 | 3425.0 | 3.0 |
| 9.78230852e8 | 8.0 | 2268.0 | 3.0 |
| 9.78230052e8 | 8.0 | 1466.0 | 4.0 |
| 9.78229702e8 | 8.0 | 1393.0 | 5.0 |
| 9.78230852e8 | 8.0 | 1682.0 | 4.0 |
| 9.78229172e8 | 8.0 | 2916.0 | 5.0 |
| 9.78230483e8 | 8.0 | 506.0 | 3.0 |
| 9.78230435e8 | 8.0 | 508.0 | 3.0 |
| 9.78233462e8 | 8.0 | 3213.0 | 3.0 |
| 9.78232754e8 | 8.0 | 42.0 | 3.0 |
| 9.78230943e8 | 8.0 | 650.0 | 5.0 |
| 9.78232754e8 | 8.0 | 3500.0 | 3.0 |
| 9.78229857e8 | 8.0 | 296.0 | 5.0 |
| 9.7823055e8 | 8.0 | 3147.0 | 5.0 |
| 9.78230248e8 | 8.0 | 3148.0 | 3.0 |
| 9.78230943e8 | 8.0 | 2702.0 | 3.0 |
| 9.78229293e8 | 8.0 | 2278.0 | 3.0 |
| 9.78231493e8 | 8.0 | 1476.0 | 3.0 |
| 9.78229293e8 | 8.0 | 2490.0 | 2.0 |
| 9.78229138e8 | 8.0 | 589.0 | 5.0 |
| 9.78231592e8 | 8.0 | 1836.0 | 4.0 |
| 9.78231121e8 | 8.0 | 1693.0 | 3.0 |
| 9.78230611e8 | 8.0 | 150.0 | 4.0 |
| 9.7823115e8 | 8.0 | 151.0 | 4.0 |
| 9.78233496e8 | 8.0 | 1.0 | 4.0 |
| 9.78232056e8 | 8.0 | 510.0 | 3.0 |
| 9.78232203e8 | 8.0 | 4.0 | 3.0 |
| 9.78230227e8 | 8.0 | 3006.0 | 3.0 |
| 9.78229817e8 | 8.0 | 2858.0 | 5.0 |
| 9.78230966e8 | 8.0 | 1621.0 | 3.0 |
| 9.78229524e8 | 8.0 | 1265.0 | 5.0 |
| 9.78229347e8 | 8.0 | 733.0 | 3.0 |
| 9.78229204e8 | 8.0 | 377.0 | 4.0 |
| 9.78232231e8 | 8.0 | 3155.0 | 3.0 |
| 9.78229204e8 | 8.0 | 2427.0 | 5.0 |
| 9.78230286e8 | 8.0 | 58.0 | 5.0 |
| 9.78230886e8 | 8.0 | 2712.0 | 3.0 |
| 9.7823189e8 | 8.0 | 2429.0 | 2.0 |
| 9.78230577e8 | 8.0 | 1840.0 | 4.0 |
| 9.78229172e8 | 8.0 | 2571.0 | 5.0 |
| 9.78229293e8 | 8.0 | 1916.0 | 5.0 |
| 9.78231982e8 | 8.0 | 1488.0 | 3.0 |
| 9.78231637e8 | 8.0 | 230.0 | 4.0 |
| 9.78230391e8 | 8.0 | 1120.0 | 4.0 |
| 9.78230435e8 | 8.0 | 161.0 | 3.0 |
| 9.78229246e8 | 8.0 | 163.0 | 5.0 |
| 9.78230391e8 | 8.0 | 1411.0 | 5.0 |
| 9.78230611e8 | 8.0 | 524.0 | 5.0 |
| 9.78229614e8 | 8.0 | 1059.0 | 3.0 |
| 9.78229857e8 | 8.0 | 527.0 | 4.0 |
| 9.78231173e8 | 8.0 | 454.0 | 3.0 |
| 9.78231659e8 | 8.0 | 1701.0 | 4.0 |
| 9.78233462e8 | 8.0 | 1274.0 | 5.0 |
| 9.78233526e8 | 8.0 | 741.0 | 5.0 |
| 9.782299e8 | 8.0 | 1704.0 | 5.0 |
| 9.78229614e8 | 8.0 | 1277.0 | 3.0 |
| 9.78229702e8 | 8.0 | 2291.0 | 5.0 |
| 9.78229702e8 | 8.0 | 1639.0 | 5.0 |
| 9.78231687e8 | 8.0 | 3528.0 | 4.0 |
| 9.78231252e8 | 8.0 | 2297.0 | 3.0 |
| 9.78229979e8 | 8.0 | 3386.0 | 3.0 |
| 9.78229347e8 | 8.0 | 2006.0 | 3.0 |
| 9.78229857e8 | 8.0 | 608.0 | 5.0 |
| 9.78231173e8 | 8.0 | 465.0 | 5.0 |
| 9.7823189e8 | 8.0 | 1711.0 | 3.0 |
| 9.78230391e8 | 8.0 | 538.0 | 3.0 |
| 9.78229138e8 | 8.0 | 393.0 | 2.0 |
| 9.7823055e8 | 8.0 | 2442.0 | 4.0 |
| 9.782308e8 | 8.0 | 1357.0 | 4.0 |
| 9.78230286e8 | 8.0 | 73.0 | 4.0 |
| 9.78230391e8 | 8.0 | 3246.0 | 4.0 |
| 9.78232012e8 | 8.0 | 3173.0 | 2.0 |
| 9.7822896e8 | 8.0 | 1573.0 | 4.0 |
| 9.78232056e8 | 8.0 | 105.0 | 4.0 |
| 9.78228789e8 | 8.0 | 1210.0 | 4.0 |
| 9.78229817e8 | 8.0 | 1213.0 | 5.0 |
| 9.78230943e8 | 8.0 | 253.0 | 5.0 |
| 9.78230666e8 | 8.0 | 3105.0 | 4.0 |
| 9.78229246e8 | 8.0 | 3107.0 | 5.0 |
| 9.78230184e8 | 8.0 | 3250.0 | 3.0 |
| 9.78230013e8 | 8.0 | 3252.0 | 3.0 |
| 9.782308e8 | 8.0 | 1721.0 | 5.0 |
| 9.78230687e8 | 8.0 | 476.0 | 3.0 |
| 9.78229347e8 | 8.0 | 3256.0 | 5.0 |
| 9.78228882e8 | 8.0 | 908.0 | 5.0 |
| 9.78247143e8 | 8.0 | 3257.0 | 3.0 |
| 9.78230886e8 | 8.0 | 1653.0 | 5.0 |
| 9.78229293e8 | 8.0 | 1580.0 | 4.0 |
| 9.78229172e8 | 8.0 | 110.0 | 5.0 |
| 9.78228832e8 | 8.0 | 111.0 | 5.0 |
| 9.78231571e8 | 8.0 | 3259.0 | 4.0 |
| 9.78230852e8 | 8.0 | 3186.0 | 4.0 |
| 9.78230943e8 | 8.0 | 1589.0 | 4.0 |
| 9.78231802e8 | 8.0 | 2023.0 | 3.0 |
| 9.78230756e8 | 8.0 | 14.0 | 4.0 |
| 9.78229138e8 | 8.0 | 2028.0 | 5.0 |
| 9.78230248e8 | 8.0 | 337.0 | 5.0 |
| 9.78229571e8 | 8.0 | 265.0 | 4.0 |
| 9.78230095e8 | 8.0 | 16.0 | 4.0 |
| 9.78231614e8 | 8.0 | 266.0 | 4.0 |
| 9.78229571e8 | 8.0 | 17.0 | 4.0 |
| 9.78232056e8 | 8.0 | 2314.0 | 3.0 |
| 9.78229391e8 | 8.0 | 2600.0 | 2.0 |
| 9.7822896e8 | 8.0 | 480.0 | 5.0 |
| 9.78231121e8 | 8.0 | 269.0 | 4.0 |
| 9.78229204e8 | 8.0 | 555.0 | 4.0 |
| 9.78232056e8 | 8.0 | 1801.0 | 3.0 |
| 9.78229957e8 | 8.0 | 3260.0 | 3.0 |
| 9.78230013e8 | 8.0 | 1730.0 | 4.0 |
| 9.78230611e8 | 8.0 | 1660.0 | 3.0 |
| 9.78229138e8 | 8.0 | 3265.0 | 5.0 |
| 9.78229614e8 | 8.0 | 1735.0 | 4.0 |
| 9.78229204e8 | 8.0 | 3267.0 | 5.0 |
| 9.78228882e8 | 8.0 | 3481.0 | 4.0 |
| 9.78229524e8 | 8.0 | 2396.0 | 5.0 |
| 9.78230152e8 | 8.0 | 2686.0 | 4.0 |
| 9.78231429e8 | 8.0 | 2688.0 | 3.0 |
| 9.78230943e8 | 8.0 | 345.0 | 3.0 |
| 9.78231802e8 | 8.0 | 2320.0 | 2.0 |
| 9.78230966e8 | 8.0 | 24.0 | 4.0 |
| 9.78229477e8 | 8.0 | 25.0 | 5.0 |
| 9.78230483e8 | 8.0 | 2324.0 | 3.0 |
| 9.78229347e8 | 8.0 | 349.0 | 4.0 |
| 9.78230184e8 | 8.0 | 562.0 | 5.0 |
| 9.782299e8 | 8.0 | 2329.0 | 5.0 |
| 9.78231031e8 | 8.0 | 1810.0 | 2.0 |
| 9.78231031e8 | 8.0 | 2541.0 | 3.0 |
| 9.78229138e8 | 8.0 | 3418.0 | 3.0 |
| 9.78230356e8 | 8.0 | 1673.0 | 5.0 |
| 9.78230052e8 | 8.0 | 2908.0 | 3.0 |
| 9.78230649e8 | 8.0 | 1678.0 | 5.0 |
| 9.78229138e8 | 8.0 | 2692.0 | 5.0 |
| 9.78232203e8 | 8.0 | 1027.0 | 4.0 |
| 9.78229347e8 | 8.0 | 2699.0 | 5.0 |
| 9.78231802e8 | 8.0 | 282.0 | 3.0 |
| 9.78230013e8 | 8.0 | 36.0 | 4.0 |
| 9.78226495e8 | 9.0 | 2268.0 | 4.0 |
| 9.78226248e8 | 9.0 | 1466.0 | 4.0 |
| 9.78226081e8 | 9.0 | 1393.0 | 3.0 |
| 9.78226665e8 | 9.0 | 861.0 | 2.0 |
| 9.78226302e8 | 9.0 | 1682.0 | 4.0 |
| 9.78225709e8 | 9.0 | 3717.0 | 3.0 |
| 9.78226315e8 | 9.0 | 508.0 | 3.0 |
| 9.78225581e8 | 9.0 | 3793.0 | 4.0 |
| 9.78225898e8 | 9.0 | 720.0 | 4.0 |
| 9.78226678e8 | 9.0 | 367.0 | 3.0 |
| 9.78226093e8 | 9.0 | 47.0 | 5.0 |
| 9.78226081e8 | 9.0 | 3147.0 | 4.0 |
| 9.78225827e8 | 9.0 | 3148.0 | 3.0 |
| 9.78225924e8 | 9.0 | 1617.0 | 4.0 |
| 9.78226665e8 | 9.0 | 2278.0 | 4.0 |
| 9.78226165e8 | 9.0 | 223.0 | 4.0 |
| 9.78226041e8 | 9.0 | 150.0 | 3.0 |
| 9.78225686e8 | 9.0 | 3298.0 | 4.0 |
| 9.78225952e8 | 9.0 | 1.0 | 5.0 |
| 9.78226041e8 | 9.0 | 3006.0 | 3.0 |
| 9.78225333e8 | 9.0 | 2858.0 | 4.0 |
| 9.78225177e8 | 9.0 | 3948.0 | 3.0 |
| 9.78225333e8 | 9.0 | 50.0 | 4.0 |
| 9.78226066e8 | 9.0 | 1265.0 | 4.0 |
| 9.78226564e8 | 9.0 | 805.0 | 3.0 |
| 9.7822557e8 | 9.0 | 3510.0 | 3.0 |
| 9.7822615e8 | 9.0 | 377.0 | 3.0 |
| 9.78225489e8 | 9.0 | 1552.0 | 2.0 |
| 9.78226434e8 | 9.0 | 590.0 | 5.0 |
| 9.7822573e8 | 9.0 | 3513.0 | 3.0 |
| 9.78226054e8 | 9.0 | 2355.0 | 4.0 |
| 9.78226343e8 | 9.0 | 1912.0 | 3.0 |
| 9.78225314e8 | 9.0 | 593.0 | 5.0 |
| 9.78225984e8 | 9.0 | 2571.0 | 5.0 |
| 9.78226473e8 | 9.0 | 597.0 | 3.0 |
| 9.78226277e8 | 9.0 | 300.0 | 4.0 |
| 9.78226291e8 | 9.0 | 1777.0 | 4.0 |
| 9.78226023e8 | 9.0 | 162.0 | 4.0 |
| 9.78226599e8 | 9.0 | 524.0 | 4.0 |
| 9.78225718e8 | 9.0 | 3301.0 | 2.0 |
| 9.78226261e8 | 9.0 | 1343.0 | 3.0 |
| 9.78225303e8 | 9.0 | 527.0 | 5.0 |
| 9.78225984e8 | 9.0 | 3160.0 | 3.0 |
| 9.78226564e8 | 9.0 | 529.0 | 5.0 |
| 9.78226006e8 | 9.0 | 457.0 | 5.0 |
| 9.78225924e8 | 9.0 | 1704.0 | 4.0 |
| 9.78225898e8 | 9.0 | 745.0 | 4.0 |
| 9.78225746e8 | 9.0 | 3452.0 | 2.0 |
| 9.78226678e8 | 9.0 | 2294.0 | 4.0 |
| 9.78226368e8 | 9.0 | 1921.0 | 4.0 |
| 9.78226109e8 | 9.0 | 1639.0 | 4.0 |
| 9.78226165e8 | 9.0 | 1923.0 | 5.0 |
| 9.78226109e8 | 9.0 | 1784.0 | 3.0 |
| 9.78226261e8 | 9.0 | 1060.0 | 4.0 |
| 9.78225883e8 | 9.0 | 318.0 | 5.0 |
| 9.78225898e8 | 9.0 | 608.0 | 4.0 |
| 9.78226644e8 | 9.0 | 1356.0 | 3.0 |
| 9.78226207e8 | 9.0 | 1358.0 | 4.0 |
| 9.78224908e8 | 9.0 | 3178.0 | 3.0 |
| 9.78224859e8 | 9.0 | 3751.0 | 4.0 |
| 9.78224893e8 | 9.0 | 1210.0 | 4.0 |
| 9.78225758e8 | 9.0 | 3826.0 | 2.0 |
| 9.78225314e8 | 9.0 | 1213.0 | 4.0 |
| 9.78225613e8 | 9.0 | 3755.0 | 2.0 |
| 9.78225952e8 | 9.0 | 2599.0 | 2.0 |
| 9.78226463e8 | 9.0 | 2302.0 | 4.0 |
| 9.78226123e8 | 9.0 | 1500.0 | 4.0 |
| 9.78226123e8 | 9.0 | 2959.0 | 4.0 |
| 9.78225333e8 | 9.0 | 1148.0 | 4.0 |
| 9.78226197e8 | 9.0 | 2166.0 | 4.0 |
| 9.78226248e8 | 9.0 | 1721.0 | 5.0 |
| 9.78226165e8 | 9.0 | 3253.0 | 4.0 |
| 9.78226216e8 | 9.0 | 3255.0 | 4.0 |
| 9.78226619e8 | 9.0 | 1653.0 | 4.0 |
| 9.78226495e8 | 9.0 | 838.0 | 3.0 |
| 9.78226233e8 | 9.0 | 1584.0 | 5.0 |
| 9.78224908e8 | 9.0 | 1221.0 | 4.0 |
| 9.78225968e8 | 9.0 | 1223.0 | 4.0 |
| 9.78226291e8 | 9.0 | 2890.0 | 5.0 |
| 9.78225908e8 | 9.0 | 2028.0 | 5.0 |
| 9.78225952e8 | 9.0 | 3114.0 | 4.0 |
| 9.78226599e8 | 9.0 | 16.0 | 4.0 |
| 9.78226448e8 | 9.0 | 480.0 | 4.0 |
| 9.78225968e8 | 9.0 | 1089.0 | 4.0 |
| 9.78224879e8 | 9.0 | 912.0 | 4.0 |
| 9.7822615e8 | 9.0 | 1446.0 | 3.0 |
| 9.7822557e8 | 9.0 | 3408.0 | 4.0 |
| 9.78225671e8 | 9.0 | 3623.0 | 4.0 |
| 9.78226248e8 | 9.0 | 778.0 | 5.0 |
| 9.7822615e8 | 9.0 | 1669.0 | 3.0 |
| 9.78225778e8 | 9.0 | 3484.0 | 2.0 |
| 9.78226261e8 | 9.0 | 412.0 | 3.0 |
| 9.78225153e8 | 9.0 | 3916.0 | 3.0 |
| 9.78226328e8 | 9.0 | 994.0 | 4.0 |
| 9.78225303e8 | 9.0 | 1233.0 | 3.0 |
| 9.78225429e8 | 9.0 | 1307.0 | 4.0 |
| 9.78226041e8 | 9.0 | 25.0 | 4.0 |
| 9.78226066e8 | 9.0 | 2324.0 | 5.0 |
| 9.78226564e8 | 9.0 | 349.0 | 4.0 |
| 9.78225401e8 | 9.0 | 920.0 | 3.0 |
| 9.78226448e8 | 9.0 | 3270.0 | 3.0 |
| 9.78225984e8 | 9.0 | 2762.0 | 4.0 |
| 9.78224859e8 | 9.0 | 1961.0 | 5.0 |
| 9.78225429e8 | 9.0 | 2692.0 | 4.0 |
| 9.78226006e8 | 9.0 | 1310.0 | 3.0 |
| 9.7822658e8 | 9.0 | 428.0 | 3.0 |
| 9.78228212e8 | 10.0 | 2622.0 | 5.0 |
| 9.78224925e8 | 10.0 | 648.0 | 4.0 |
| 9.78228408e8 | 10.0 | 2628.0 | 3.0 |
| 9.78226378e8 | 10.0 | 3358.0 | 5.0 |
| 9.78227125e8 | 10.0 | 3359.0 | 3.0 |
| 9.78226319e8 | 10.0 | 1682.0 | 5.0 |
| 9.78228655e8 | 10.0 | 1756.0 | 4.0 |
| 9.78230837e8 | 10.0 | 1320.0 | 3.0 |
| 9.78229208e8 | 10.0 | 2124.0 | 3.0 |
| 9.79167795e8 | 10.0 | 2125.0 | 5.0 |
| 9.78226572e8 | 10.0 | 1250.0 | 3.0 |
| 9.78229409e8 | 10.0 | 2054.0 | 4.0 |
| 9.79775409e8 | 10.0 | 1252.0 | 3.0 |
| 9.78226866e8 | 10.0 | 1253.0 | 5.0 |
| 9.7977524e8 | 10.0 | 720.0 | 5.0 |
| 9.78225997e8 | 10.0 | 3868.0 | 3.0 |
| 9.78226254e8 | 10.0 | 1254.0 | 3.0 |
| 9.78230562e8 | 10.0 | 3869.0 | 3.0 |
| 9.79168591e8 | 10.0 | 1256.0 | 3.0 |
| 9.78228153e8 | 10.0 | 3500.0 | 5.0 |
| 9.782273e8 | 10.0 | 1257.0 | 5.0 |
| 9.78228911e8 | 10.0 | 3501.0 | 5.0 |
| 9.78226026e8 | 10.0 | 2997.0 | 4.0 |
| 9.79168564e8 | 10.0 | 1259.0 | 3.0 |
| 9.78227051e8 | 10.0 | 653.0 | 4.0 |
| 9.79776206e8 | 10.0 | 1831.0 | 5.0 |
| 9.78227696e8 | 10.0 | 3363.0 | 5.0 |
| 9.78228747e8 | 10.0 | 586.0 | 3.0 |
| 9.78227028e8 | 10.0 | 587.0 | 5.0 |
| 9.79168346e8 | 10.0 | 3438.0 | 3.0 |
| 9.782259e8 | 10.0 | 588.0 | 4.0 |
| 9.78229894e8 | 10.0 | 3439.0 | 2.0 |
| 9.78226128e8 | 10.0 | 589.0 | 4.0 |
| 9.78230253e8 | 10.0 | 1690.0 | 4.0 |
| 9.78226103e8 | 10.0 | 3296.0 | 4.0 |
| 9.782259e8 | 10.0 | 223.0 | 2.0 |
| 9.78226319e8 | 10.0 | 150.0 | 5.0 |
| 9.78227087e8 | 10.0 | 2496.0 | 4.0 |
| 9.78226474e8 | 10.0 | 1.0 | 5.0 |
| 9.78229979e8 | 10.0 | 2497.0 | 4.0 |
| 9.79168267e8 | 10.0 | 2.0 | 5.0 |
| 9.79776161e8 | 10.0 | 2498.0 | 4.0 |
| 9.78229917e8 | 10.0 | 153.0 | 3.0 |
| 9.78227763e8 | 10.0 | 7.0 | 4.0 |
| 9.78229171e8 | 10.0 | 2133.0 | 4.0 |
| 9.78224779e8 | 10.0 | 2135.0 | 4.0 |
| 9.7829548e8 | 10.0 | 3948.0 | 4.0 |
| 9.78230063e8 | 10.0 | 2136.0 | 4.0 |
| 9.78227842e8 | 10.0 | 2137.0 | 4.0 |
| 9.78227188e8 | 10.0 | 1408.0 | 4.0 |
| 9.78226425e8 | 10.0 | 802.0 | 5.0 |
| 9.78228848e8 | 10.0 | 2138.0 | 3.0 |
| 9.78229624e8 | 10.0 | 1409.0 | 5.0 |
| 9.791676e8 | 10.0 | 2067.0 | 3.0 |
| 9.7916766e8 | 10.0 | 1265.0 | 5.0 |
| 9.79168036e8 | 10.0 | 1339.0 | 5.0 |
| 9.782265e8 | 10.0 | 1269.0 | 5.0 |
| 9.79168108e8 | 10.0 | 1196.0 | 5.0 |
| 9.78227646e8 | 10.0 | 590.0 | 5.0 |
| 9.7916766e8 | 10.0 | 1197.0 | 5.0 |
| 9.7822563e8 | 10.0 | 1198.0 | 5.0 |
| 9.7916821e8 | 10.0 | 2640.0 | 5.0 |
| 9.78227263e8 | 10.0 | 592.0 | 4.0 |
| 9.78226913e8 | 10.0 | 594.0 | 5.0 |
| 9.78225952e8 | 10.0 | 2716.0 | 4.0 |
| 9.80638533e8 | 10.0 | 595.0 | 5.0 |
| 9.79775973e8 | 10.0 | 2717.0 | 4.0 |
| 9.79168108e8 | 10.0 | 2571.0 | 5.0 |
| 9.78228174e8 | 10.0 | 596.0 | 4.0 |
| 9.80638373e8 | 10.0 | 3447.0 | 5.0 |
| 9.78224375e8 | 10.0 | 597.0 | 4.0 |
| 9.7823023e8 | 10.0 | 1918.0 | 3.0 |
| 9.79168295e8 | 10.0 | 2140.0 | 5.0 |
| 9.78226287e8 | 10.0 | 1411.0 | 4.0 |
| 9.78230923e8 | 10.0 | 2072.0 | 4.0 |
| 9.78225735e8 | 10.0 | 1270.0 | 4.0 |
| 9.78226349e8 | 10.0 | 1271.0 | 5.0 |
| 9.78229295e8 | 10.0 | 1345.0 | 3.0 |
| 9.79775266e8 | 10.0 | 2077.0 | 3.0 |
| 9.78227646e8 | 10.0 | 2078.0 | 4.0 |
| 9.79168446e8 | 10.0 | 1276.0 | 3.0 |
| 9.78224375e8 | 10.0 | 743.0 | 3.0 |
| 9.78228408e8 | 10.0 | 671.0 | 3.0 |
| 9.78225922e8 | 10.0 | 1278.0 | 5.0 |
| 9.79168564e8 | 10.0 | 745.0 | 5.0 |
| 9.78226739e8 | 10.0 | 3451.0 | 5.0 |
| 9.782265e8 | 10.0 | 3525.0 | 2.0 |
| 9.78227503e8 | 10.0 | 1921.0 | 4.0 |
| 9.78228288e8 | 10.0 | 1923.0 | 5.0 |
| 9.78225599e8 | 10.0 | 1927.0 | 3.0 |
| 9.78227379e8 | 10.0 | 3386.0 | 4.0 |
| 9.78224779e8 | 10.0 | 2657.0 | 4.0 |
| 9.78230736e8 | 10.0 | 3388.0 | 3.0 |
| 9.7916847e8 | 10.0 | 1784.0 | 5.0 |
| 9.78227503e8 | 10.0 | 316.0 | 5.0 |
| 9.78228625e8 | 10.0 | 317.0 | 4.0 |
| 9.79775159e8 | 10.0 | 318.0 | 4.0 |
| 9.78229811e8 | 10.0 | 248.0 | 5.0 |
| 9.78226866e8 | 10.0 | 2080.0 | 4.0 |
| 9.78228053e8 | 10.0 | 2081.0 | 5.0 |
| 9.78228754e8 | 10.0 | 2082.0 | 3.0 |
| 9.78224549e8 | 10.0 | 1282.0 | 5.0 |
| 9.78227871e8 | 10.0 | 1356.0 | 5.0 |
| 9.80638614e8 | 10.0 | 1283.0 | 3.0 |
| 9.79775386e8 | 10.0 | 750.0 | 4.0 |
| 9.78227625e8 | 10.0 | 1357.0 | 5.0 |
| 9.79775738e8 | 10.0 | 2087.0 | 5.0 |
| 9.79167906e8 | 10.0 | 1286.0 | 5.0 |
| 9.78227425e8 | 10.0 | 1287.0 | 3.0 |
| 9.78227003e8 | 10.0 | 2804.0 | 5.0 |
| 9.78228601e8 | 10.0 | 3608.0 | 3.0 |
| 9.78227529e8 | 10.0 | 2662.0 | 4.0 |
| 9.79168267e8 | 10.0 | 3466.0 | 5.0 |
| 9.78227105e8 | 10.0 | 3100.0 | 3.0 |
| 9.78225682e8 | 10.0 | 2300.0 | 5.0 |
| 9.78228886e8 | 10.0 | 253.0 | 5.0 |
| 9.78226766e8 | 10.0 | 180.0 | 2.0 |
| 9.79168618e8 | 10.0 | 2599.0 | 5.0 |
| 9.78227317e8 | 10.0 | 2302.0 | 5.0 |
| 9.78229147e8 | 10.0 | 329.0 | 5.0 |
| 9.8063833e8 | 10.0 | 3034.0 | 4.0 |
| 9.79168036e8 | 10.0 | 3108.0 | 5.0 |
| 9.78225997e8 | 10.0 | 3035.0 | 3.0 |
| 9.78229938e8 | 10.0 | 186.0 | 3.0 |
| 9.78228212e8 | 10.0 | 2161.0 | 5.0 |
| 9.78225682e8 | 10.0 | 3037.0 | 5.0 |
| 9.7822882e8 | 10.0 | 2090.0 | 4.0 |
| 9.78228053e8 | 10.0 | 3039.0 | 4.0 |
| 9.78230593e8 | 10.0 | 2091.0 | 3.0 |
| 9.79167926e8 | 10.0 | 902.0 | 5.0 |
| 9.78229716e8 | 10.0 | 830.0 | 5.0 |
| 9.78229624e8 | 10.0 | 2093.0 | 4.0 |
| 9.78225735e8 | 10.0 | 1291.0 | 5.0 |
| 9.79775329e8 | 10.0 | 904.0 | 4.0 |
| 9.78229097e8 | 10.0 | 2094.0 | 4.0 |
| 9.79775615e8 | 10.0 | 1292.0 | 5.0 |
| 9.78225682e8 | 10.0 | 1293.0 | 4.0 |
| 9.79775364e8 | 10.0 | 2096.0 | 5.0 |
| 9.78225952e8 | 10.0 | 1294.0 | 4.0 |
| 9.7823033e8 | 10.0 | 765.0 | 4.0 |
| 9.78226231e8 | 10.0 | 3471.0 | 5.0 |
| 9.78228307e8 | 10.0 | 2746.0 | 5.0 |
| 9.79168267e8 | 10.0 | 1009.0 | 5.0 |
| 9.78226805e8 | 10.0 | 1947.0 | 5.0 |
| 9.782244e8 | 10.0 | 1948.0 | 4.0 |
| 9.78228911e8 | 10.0 | 333.0 | 3.0 |
| 9.79168077e8 | 10.0 | 260.0 | 5.0 |
| 9.78225759e8 | 10.0 | 3114.0 | 4.0 |
| 9.78224549e8 | 10.0 | 2312.0 | 5.0 |
| 9.79167945e8 | 10.0 | 339.0 | 5.0 |
| 9.7823023e8 | 10.0 | 1513.0 | 3.0 |
| 9.78229666e8 | 10.0 | 2316.0 | 4.0 |
| 9.78228451e8 | 10.0 | 1441.0 | 5.0 |
| 9.78227961e8 | 10.0 | 1517.0 | 3.0 |
| 9.78229517e8 | 10.0 | 1371.0 | 4.0 |
| 9.78227669e8 | 10.0 | 2174.0 | 5.0 |
| 9.78228288e8 | 10.0 | 1372.0 | 4.0 |
| 9.78225708e8 | 10.0 | 912.0 | 3.0 |
| 9.782259e8 | 10.0 | 913.0 | 3.0 |
| 9.7916816e8 | 10.0 | 1374.0 | 4.0 |
| 9.78226805e8 | 10.0 | 914.0 | 5.0 |
| 9.78228212e8 | 10.0 | 1375.0 | 4.0 |
| 9.78227215e8 | 10.0 | 915.0 | 5.0 |
| 9.79168132e8 | 10.0 | 1376.0 | 4.0 |
| 9.78229894e8 | 10.0 | 1377.0 | 3.0 |
| 9.79168424e8 | 10.0 | 916.0 | 5.0 |
| 9.78227215e8 | 10.0 | 918.0 | 5.0 |
| 9.78225561e8 | 10.0 | 919.0 | 5.0 |
| 9.78227462e8 | 10.0 | 1012.0 | 3.0 |
| 9.7822505e8 | 10.0 | 3481.0 | 4.0 |
| 9.7823054e8 | 10.0 | 2826.0 | 4.0 |
| 9.78225428e8 | 10.0 | 3629.0 | 3.0 |
| 9.78230923e8 | 10.0 | 1015.0 | 3.0 |
| 9.78229171e8 | 10.0 | 1016.0 | 5.0 |
| 9.78225735e8 | 10.0 | 1954.0 | 3.0 |
| 9.78227763e8 | 10.0 | 1019.0 | 4.0 |
| 9.78229772e8 | 10.0 | 1884.0 | 2.0 |
| 9.79168295e8 | 10.0 | 3489.0 | 4.0 |
| 9.7823033e8 | 10.0 | 1959.0 | 5.0 |
| 9.78229343e8 | 10.0 | 344.0 | 4.0 |
| 9.78230586e8 | 10.0 | 24.0 | 3.0 |
| 9.78227336e8 | 10.0 | 2321.0 | 4.0 |
| 9.79776321e8 | 10.0 | 275.0 | 4.0 |
| 9.78226287e8 | 10.0 | 2324.0 | 5.0 |
| 9.80638688e8 | 10.0 | 2252.0 | 5.0 |
| 9.78226147e8 | 10.0 | 277.0 | 3.0 |
| 9.78229336e8 | 10.0 | 1380.0 | 5.0 |
| 9.78227029e8 | 10.0 | 920.0 | 5.0 |
| 9.80638575e8 | 10.0 | 923.0 | 3.0 |
| 9.79168183e8 | 10.0 | 924.0 | 3.0 |
| 9.78227871e8 | 10.0 | 3701.0 | 3.0 |
| 9.78226103e8 | 10.0 | 3702.0 | 3.0 |
| 9.78228625e8 | 10.0 | 780.0 | 5.0 |
| 9.78226805e8 | 10.0 | 3703.0 | 2.0 |
| 9.78225952e8 | 10.0 | 1387.0 | 3.0 |
| 9.78226212e8 | 10.0 | 926.0 | 4.0 |
| 9.78228364e8 | 10.0 | 3704.0 | 2.0 |
| 9.78228726e8 | 10.0 | 1020.0 | 3.0 |
| 9.78230946e8 | 10.0 | 784.0 | 3.0 |
| 9.78224375e8 | 10.0 | 858.0 | 3.0 |
| 9.79775689e8 | 10.0 | 1022.0 | 5.0 |
Here we simply check the size of the datasets we are using
val numRatings = ratingsRDD.count
val numUsers = ratingsRDD.map(_.user).distinct.count
val numMovies = ratingsRDD.map(_.product).distinct.count
println("Got " + numRatings + " ratings from "
+ numUsers + " users on " + numMovies + " movies.")
Got 487650 ratings from 2999 users on 3615 movies.
numRatings: Long = 487650
numUsers: Long = 2999
numMovies: Long = 3615
Now that we have the dataset we need, let's make a recommender system.
Creating a Training Set, test Set and Validation Set
Before we jump into using machine learning, we need to break up the ratingsRDD dataset into three pieces:
- A training set (RDD), which we will use to train models
- A validation set (RDD), which we will use to choose the best model
- A test set (RDD), which we will use for our experiments
To randomly split the dataset into the multiple groups, we can use the randomSplit() transformation. randomSplit() takes a set of splits and seed and returns multiple RDDs.
val Array(trainingRDD, validationRDD, testRDD) = ratingsRDD.randomSplit(Array(0.60, 0.20, 0.20), 0L)
trainingRDD: org.apache.spark.rdd.RDD[org.apache.spark.mllib.recommendation.Rating] = MapPartitionsRDD[47347] at randomSplit at command-2972105651607159:1
validationRDD: org.apache.spark.rdd.RDD[org.apache.spark.mllib.recommendation.Rating] = MapPartitionsRDD[47348] at randomSplit at command-2972105651607159:1
testRDD: org.apache.spark.rdd.RDD[org.apache.spark.mllib.recommendation.Rating] = MapPartitionsRDD[47349] at randomSplit at command-2972105651607159:1
After splitting the dataset, your training set has about 293,000 entries and the validation and test sets each have about 97,000 entries (the exact number of entries in each dataset varies slightly due to the random nature of the randomSplit() transformation.
// let's find the exact sizes we have next
println(" training data size = " + trainingRDD.count() +
", validation data size = " + validationRDD.count() +
", test data size = " + testRDD.count() + ".")
training data size = 292318, validation data size = 97175, test data size = 98157.
See http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.recommendation.ALS.
(2c) Using ALS.train()
In this part, we will use the MLlib implementation of Alternating Least Squares, ALS.train(). ALS takes a training dataset (RDD) and several parameters that control the model creation process. To determine the best values for the parameters, we will use ALS to train several models, and then we will select the best model and use the parameters from that model in the rest of this lab exercise.
The process we will use for determining the best model is as follows:
- Pick a set of model parameters. The most important parameter to
ALS.train()is the rank, which is the number of rows in the Users matrix (green in the diagram above) or the number of columns in the Movies matrix (blue in the diagram above). (In general, a lower rank will mean higher error on the training dataset, but a high rank may lead to overfitting.) We will train models with ranks of 4, 8, and 12 using thetrainingRDDdataset. - Create a model using
ALS.train(trainingRDD, rank, seed=seed, iterations=iterations, lambda_=regularizationParameter)with three parameters: an RDD consisting of tuples of the form (UserID, MovieID, rating) used to train the model, an integer rank (4, 8, or 12), a number of iterations to execute (we will use 5 for theiterationsparameter), and a regularization coefficient (we will use 0.1 for theregularizationParameter). - For the prediction step, create an input RDD,
validationForPredictRDD, consisting of (UserID, MovieID) pairs that you extract fromvalidationRDD. You will end up with an RDD of the form:[(1, 1287), (1, 594), (1, 1270)] - Using the model and
validationForPredictRDD, we can predict rating values by calling model.predictAll() with thevalidationForPredictRDDdataset, wheremodelis the model we generated with ALS.train().predictAllaccepts an RDD with each entry in the format (userID, movieID) and outputs an RDD with each entry in the format (userID, movieID, rating).
// Build the recommendation model using ALS by fitting to the training data
// using a fixed rank=10, numIterations=10 and regularisation=0.01
val rank = 10
val numIterations = 10
val model = ALS.train(trainingRDD, rank, numIterations, 0.01)
rank: Int = 10
numIterations: Int = 10
model: org.apache.spark.mllib.recommendation.MatrixFactorizationModel = org.apache.spark.mllib.recommendation.MatrixFactorizationModel@6abb826a
// Evaluate the model on test data
val usersProductsTest = testRDD.map { case Rating(user, product, rate) =>
(user, product)
}
usersProductsTest: org.apache.spark.rdd.RDD[(Int, Int)] = MapPartitionsRDD[47557] at map at command-2972105651607165:2
usersProductsTest.take(10) // Checking
res9: Array[(Int, Int)] = Array((1,2321), (1,720), (1,1545), (1,745), (1,2294), (1,1836), (1,1022), (1,150), (1,531), (2,1357))
// get the predictions on test data
val predictions =
model.predict(usersProductsTest).map { case Rating(user, product, rate) =>
((user, product), rate)
}
predictions: org.apache.spark.rdd.RDD[((Int, Int), Double)] = MapPartitionsRDD[47566] at map at command-2972105651607167:3
// find the actual ratings and join with predictions
val ratesAndPreds = testRDD.map { case Rating(user, product, rate) =>
((user, product), rate)
}.join(predictions)
ratesAndPreds: org.apache.spark.rdd.RDD[((Int, Int), (Double, Double))] = MapPartitionsRDD[47570] at join at command-2972105651607168:4
ratesAndPreds.take(10).map(println) // print first 10 pairs of (user,product) and (true_rating, predicted_rating)
((1147,1199),(2.0,3.8559293170362228))
((2181,1097),(2.0,4.164067445701537))
((2670,2344),(4.0,3.076550452498327))
((2156,2826),(4.0,2.3447670482871192))
((855,1455),(3.0,2.815816909694013))
((1172,1917),(3.0,3.6666224824309066))
((1635,2012),(3.0,3.3799041063244064))
((1969,592),(3.0,4.080292663763599))
((1897,1086),(4.0,4.262370135546664))
((2906,1073),(3.0,3.488097650186112))
res10: Array[Unit] = Array((), (), (), (), (), (), (), (), (), ())
Let's evaluate the model using Mean Squared Error metric.
val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
val err = (r1 - r2)
err * err
}.mean()
println("Mean Squared Error = " + MSE)
Mean Squared Error = 0.9692691974119817
MSE: Double = 0.9692691974119817
Can we improve the MSE by changing one of the hyper parameters?
// Build the recommendation model using ALS by fitting to the validation data
// just trying three different hyper-parameter (rank) values to optimise over
val ranks = List(4, 8, 12);
var rank=0;
for ( rank <- ranks ){
val numIterations = 10
val regularizationParameter = 0.01
val model = ALS.train(trainingRDD, rank, numIterations, regularizationParameter)
// Evaluate the model on test data
val usersProductsValidate = validationRDD.map { case Rating(user, product, rate) =>
(user, product)
}
// get the predictions on test data
val predictions = model.predict(usersProductsValidate)
.map { case Rating(user, product, rate)
=> ((user, product), rate)
}
// find the actual ratings and join with predictions
val ratesAndPreds = validationRDD.map { case Rating(user, product, rate)
=> ((user, product), rate)
}.join(predictions)
val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
val err = (r1 - r2)
err * err
}.mean()
println("rank and Mean Squared Error = " + rank + " and " + MSE)
} // end of loop over ranks
rank and Mean Squared Error = 4 and 0.8483329344059245
rank and Mean Squared Error = 8 and 0.9449377020839357
rank and Mean Squared Error = 12 and 1.0217748398862294
ranks: List[Int] = List(4, 8, 12)
rank: Int = 0
Now let us try to apply this to the test data and find the MSE for the best model.
val rank = 4
val numIterations = 10
val regularizationParameter = 0.01
val model = ALS.train(trainingRDD, rank, numIterations, regularizationParameter)
// Evaluate the model on test data
val usersProductsTest = testRDD.map { case Rating(user, product, rate) =>
(user, product)
}
// get the predictions on test data
val predictions = model.predict(usersProductsTest)
.map { case Rating(user, product, rate)
=> ((user, product), rate)
}
// find the actual ratings and join with predictions
val ratesAndPreds = testRDD.map { case Rating(user, product, rate)
=> ((user, product), rate)
}.join(predictions)
val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
val err = (r1 - r2)
err * err
}.mean()
println("rank and Mean Squared Error for test data = " + rank + " and " + MSE)
rank and Mean Squared Error for test data = 4 and 0.8553006717351811
rank: Int = 4
numIterations: Int = 10
regularizationParameter: Double = 0.01
model: org.apache.spark.mllib.recommendation.MatrixFactorizationModel = org.apache.spark.mllib.recommendation.MatrixFactorizationModel@2efe95e4
usersProductsTest: org.apache.spark.rdd.RDD[(Int, Int)] = MapPartitionsRDD[48449] at map at command-2972105651607175:7
predictions: org.apache.spark.rdd.RDD[((Int, Int), Double)] = MapPartitionsRDD[48458] at map at command-2972105651607175:13
ratesAndPreds: org.apache.spark.rdd.RDD[((Int, Int), (Double, Double))] = MapPartitionsRDD[48462] at join at command-2972105651607175:20
MSE: Double = 0.8553006717351811
** Potential flaws of CF **
- Cold start for users and items
- Gray sheep: https://en.wikipedia.org/wiki/Collaborativefiltering#Graysheep
- Shilling attacks: https://en.wikipedia.org/wiki/Collaborativefiltering#Shillingattacks
- Positive feedback problems (rich-get-richer effect): https://en.wikipedia.org/wiki/Collaborativefiltering#Diversityandthelong_tail
** Areas to improve upon **
- Works in theory but didn't manage to produce a system that takes user info and outputs suggestions
- More complete models would include analysing the genres to give better recommendations.
- For first time users, the program could give the top rated movies over all users.
- Could have used bigger dataset
Project Ideas
- Do an analysis od ALS algorithm under the hood.
- Try to improve the model
Extending spark.graphx.lib.ShortestPaths to GraphXShortestWeightedPaths
2016-2020, Ivan Sadikov and Raazesh Sainudiin
We extend Shortest Paths algorithm in Spark's GraphX Library to allow for user-specified edge-weights as an edge attribute.
This is part of Project MEP: Meme Evolution Programme and supported by databricks academic partners program.
The analysis is available in the following databricks notebook: * http://lamastex.org/lmse/mep/src/GraphXShortestWeightedPaths.html
Copyright 2016 Ivan Sadikov and Raazesh Sainudiin
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Let's modify shortest paths algorithm to allow for user-specified edge-weights
Update shortest paths algorithm to work over edge attribute of edge-weights as Double, key concepts are: - we increment map with delta, which is edge.attr - edge attribute is anything numeric, tested on Double - infinity value is not infinity, but Integer.MAX_VALUE
Modifying the following code: * https://github.com/apache/spark/blob/master/graphx/src/main/scala/org/apache/spark/graphx/lib/ShortestPaths.scala
Explained here: * http://note.yuhc.me/2015/03/graphx-pregel-shortest-path/
import scala.reflect.ClassTag
import org.apache.spark.graphx._
/**
* Computes shortest weighted paths to the given set of landmark vertices, returning a graph where each
* vertex attribute is a map containing the shortest-path distance to each reachable landmark.
* Currently supports only Graph of [VD, Double], where VD is an arbitrary vertex type.
*/
object GraphXShortestWeightedPaths extends Serializable {
/** Stores a map from the vertex id of a landmark to the distance to that landmark. */
type SPMap = Map[VertexId, Double]
// initial and infinity values, use to relax edges
private val INITIAL = 0.0
private val INFINITY = Int.MaxValue.toDouble
private def makeMap(x: (VertexId, Double)*) = Map(x: _*)
private def incrementMap(spmap: SPMap, delta: Double): SPMap = {
spmap.map { case (v, d) => v -> (d + delta) }
}
private def addMaps(spmap1: SPMap, spmap2: SPMap): SPMap = {
(spmap1.keySet ++ spmap2.keySet).map {
k => k -> math.min(spmap1.getOrElse(k, INFINITY), spmap2.getOrElse(k, INFINITY))
}.toMap
}
// at this point it does not really matter what vertex type is
def run[VD](graph: Graph[VD, Double], landmarks: Seq[VertexId]): Graph[SPMap, Double] = {
val spGraph = graph.mapVertices { (vid, attr) =>
// initial value for itself is 0.0 as Double
if (landmarks.contains(vid)) makeMap(vid -> INITIAL) else makeMap()
}
val initialMessage = makeMap()
def vertexProgram(id: VertexId, attr: SPMap, msg: SPMap): SPMap = {
addMaps(attr, msg)
}
def sendMessage(edge: EdgeTriplet[SPMap, Double]): Iterator[(VertexId, SPMap)] = {
val newAttr = incrementMap(edge.dstAttr, edge.attr)
if (edge.srcAttr != addMaps(newAttr, edge.srcAttr)) Iterator((edge.srcId, newAttr))
else Iterator.empty
}
Pregel(spGraph, initialMessage)(vertexProgram, sendMessage, addMaps)
}
}
println("Usage: val result = GraphXShortestWeightedPaths.run(graph, Seq(4L, 0L, 9L))")
Usage: val result = GraphXShortestWeightedPaths.run(graph, Seq(4L, 0L, 9L))
import scala.reflect.ClassTag
import org.apache.spark.graphx._
defined object GraphXShortestWeightedPaths
Generate test graph
Generate simple graph with double weights for edges
import scala.util.Random
import org.apache.spark.graphx.{Graph, VertexId}
import org.apache.spark.graphx.util.GraphGenerators
// A graph with edge attributes containing distances
val graph: Graph[Long, Double] = GraphGenerators.logNormalGraph(sc, numVertices = 10, seed=123L).mapEdges { e =>
// to make things nicer we assign 0 distance to itself
if (e.srcId == e.dstId) 0.0 else Random.nextDouble()
}
import scala.util.Random
import org.apache.spark.graphx.{Graph, VertexId}
import org.apache.spark.graphx.util.GraphGenerators
graph: org.apache.spark.graphx.Graph[Long,Double] = org.apache.spark.graphx.impl.GraphImpl@45dcade2
val landMarkVertexIds = Seq(4L, 0L, 9L)
val result = GraphXShortestWeightedPaths.run(graph, landMarkVertexIds)
landMarkVertexIds: Seq[Long] = List(4, 0, 9)
result: org.apache.spark.graphx.Graph[GraphXShortestWeightedPaths.SPMap,Double] = org.apache.spark.graphx.impl.GraphImpl@4bc2f186
// Found shortest paths
println(result.vertices.collect.mkString("\n"))
(0,Map(0 -> 0.0, 4 -> 0.9508628404888835, 9 -> 0.9591413283099168))
(8,Map(0 -> 0.2934032163544852, 4 -> 0.9801207594550188, 9 -> 0.8196019765599419))
(1,Map(0 -> 0.5802357924540097, 4 -> 1.2669533355545433, 9 -> 1.2752318233755766))
(9,Map(9 -> 0.0, 0 -> 0.5231864117917981, 4 -> 0.7980365238526225))
(2,Map(0 -> 0.2582682915929926, 4 -> 0.8339520209319522, 9 -> 0.5701009018719322))
(3,Map(0 -> 0.0923559237731384, 4 -> 0.18942157781466173, 9 -> 0.1977000656356951))
(4,Map(4 -> 0.0, 0 -> 0.25873464218225106, 9 -> 0.008278487821033353))
(5,Map(0 -> 0.031742669363998166, 4 -> 0.1288083234055215, 9 -> 0.13708681122655486))
(6,Map(0 -> 0.12059036542225454, 4 -> 0.8073079085227881, 9 -> 0.8155863963438215))
(7,Map(4 -> 0.22844606564222758, 9 -> 0.23672455346326093, 0 -> 0.33818821718284775))
// edges with weights, make sure to check couple of shortest paths from above
display(result.edges.toDF)
| srcId | dstId | attr |
|---|---|---|
| 0.0 | 0.0 | 0.0 |
| 0.0 | 1.0 | 0.6721979874068958 |
| 0.0 | 4.0 | 0.9508628404888835 |
| 1.0 | 1.0 | 0.0 |
| 1.0 | 6.0 | 0.45964542703175515 |
| 1.0 | 6.0 | 0.7349975451261668 |
| 2.0 | 0.0 | 0.2582682915929926 |
| 2.0 | 1.0 | 0.9280493920155959 |
| 2.0 | 4.0 | 0.8339520209319522 |
| 2.0 | 6.0 | 0.26091386534774674 |
| 2.0 | 6.0 | 0.9481996224496678 |
| 2.0 | 9.0 | 0.5701009018719322 |
| 3.0 | 2.0 | 0.8893068591814861 |
| 3.0 | 3.0 | 0.0 |
| 3.0 | 5.0 | 6.061325440914023e-2 |
| 3.0 | 7.0 | 0.26415564719487117 |
| 3.0 | 8.0 | 0.5847037097670544 |
| 4.0 | 0.0 | 0.373044758393941 |
| 4.0 | 3.0 | 0.16637871840911267 |
| 4.0 | 5.0 | 0.8133138055926398 |
| 4.0 | 7.0 | 0.5329270151953733 |
| 4.0 | 7.0 | 0.7076376180464232 |
| 4.0 | 9.0 | 0.3590440724653333 |
| 4.0 | 9.0 | 8.278487821033353e-3 |
| 5.0 | 0.0 | 3.1742669363998166e-2 |
| 5.0 | 1.0 | 0.4799303618281624 |
| 5.0 | 2.0 | 0.9373917774347413 |
| 5.0 | 3.0 | 5.426676615013515e-2 |
| 5.0 | 4.0 | 0.1288083234055215 |
| 5.0 | 5.0 | 0.0 |
| 6.0 | 0.0 | 0.12059036542225454 |
| 6.0 | 1.0 | 1.867282877196641e-2 |
| 6.0 | 2.0 | 0.9882878315838144 |
| 6.0 | 3.0 | 0.944141072288605 |
| 6.0 | 5.0 | 0.6784995851172666 |
| 6.0 | 6.0 | 0.0 |
| 6.0 | 6.0 | 0.0 |
| 6.0 | 6.0 | 0.0 |
| 6.0 | 8.0 | 0.16318250935765966 |
| 7.0 | 2.0 | 7.991992558985517e-2 |
| 7.0 | 2.0 | 0.7824158528877193 |
| 7.0 | 2.0 | 0.17715746355547157 |
| 7.0 | 4.0 | 0.7655621066580156 |
| 7.0 | 4.0 | 0.2580382514924928 |
| 7.0 | 4.0 | 0.22844606564222758 |
| 7.0 | 8.0 | 0.750024180696321 |
| 7.0 | 9.0 | 0.9228699021998864 |
| 8.0 | 1.0 | 0.351758945103697 |
| 8.0 | 2.0 | 0.24950107468800975 |
| 8.0 | 3.0 | 0.9928995269181429 |
| 8.0 | 6.0 | 0.17281285093223064 |
| 9.0 | 0.0 | 0.5838217791812277 |
| 9.0 | 2.0 | 0.26491812019880556 |
| 9.0 | 4.0 | 0.853261480233851 |
| 9.0 | 7.0 | 0.5695904582103949 |
display(graph.edges.toDF) // this is the directed weighted edge of the graph
| srcId | dstId | attr |
|---|---|---|
| 0.0 | 0.0 | 0.0 |
| 0.0 | 1.0 | 0.2473870305881326 |
| 0.0 | 4.0 | 0.7593678671727498 |
| 1.0 | 1.0 | 0.0 |
| 1.0 | 6.0 | 0.4721793531500751 |
| 1.0 | 6.0 | 0.7565157046946671 |
| 2.0 | 0.0 | 0.8601376666144385 |
| 2.0 | 1.0 | 0.15474243507003416 |
| 2.0 | 4.0 | 0.17585136043466876 |
| 2.0 | 6.0 | 0.9762506825428003 |
| 2.0 | 6.0 | 0.8022953138023812 |
| 2.0 | 9.0 | 0.8898859140300339 |
| 3.0 | 2.0 | 0.23688722482284008 |
| 3.0 | 3.0 | 0.0 |
| 3.0 | 5.0 | 0.6621552329080445 |
| 3.0 | 7.0 | 0.6189436789554128 |
| 3.0 | 8.0 | 0.7648758038805598 |
| 4.0 | 0.0 | 0.9568298106188533 |
| 4.0 | 3.0 | 0.41779642900620517 |
| 4.0 | 5.0 | 0.2897812470698585 |
| 4.0 | 7.0 | 0.9845657600597383 |
| 4.0 | 7.0 | 0.6752427687212571 |
| 4.0 | 9.0 | 0.23033960072928972 |
| 4.0 | 9.0 | 0.6688651818696816 |
| 5.0 | 0.0 | 0.4544165543058408 |
| 5.0 | 1.0 | 3.778698342738085e-2 |
| 5.0 | 2.0 | 0.8383018223597426 |
| 5.0 | 3.0 | 0.7890454146717444 |
| 5.0 | 4.0 | 0.3471895990573223 |
| 5.0 | 5.0 | 0.0 |
| 6.0 | 0.0 | 0.2431403242371375 |
| 6.0 | 1.0 | 0.6109097064072662 |
| 6.0 | 2.0 | 0.7701858934760617 |
| 6.0 | 3.0 | 0.12066591357902912 |
| 6.0 | 5.0 | 0.5034427240618765 |
| 6.0 | 6.0 | 0.0 |
| 6.0 | 6.0 | 0.0 |
| 6.0 | 6.0 | 0.0 |
| 6.0 | 8.0 | 0.616288715486008 |
| 7.0 | 2.0 | 7.870712177892414e-2 |
| 7.0 | 2.0 | 0.5147452602931151 |
| 7.0 | 2.0 | 0.8214716030697653 |
| 7.0 | 4.0 | 0.25578984521277015 |
| 7.0 | 4.0 | 0.3583207455757458 |
| 7.0 | 4.0 | 8.320072407893553e-2 |
| 7.0 | 8.0 | 0.36976177580734204 |
| 7.0 | 9.0 | 0.5704097005469446 |
| 8.0 | 1.0 | 0.20123008667210507 |
| 8.0 | 2.0 | 0.7558945801315478 |
| 8.0 | 3.0 | 0.5592884924812593 |
| 8.0 | 6.0 | 0.5437831714308268 |
| 9.0 | 0.0 | 0.7347489314741307 |
| 9.0 | 2.0 | 0.2611206797801059 |
| 9.0 | 4.0 | 0.6543183939764342 |
| 9.0 | 7.0 | 0.6768117581780281 |
// now let us collect the shortest distance between every vertex and every landmark vertex
// to manipulate scala maps that are vertices of the result see: http://docs.scala-lang.org/overviews/collections/maps.html
// a quick point: http://stackoverflow.com/questions/28769367/scala-map-a-map-to-list-of-tuples
val shortestDistsVertex2Landmark = result.vertices.flatMap(GxSwpSPMap => {
GxSwpSPMap._2.toSeq.map(x => (GxSwpSPMap._1, x._1, x._2)) // to get triples: vertex, landmarkVertex, shortest_distance
})
shortestDistsVertex2Landmark: org.apache.spark.rdd.RDD[(org.apache.spark.graphx.VertexId, org.apache.spark.graphx.VertexId, Double)] = MapPartitionsRDD[3128] at flatMap at command-2744653148555415:4
shortestDistsVertex2Landmark.collect.mkString("\n")
res4: String =
(0,0,0.0)
(0,4,0.9508628404888835)
(0,9,0.9591413283099168)
(8,0,0.2934032163544852)
(8,4,0.9801207594550188)
(8,9,0.8196019765599419)
(1,0,0.5802357924540097)
(1,4,1.2669533355545433)
(1,9,1.2752318233755766)
(9,9,0.0)
(9,0,0.5231864117917981)
(9,4,0.7980365238526225)
(2,0,0.2582682915929926)
(2,4,0.8339520209319522)
(2,9,0.5701009018719322)
(3,0,0.0923559237731384)
(3,4,0.18942157781466173)
(3,9,0.1977000656356951)
(4,4,0.0)
(4,0,0.25873464218225106)
(4,9,0.008278487821033353)
(5,0,0.031742669363998166)
(5,4,0.1288083234055215)
(5,9,0.13708681122655486)
(6,0,0.12059036542225454)
(6,4,0.8073079085227881)
(6,9,0.8155863963438215)
(7,4,0.22844606564222758)
(7,9,0.23672455346326093)
(7,0,0.33818821718284775)
Let's make a DataFrame for visualizing pairwise matrix plots
We want to make 4 columns in this example as follows (note actual values change for each realisation of graph!):
landmark_Id1 ("0"), landmarkID2 ("4"), landmarkId3 ("9"), srcVertexId
------------------------------------------------------------------------
0.0, 0.7425.., 0.8718, 0
0.924..., 1.2464.., 1.0472, 1
...
// http://alvinalexander.com/scala/how-to-sort-map-in-scala-key-value-sortby-sortwith
// we need this to make sure that the maps are ordered by the keys for ensuring unique column values
import scala.collection.immutable.ListMap
import sqlContext.implicits._
import scala.collection.immutable.ListMap
import sqlContext.implicits._
// recall our landmark vertices in landMarkVertexIds. let's use their Strings for names
val unorderedNamedLandmarkVertices = landMarkVertexIds.map(id => (id, id.toString) )
val orderedNamedLandmarkVertices = ListMap(unorderedNamedLandmarkVertices.sortBy(_._1):_*)
val orderedLandmarkVertexNames = orderedNamedLandmarkVertices.toSeq.map(x => x._2)
orderedLandmarkVertexNames.mkString(", ")
unorderedNamedLandmarkVertices: Seq[(Long, String)] = List((4,4), (0,0), (9,9))
orderedNamedLandmarkVertices: scala.collection.immutable.ListMap[Long,String] = ListMap(0 -> 0, 4 -> 4, 9 -> 9)
orderedLandmarkVertexNames: Seq[String] = Vector(0, 4, 9)
res5: String = 0, 4, 9
// this is going to be our column names
val columnNames:Seq[String] = orderedLandmarkVertexNames :+ "srcVertexId"
columnNames: Seq[String] = Vector(0, 4, 9, srcVertexId)
// a case class to make a data-frame quickly from the result
case class SeqOfDoublesAndsrcVertexId(shortestDistances: Seq[Double], srcVertexId: VertexId)
defined class SeqOfDoublesAndsrcVertexId
val shortestDistsSeqFromVertex2Landmark2DF = result.vertices.map(GxSwpSPMap => {
//GxSwpSPMap._2.toSeq.map(x => (GxSwpSPMap._1, x._1, x._2)) // from before to get triples: vertex, landmarkVertex, shortest_distance
val v = GxSwpSPMap._1
val a = ListMap(GxSwpSPMap._2.toSeq.sortBy(_._1):_*).toSeq.map(x => x._2)
val d = (a,v)
d
}).map(x => SeqOfDoublesAndsrcVertexId(x._1, x._2)).toDF()
shortestDistsSeqFromVertex2Landmark2DF: org.apache.spark.sql.DataFrame = [shortestDistances: array<double>, srcVertexId: bigint]
display(shortestDistsSeqFromVertex2Landmark2DF) // but this dataframe needs the first column exploded into 3 columns
Now we want to make separate columns for each distance in the Sequence in column 'shortestDistances'.
Let us use the following ideas for this: * https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/3741049972324885/2662535171379268/4413065072037724/latest.html
// this is from https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/3741049972324885/2662535171379268/4413065072037724/latest.html
import org.apache.spark.sql.{Column, DataFrame}
import org.apache.spark.sql.functions.{lit, udf}
// UDF to extract i-th element from array column
//val elem = udf((x: Seq[Int], y: Int) => x(y))
val elem = udf((x: Seq[Double], y: Int) => x(y)) // modified for Sequence of Doubles
// Method to apply 'elem' UDF on each element, requires knowing length of sequence in advance
def split(col: Column, len: Int): Seq[Column] = {
for (i <- 0 until len) yield { elem(col, lit(i)).as(s"$col($i)") }
}
// Implicit conversion to make things nicer to use, e.g.
// select(Column, Seq[Column], Column) is converted into select(Column*) flattening sequences
implicit class DataFrameSupport(df: DataFrame) {
def select(cols: Any*): DataFrame = {
var buffer: Seq[Column] = Seq.empty
for (col <- cols) {
if (col.isInstanceOf[Seq[_]]) {
buffer = buffer ++ col.asInstanceOf[Seq[Column]]
} else {
buffer = buffer :+ col.asInstanceOf[Column]
}
}
df.select(buffer:_*)
}
}
import org.apache.spark.sql.{Column, DataFrame}
import org.apache.spark.sql.functions.{lit, udf}
elem: org.apache.spark.sql.expressions.UserDefinedFunction = SparkUserDefinedFunction($Lambda$7813/1969990681@1a441b0e,DoubleType,List(Some(class[value[0]: array<double>]), Some(class[value[0]: int])),None,false,true)
split: (col: org.apache.spark.sql.Column, len: Int)Seq[org.apache.spark.sql.Column]
defined class DataFrameSupport
val shortestDistsFromVertex2Landmark2DF = shortestDistsSeqFromVertex2Landmark2DF.select(split($"shortestDistances", 3), $"srcVertexId")
shortestDistsFromVertex2Landmark2DF: org.apache.spark.sql.DataFrame = [shortestDistances(0): double, shortestDistances(1): double ... 2 more fields]
display(shortestDistsFromVertex2Landmark2DF)
| shortestDistances(0) | shortestDistances(1) | shortestDistances(2) | srcVertexId |
|---|---|---|---|
| 0.0 | 0.9508628404888835 | 0.9591413283099168 | 0.0 |
| 0.2934032163544852 | 0.9801207594550188 | 0.8196019765599419 | 8.0 |
| 0.5802357924540097 | 1.2669533355545433 | 1.2752318233755766 | 1.0 |
| 0.5231864117917981 | 0.7980365238526225 | 0.0 | 9.0 |
| 0.2582682915929926 | 0.8339520209319522 | 0.5701009018719322 | 2.0 |
| 9.23559237731384e-2 | 0.18942157781466173 | 0.1977000656356951 | 3.0 |
| 0.25873464218225106 | 0.0 | 8.278487821033353e-3 | 4.0 |
| 3.1742669363998166e-2 | 0.1288083234055215 | 0.13708681122655486 | 5.0 |
| 0.12059036542225454 | 0.8073079085227881 | 0.8155863963438215 | 6.0 |
| 0.33818821718284775 | 0.22844606564222758 | 0.23672455346326093 | 7.0 |
// now let's give it our names based on the landmark vertex Ids
val shortestDistsFromVertex2Landmark2DF = shortestDistsSeqFromVertex2Landmark2DF.select(split($"shortestDistances", 3), $"srcVertexId").toDF(columnNames:_*)
shortestDistsFromVertex2Landmark2DF: org.apache.spark.sql.DataFrame = [0: double, 4: double ... 2 more fields]
display(shortestDistsFromVertex2Landmark2DF)
| 0 | 4 | 9 | srcVertexId |
|---|---|---|---|
| 0.0 | 0.9508628404888835 | 0.9591413283099168 | 0.0 |
| 0.2934032163544852 | 0.9801207594550188 | 0.8196019765599419 | 8.0 |
| 0.5802357924540097 | 1.2669533355545433 | 1.2752318233755766 | 1.0 |
| 0.5231864117917981 | 0.7980365238526225 | 0.0 | 9.0 |
| 0.2582682915929926 | 0.8339520209319522 | 0.5701009018719322 | 2.0 |
| 9.23559237731384e-2 | 0.18942157781466173 | 0.1977000656356951 | 3.0 |
| 0.25873464218225106 | 0.0 | 8.278487821033353e-3 | 4.0 |
| 3.1742669363998166e-2 | 0.1288083234055215 | 0.13708681122655486 | 5.0 |
| 0.12059036542225454 | 0.8073079085227881 | 0.8155863963438215 | 6.0 |
| 0.33818821718284775 | 0.22844606564222758 | 0.23672455346326093 | 7.0 |
display(shortestDistsFromVertex2Landmark2DF.select($"0",$"4",$"9"))
| 0 | 4 | 9 |
|---|---|---|
| 0.0 | 0.9508628404888835 | 0.9591413283099168 |
| 0.2934032163544852 | 0.9801207594550188 | 0.8196019765599419 |
| 0.5802357924540097 | 1.2669533355545433 | 1.2752318233755766 |
| 0.5231864117917981 | 0.7980365238526225 | 0.0 |
| 0.2582682915929926 | 0.8339520209319522 | 0.5701009018719322 |
| 9.23559237731384e-2 | 0.18942157781466173 | 0.1977000656356951 |
| 0.25873464218225106 | 0.0 | 8.278487821033353e-3 |
| 3.1742669363998166e-2 | 0.1288083234055215 | 0.13708681122655486 |
| 0.12059036542225454 | 0.8073079085227881 | 0.8155863963438215 |
| 0.33818821718284775 | 0.22844606564222758 | 0.23672455346326093 |
Financial Fraud Detection using Decision Tree Machine Learning Models
This notebooks is the accompaniment to the following databricks blog: - https://databricks.com/blog/2019/05/02/detecting-financial-fraud-at-scale-with-decision-trees-and-mlflow-on-databricks.html
This is an exercise in self-learning. Here you will learn to use mlflow in pyspark to keep track of your experiments.
Also you may have to adapt it for Spark 3.x if needed.
In this notebook, we will showcase the use of decision tree ML models to perform financial fraud detection.
Source Data
PaySim simulates mobile money transactions based on a sample of real transactions extracted from one month of financial logs from a mobile money service implemented in an African country. The original logs were provided by a multinational company, who is the provider of the mobile financial service which is currently running in more than 14 countries all around the world.
This synthetic dataset is scaled down 1/4 of the original dataset and it is created just for Kaggle. To load the dataset yourself, please download it to your local machine from Kaggle and then import the data via Import Data: Azure | AWS.
Dictionary
This is the column definition of the referenced sythentic dataset.
| Column Name | Description |
|---|---|
| step | maps a unit of time in the real world. In this case 1 step is 1 hour of time. Total steps 744 (30 days simulation). |
| type | CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER. |
| amount | amount of the transaction in local currency. |
| nameOrig | customer who started the transaction |
| oldbalanceOrg | initial balance before the transaction |
| newbalanceOrig | new balance after the transaction |
| nameDest | customer who is the recipient of the transaction |
| oldbalanceDest | initial balance recipient before the transaction. Note that there is not information for customers that start with M (Merchants). |
| newbalanceDest | new balance recipient after the transaction. Note that there is not information for customers that start with M (Merchants). |
# Configure MLflow Experiment
mlflow_experiment_id = 866112
# Including MLflow
import mlflow
import mlflow.spark
import os
print("MLflow Version: %s" % mlflow.__version__)
MLflow Version: 0.8.2
# Create df DataFrame which contains our simulated financial fraud detection dataset
df = spark.sql("select step, type, amount, nameOrig, oldbalanceOrg, newbalanceOrig, nameDest, oldbalanceDest, newbalanceDest from sim_fin_fraud_detection")
# Review the schema of your data
df.printSchema()
root
|-- step: integer (nullable = true)
|-- type: string (nullable = true)
|-- amount: double (nullable = true)
|-- nameOrig: string (nullable = true)
|-- oldbalanceOrg: double (nullable = true)
|-- newbalanceOrig: double (nullable = true)
|-- nameDest: string (nullable = true)
|-- oldbalanceDest: double (nullable = true)
|-- newbalanceDest: double (nullable = true)
Calculate Differences between Originating and Destination Balanaces
With the following PySpark DataFrame query, we will calculate the following columns:
| New Column | Definition |
|---|---|
| orgDiff | Difference between the originating balance |
| destDiff | Difference between the destination balance |
# Calculate the differences between originating and destination balances
df = df.withColumn("orgDiff", df.newbalanceOrig - df.oldbalanceOrg).withColumn("destDiff", df.newbalanceDest - df.oldbalanceDest)
# Create temporary view
df.createOrReplaceTempView("financials")
# Review the new table (including the origination and destiation differences)
display(df)
| step | type | amount | nameOrig | oldbalanceOrg | newbalanceOrig | nameDest | oldbalanceDest | newbalanceDest | orgDiff | destDiff |
|---|---|---|---|---|---|---|---|---|---|---|
| 1.0 | PAYMENT | 9839.64 | C1231006815 | 170136.0 | 160296.36 | M1979787155 | 0.0 | 0.0 | -9839.640000000014 | 0.0 |
| 1.0 | PAYMENT | 1864.28 | C1666544295 | 21249.0 | 19384.72 | M2044282225 | 0.0 | 0.0 | -1864.2799999999988 | 0.0 |
| 1.0 | TRANSFER | 181.0 | C1305486145 | 181.0 | 0.0 | C553264065 | 0.0 | 0.0 | -181.0 | 0.0 |
| 1.0 | CASH_OUT | 181.0 | C840083671 | 181.0 | 0.0 | C38997010 | 21182.0 | 0.0 | -181.0 | -21182.0 |
| 1.0 | PAYMENT | 11668.14 | C2048537720 | 41554.0 | 29885.86 | M1230701703 | 0.0 | 0.0 | -11668.14 | 0.0 |
| 1.0 | PAYMENT | 7817.71 | C90045638 | 53860.0 | 46042.29 | M573487274 | 0.0 | 0.0 | -7817.709999999999 | 0.0 |
| 1.0 | PAYMENT | 7107.77 | C154988899 | 183195.0 | 176087.23 | M408069119 | 0.0 | 0.0 | -7107.7699999999895 | 0.0 |
| 1.0 | PAYMENT | 7861.64 | C1912850431 | 176087.23 | 168225.59 | M633326333 | 0.0 | 0.0 | -7861.640000000014 | 0.0 |
| 1.0 | PAYMENT | 4024.36 | C1265012928 | 2671.0 | 0.0 | M1176932104 | 0.0 | 0.0 | -2671.0 | 0.0 |
| 1.0 | DEBIT | 5337.77 | C712410124 | 41720.0 | 36382.23 | C195600860 | 41898.0 | 40348.79 | -5337.769999999997 | -1549.2099999999991 |
| 1.0 | DEBIT | 9644.94 | C1900366749 | 4465.0 | 0.0 | C997608398 | 10845.0 | 157982.12 | -4465.0 | 147137.12 |
| 1.0 | PAYMENT | 3099.97 | C249177573 | 20771.0 | 17671.03 | M2096539129 | 0.0 | 0.0 | -3099.970000000001 | 0.0 |
| 1.0 | PAYMENT | 2560.74 | C1648232591 | 5070.0 | 2509.26 | M972865270 | 0.0 | 0.0 | -2560.74 | 0.0 |
| 1.0 | PAYMENT | 11633.76 | C1716932897 | 10127.0 | 0.0 | M801569151 | 0.0 | 0.0 | -10127.0 | 0.0 |
| 1.0 | PAYMENT | 4098.78 | C1026483832 | 503264.0 | 499165.22 | M1635378213 | 0.0 | 0.0 | -4098.780000000028 | 0.0 |
| 1.0 | CASH_OUT | 229133.94 | C905080434 | 15325.0 | 0.0 | C476402209 | 5083.0 | 51513.44 | -15325.0 | 46430.44 |
| 1.0 | PAYMENT | 1563.82 | C761750706 | 450.0 | 0.0 | M1731217984 | 0.0 | 0.0 | -450.0 | 0.0 |
| 1.0 | PAYMENT | 1157.86 | C1237762639 | 21156.0 | 19998.14 | M1877062907 | 0.0 | 0.0 | -1157.8600000000006 | 0.0 |
| 1.0 | PAYMENT | 671.64 | C2033524545 | 15123.0 | 14451.36 | M473053293 | 0.0 | 0.0 | -671.6399999999994 | 0.0 |
| 1.0 | TRANSFER | 215310.3 | C1670993182 | 705.0 | 0.0 | C1100439041 | 22425.0 | 0.0 | -705.0 | -22425.0 |
| 1.0 | PAYMENT | 1373.43 | C20804602 | 13854.0 | 12480.57 | M1344519051 | 0.0 | 0.0 | -1373.4300000000003 | 0.0 |
| 1.0 | DEBIT | 9302.79 | C1566511282 | 11299.0 | 1996.21 | C1973538135 | 29832.0 | 16896.7 | -9302.79 | -12935.3 |
| 1.0 | DEBIT | 1065.41 | C1959239586 | 1817.0 | 751.59 | C515132998 | 10330.0 | 0.0 | -1065.4099999999999 | -10330.0 |
| 1.0 | PAYMENT | 3876.41 | C504336483 | 67852.0 | 63975.59 | M1404932042 | 0.0 | 0.0 | -3876.4100000000035 | 0.0 |
| 1.0 | TRANSFER | 311685.89 | C1984094095 | 10835.0 | 0.0 | C932583850 | 6267.0 | 2719172.89 | -10835.0 | 2712905.89 |
| 1.0 | PAYMENT | 6061.13 | C1043358826 | 443.0 | 0.0 | M1558079303 | 0.0 | 0.0 | -443.0 | 0.0 |
| 1.0 | PAYMENT | 9478.39 | C1671590089 | 116494.0 | 107015.61 | M58488213 | 0.0 | 0.0 | -9478.39 | 0.0 |
| 1.0 | PAYMENT | 8009.09 | C1053967012 | 10968.0 | 2958.91 | M295304806 | 0.0 | 0.0 | -8009.09 | 0.0 |
| 1.0 | PAYMENT | 8901.99 | C1632497828 | 2958.91 | 0.0 | M33419717 | 0.0 | 0.0 | -2958.91 | 0.0 |
| 1.0 | PAYMENT | 9920.52 | C764826684 | 0.0 | 0.0 | M1940055334 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 3448.92 | C2103763750 | 0.0 | 0.0 | M335107734 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 4206.84 | C215078753 | 0.0 | 0.0 | M1757317128 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 5885.56 | C840514538 | 0.0 | 0.0 | M1804441305 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 5307.88 | C1768242710 | 0.0 | 0.0 | M1971783162 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 5031.22 | C247113419 | 0.0 | 0.0 | M151442075 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 24213.67 | C1238616099 | 0.0 | 0.0 | M70695990 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 8603.42 | C1608633989 | 253.0 | 0.0 | M1615617512 | 0.0 | 0.0 | -253.0 | 0.0 |
| 1.0 | PAYMENT | 2791.42 | C923341586 | 300481.0 | 297689.58 | M107994825 | 0.0 | 0.0 | -2791.4199999999837 | 0.0 |
| 1.0 | PAYMENT | 7413.54 | C1470868839 | 297689.58 | 290276.03 | M1426725223 | 0.0 | 0.0 | -7413.549999999988 | 0.0 |
| 1.0 | PAYMENT | 3295.19 | C711197015 | 233633.0 | 230337.81 | M1384454980 | 0.0 | 0.0 | -3295.1900000000023 | 0.0 |
| 1.0 | PAYMENT | 1684.81 | C1481594086 | 297.0 | 0.0 | M1569435561 | 0.0 | 0.0 | -297.0 | 0.0 |
| 1.0 | DEBIT | 5758.59 | C1466917878 | 32604.0 | 26845.41 | C1297685781 | 209699.0 | 16997.22 | -5758.59 | -192701.78 |
| 1.0 | CASH_OUT | 110414.71 | C768216420 | 26845.41 | 0.0 | C1509514333 | 288800.0 | 2415.16 | -26845.41 | -286384.84 |
| 1.0 | PAYMENT | 7823.46 | C260084831 | 998.0 | 0.0 | M267814113 | 0.0 | 0.0 | -998.0 | 0.0 |
| 1.0 | PAYMENT | 5086.48 | C598357562 | 0.0 | 0.0 | M1593224710 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 5281.48 | C1440738283 | 152019.0 | 146737.52 | M1849015357 | 0.0 | 0.0 | -5281.4800000000105 | 0.0 |
| 1.0 | PAYMENT | 13875.98 | C484199463 | 15818.0 | 1942.02 | M2008106788 | 0.0 | 0.0 | -13875.98 | 0.0 |
| 1.0 | CASH_OUT | 56953.9 | C1570470538 | 1942.02 | 0.0 | C824009085 | 70253.0 | 64106.18 | -1942.02 | -6146.82 |
| 1.0 | CASH_OUT | 5346.89 | C512549200 | 0.0 | 0.0 | C248609774 | 652637.0 | 6453430.91 | 0.0 | 5800793.91 |
| 1.0 | PAYMENT | 2204.04 | C1615801298 | 586.0 | 0.0 | M490391704 | 0.0 | 0.0 | -586.0 | 0.0 |
| 1.0 | PAYMENT | 2641.47 | C460570271 | 23053.0 | 20411.53 | M1653361344 | 0.0 | 0.0 | -2641.470000000001 | 0.0 |
| 1.0 | CASH_OUT | 23261.3 | C2072313080 | 20411.53 | 0.0 | C2001112025 | 25742.0 | 0.0 | -20411.53 | -25742.0 |
| 1.0 | PAYMENT | 2330.64 | C816944408 | 203543.0 | 201212.36 | M909132503 | 0.0 | 0.0 | -2330.640000000014 | 0.0 |
| 1.0 | PAYMENT | 1614.64 | C912966811 | 41276.0 | 39661.36 | M1792384402 | 0.0 | 0.0 | -1614.6399999999994 | 0.0 |
| 1.0 | PAYMENT | 9164.71 | C1458621573 | 47235.77 | 38071.06 | M1658980982 | 0.0 | 0.0 | -9164.71 | 0.0 |
| 1.0 | PAYMENT | 2970.97 | C46941357 | 38071.06 | 35100.09 | M1152606315 | 0.0 | 0.0 | -2970.970000000001 | 0.0 |
| 1.0 | PAYMENT | 38.66 | C343345308 | 16174.0 | 16135.34 | M1714688478 | 0.0 | 0.0 | -38.659999999999854 | 0.0 |
| 1.0 | PAYMENT | 2252.44 | C104716441 | 1627.0 | 0.0 | M1506951181 | 0.0 | 0.0 | -1627.0 | 0.0 |
| 1.0 | TRANSFER | 62610.8 | C1976401987 | 79114.0 | 16503.2 | C1937962514 | 517.0 | 8383.29 | -62610.8 | 7866.290000000001 |
| 1.0 | DEBIT | 5529.13 | C867288517 | 8547.0 | 3017.87 | C242131142 | 10206.0 | 0.0 | -5529.13 | -10206.0 |
| 1.0 | CASH_OUT | 82940.31 | C1528834618 | 3017.87 | 0.0 | C476800120 | 132372.0 | 49864.36 | -3017.87 | -82507.64 |
| 1.0 | DEBIT | 4510.22 | C280615803 | 10256.0 | 5745.78 | C1254526270 | 10697.0 | 0.0 | -4510.22 | -10697.0 |
| 1.0 | DEBIT | 8727.74 | C166694583 | 882770.0 | 874042.26 | C1129670968 | 12636.0 | 0.0 | -8727.73999999999 | -12636.0 |
| 1.0 | PAYMENT | 2599.46 | C885910946 | 874042.26 | 871442.79 | M1860591867 | 0.0 | 0.0 | -2599.469999999972 | 0.0 |
| 1.0 | DEBIT | 4874.49 | C811207775 | 153.0 | 0.0 | C1971489295 | 253104.0 | 0.0 | -153.0 | -253104.0 |
| 1.0 | PAYMENT | 6440.78 | C1161148117 | 2192.0 | 0.0 | M516875052 | 0.0 | 0.0 | -2192.0 | 0.0 |
| 1.0 | PAYMENT | 4910.14 | C1131592118 | 41551.0 | 36640.86 | M589987187 | 0.0 | 0.0 | -4910.139999999999 | 0.0 |
| 1.0 | PAYMENT | 6444.64 | C1262609629 | 12019.0 | 5574.36 | M587180314 | 0.0 | 0.0 | -6444.64 | 0.0 |
| 1.0 | DEBIT | 5149.66 | C1955990522 | 4782.0 | 0.0 | C1330106945 | 52752.0 | 24044.18 | -4782.0 | -28707.82 |
| 1.0 | PAYMENT | 7292.16 | C69673470 | 216827.0 | 209534.84 | M1082411691 | 0.0 | 0.0 | -7292.1600000000035 | 0.0 |
| 1.0 | CASH_OUT | 47458.86 | C527211736 | 209534.84 | 162075.98 | C2096057945 | 52120.0 | 0.0 | -47458.859999999986 | -52120.0 |
| 1.0 | CASH_OUT | 136872.92 | C1533123860 | 162075.98 | 25203.05 | C766572210 | 217806.0 | 0.0 | -136872.93000000002 | -217806.0 |
| 1.0 | CASH_OUT | 94253.33 | C1718906711 | 25203.05 | 0.0 | C977993101 | 99773.0 | 965870.05 | -25203.05 | 866097.05 |
| 1.0 | PAYMENT | 2998.04 | C71802912 | 12030.0 | 9031.96 | M2134271532 | 0.0 | 0.0 | -2998.040000000001 | 0.0 |
| 1.0 | PAYMENT | 3454.08 | C686349795 | 9031.96 | 5577.88 | M1831010686 | 0.0 | 0.0 | -3454.079999999999 | 0.0 |
| 1.0 | PAYMENT | 4316.2 | C1423768154 | 10999.0 | 6682.8 | M404222443 | 0.0 | 0.0 | -4316.2 | 0.0 |
| 1.0 | PAYMENT | 2131.84 | C1987977423 | 224.0 | 0.0 | M61073295 | 0.0 | 0.0 | -224.0 | 0.0 |
| 1.0 | PAYMENT | 12986.61 | C807322507 | 23350.0 | 10363.39 | M396485834 | 0.0 | 0.0 | -12986.61 | 0.0 |
| 1.0 | TRANSFER | 42712.39 | C283039401 | 10363.39 | 0.0 | C1330106945 | 57901.66 | 24044.18 | -10363.39 | -33857.48 |
| 1.0 | TRANSFER | 77957.68 | C207471778 | 0.0 | 0.0 | C1761291320 | 94900.0 | 22233.65 | 0.0 | -72666.35 |
| 1.0 | TRANSFER | 17231.46 | C1243171897 | 0.0 | 0.0 | C783286238 | 24672.0 | 0.0 | 0.0 | -24672.0 |
| 1.0 | TRANSFER | 78766.03 | C1376151044 | 0.0 | 0.0 | C1749186397 | 103772.0 | 277515.05 | 0.0 | 173743.05 |
| 1.0 | TRANSFER | 224606.64 | C873175411 | 0.0 | 0.0 | C766572210 | 354678.92 | 0.0 | 0.0 | -354678.92 |
| 1.0 | TRANSFER | 125872.53 | C1443967876 | 0.0 | 0.0 | C392292416 | 348512.0 | 3420103.09 | 0.0 | 3071591.09 |
| 1.0 | TRANSFER | 379856.23 | C1449772539 | 0.0 | 0.0 | C1590550415 | 900180.0 | 1.916920493e7 | 0.0 | 1.826902493e7 |
| 1.0 | TRANSFER | 1505626.01 | C926859124 | 0.0 | 0.0 | C665576141 | 29031.0 | 5515763.34 | 0.0 | 5486732.34 |
| 1.0 | TRANSFER | 554026.99 | C1603696865 | 0.0 | 0.0 | C766572210 | 579285.56 | 0.0 | 0.0 | -579285.56 |
| 1.0 | TRANSFER | 147543.1 | C12905860 | 0.0 | 0.0 | C1359044626 | 223220.0 | 16518.36 | 0.0 | -206701.64 |
| 1.0 | TRANSFER | 761507.39 | C412788346 | 0.0 | 0.0 | C1590550415 | 1280036.23 | 1.916920493e7 | 0.0 | 1.78891687e7 |
| 1.0 | TRANSFER | 1429051.47 | C1520267010 | 0.0 | 0.0 | C1590550415 | 2041543.62 | 1.916920493e7 | 0.0 | 1.712766131e7 |
| 1.0 | TRANSFER | 358831.92 | C908084672 | 0.0 | 0.0 | C392292416 | 474384.53 | 3420103.09 | 0.0 | 2945718.5599999996 |
| 1.0 | TRANSFER | 367768.4 | C288306765 | 0.0 | 0.0 | C1359044626 | 370763.1 | 16518.36 | 0.0 | -354244.74 |
| 1.0 | TRANSFER | 209711.11 | C1556867940 | 0.0 | 0.0 | C1509514333 | 399214.71 | 2415.16 | 0.0 | -396799.55000000005 |
| 1.0 | TRANSFER | 583848.46 | C1839168128 | 0.0 | 0.0 | C1286084959 | 667778.0 | 2107778.11 | 0.0 | 1440000.1099999999 |
| 1.0 | TRANSFER | 1724887.05 | C1495608502 | 0.0 | 0.0 | C1590550415 | 3470595.1 | 1.916920493e7 | 0.0 | 1.569860983e7 |
| 1.0 | TRANSFER | 710544.77 | C835773569 | 0.0 | 0.0 | C1359044626 | 738531.5 | 16518.36 | 0.0 | -722013.14 |
| 1.0 | TRANSFER | 581294.26 | C843299092 | 0.0 | 0.0 | C1590550415 | 5195482.15 | 1.916920493e7 | 0.0 | 1.397372278e7 |
| 1.0 | TRANSFER | 11996.58 | C605982374 | 0.0 | 0.0 | C1225616405 | 40255.0 | 0.0 | 0.0 | -40255.0 |
| 1.0 | PAYMENT | 2875.1 | C1412322831 | 15443.0 | 12567.9 | M1651262695 | 0.0 | 0.0 | -2875.1000000000004 | 0.0 |
| 1.0 | PAYMENT | 8586.98 | C1305004711 | 3763.0 | 0.0 | M494077446 | 0.0 | 0.0 | -3763.0 | 0.0 |
| 1.0 | PAYMENT | 871.75 | C1003206025 | 19869.0 | 18997.25 | M989889899 | 0.0 | 0.0 | -871.75 | 0.0 |
| 1.0 | PAYMENT | 1035.36 | C806813022 | 71636.0 | 70600.64 | M902860396 | 0.0 | 0.0 | -1035.3600000000006 | 0.0 |
| 1.0 | PAYMENT | 1063.53 | C1406206626 | 83084.0 | 82020.47 | M1816522350 | 0.0 | 0.0 | -1063.5299999999988 | 0.0 |
| 1.0 | PAYMENT | 1019.9 | C1799230133 | 204237.0 | 203217.1 | M1521238608 | 0.0 | 0.0 | -1019.8999999999942 | 0.0 |
| 1.0 | PAYMENT | 4059.38 | C20156341 | 26304.0 | 22244.62 | M1111897955 | 0.0 | 0.0 | -4059.380000000001 | 0.0 |
| 1.0 | PAYMENT | 1876.44 | C1509309988 | 182.0 | 0.0 | M1643141512 | 0.0 | 0.0 | -182.0 | 0.0 |
| 1.0 | CASH_OUT | 28404.6 | C2091072548 | 0.0 | 0.0 | C1282788025 | 51744.0 | 0.0 | 0.0 | -51744.0 |
| 1.0 | CASH_OUT | 75405.1 | C263053820 | 0.0 | 0.0 | C1870252780 | 104209.0 | 46462.23 | 0.0 | -57746.77 |
| 1.0 | CASH_OUT | 50101.88 | C1740826931 | 0.0 | 0.0 | C97730845 | 67684.0 | 9940339.29 | 0.0 | 9872655.29 |
| 1.0 | CASH_OUT | 14121.82 | C69062746 | 0.0 | 0.0 | C100555887 | 52679.0 | 10963.66 | 0.0 | -41715.34 |
| 1.0 | CASH_OUT | 78292.91 | C1631227617 | 0.0 | 0.0 | C1983747920 | 121112.0 | 95508.95 | 0.0 | -25603.050000000003 |
| 1.0 | CASH_OUT | 176149.9 | C24650043 | 0.0 | 0.0 | C736709391 | 259813.0 | 46820.71 | 0.0 | -212992.29 |
| 1.0 | CASH_OUT | 212228.35 | C1896074070 | 0.0 | 0.0 | C401424608 | 429747.0 | 1178808.14 | 0.0 | 749061.1399999999 |
| 1.0 | CASH_OUT | 85423.63 | C460741164 | 0.0 | 0.0 | C1590550415 | 5776776.41 | 1.916920493e7 | 0.0 | 1.339242852e7 |
| 1.0 | CASH_OUT | 11648.5 | C781091365 | 0.0 | 0.0 | C985934102 | 260976.0 | 971418.91 | 0.0 | 710442.91 |
| 1.0 | PAYMENT | 6165.58 | C1858015030 | 20925.0 | 14759.42 | M25764044 | 0.0 | 0.0 | -6165.58 | 0.0 |
| 1.0 | PAYMENT | 3705.83 | C671596011 | 41903.46 | 38197.63 | M1925352804 | 0.0 | 0.0 | -3705.8300000000017 | 0.0 |
| 1.0 | CASH_OUT | 419801.4 | C1687354037 | 38197.63 | 0.0 | C33524623 | 499962.0 | 1517262.16 | -38197.63 | 1017300.1599999999 |
| 1.0 | CASH_OUT | 335416.51 | C743778731 | 144478.0 | 0.0 | C575335780 | 295.0 | 52415.15 | -144478.0 | 52120.15 |
| 1.0 | PAYMENT | 3372.29 | C967323951 | 41398.0 | 38025.71 | M1600594643 | 0.0 | 0.0 | -3372.290000000001 | 0.0 |
| 1.0 | PAYMENT | 661.43 | C743648472 | 14078.0 | 13416.57 | M692998280 | 0.0 | 0.0 | -661.4300000000003 | 0.0 |
| 1.0 | DEBIT | 864.68 | C1368862151 | 69836.0 | 68971.32 | C20671747 | 12040.0 | 43691.09 | -864.679999999993 | 31651.089999999997 |
| 1.0 | PAYMENT | 1203.44 | C922807452 | 29941.0 | 28737.56 | M33563464 | 0.0 | 0.0 | -1203.4399999999987 | 0.0 |
| 1.0 | TRANSFER | 330757.04 | C1494346128 | 103657.0 | 0.0 | C564160838 | 79676.0 | 1254956.07 | -103657.0 | 1175280.07 |
| 1.0 | PAYMENT | 1915.43 | C822087264 | 11450.0 | 9534.57 | M30699728 | 0.0 | 0.0 | -1915.4300000000003 | 0.0 |
| 1.0 | PAYMENT | 8.73 | C38603201 | 81313.0 | 81304.27 | M1422273905 | 0.0 | 0.0 | -8.729999999995925 | 0.0 |
| 1.0 | DEBIT | 3058.8 | C1694784135 | 18138.0 | 15079.2 | C801197928 | 11054.0 | 8917.54 | -3058.7999999999993 | -2136.459999999999 |
| 1.0 | PAYMENT | 5154.97 | C1207231495 | 9476.0 | 4321.03 | M756936249 | 0.0 | 0.0 | -5154.97 | 0.0 |
| 1.0 | PAYMENT | 5213.25 | C1221981006 | 36380.0 | 31166.75 | M264394929 | 0.0 | 0.0 | -5213.25 | 0.0 |
| 1.0 | PAYMENT | 5392.75 | C1878413714 | 50757.0 | 45364.25 | M769132147 | 0.0 | 0.0 | -5392.75 | 0.0 |
| 1.0 | PAYMENT | 25.12 | C1257299717 | 61663.0 | 61637.88 | M1474957626 | 0.0 | 0.0 | -25.12000000000262 | 0.0 |
| 1.0 | PAYMENT | 17150.89 | C181252244 | 61637.88 | 44486.99 | M1733022752 | 0.0 | 0.0 | -17150.89 | 0.0 |
| 1.0 | TRANSFER | 679502.24 | C722417467 | 290.0 | 0.0 | C451111351 | 171866.0 | 3940085.21 | -290.0 | 3768219.21 |
| 1.0 | TRANSFER | 177652.91 | C753631393 | 23720.0 | 0.0 | C716157500 | 55.0 | 4894.45 | -23720.0 | 4839.45 |
| 1.0 | PAYMENT | 5443.26 | C1262869688 | 236997.0 | 231553.74 | M1914108708 | 0.0 | 0.0 | -5443.260000000009 | 0.0 |
| 1.0 | PAYMENT | 244.87 | C544386226 | 257978.49 | 257733.62 | M1357700757 | 0.0 | 0.0 | -244.86999999999534 | 0.0 |
| 1.0 | PAYMENT | 8281.57 | C900298796 | 257733.62 | 249452.05 | M1889757798 | 0.0 | 0.0 | -8281.570000000007 | 0.0 |
| 1.0 | PAYMENT | 1175.59 | C1166106620 | 20509.0 | 19333.41 | M1932470703 | 0.0 | 0.0 | -1175.5900000000001 | 0.0 |
| 1.0 | PAYMENT | 1097.74 | C221861886 | 24866.0 | 23768.26 | M1713568869 | 0.0 | 0.0 | -1097.7400000000016 | 0.0 |
| 1.0 | TRANSFER | 184986.8 | C697508322 | 39588.0 | 0.0 | C1688019098 | 52340.0 | 97263.78 | -39588.0 | 44923.78 |
| 1.0 | PAYMENT | 1718.29 | C603658030 | 117811.0 | 116092.71 | M1689924104 | 0.0 | 0.0 | -1718.2899999999936 | 0.0 |
| 1.0 | PAYMENT | 3671.32 | C361380654 | 100094.0 | 96422.68 | M631673932 | 0.0 | 0.0 | -3671.320000000007 | 0.0 |
| 1.0 | DEBIT | 3580.26 | C1579132337 | 2565.0 | 0.0 | C1321640594 | 21185.0 | 18910.85 | -2565.0 | -2274.1500000000015 |
| 1.0 | PAYMENT | 8550.9 | C1795225096 | 40060.0 | 31509.1 | M790094605 | 0.0 | 0.0 | -8550.900000000001 | 0.0 |
| 1.0 | PAYMENT | 9753.55 | C1048712791 | 60829.0 | 51075.45 | M487792155 | 0.0 | 0.0 | -9753.550000000003 | 0.0 |
| 1.0 | PAYMENT | 3026.98 | C1909398279 | 31139.0 | 28112.02 | M1632670136 | 0.0 | 0.0 | -3026.9799999999996 | 0.0 |
| 1.0 | CASH_OUT | 467177.03 | C1338905451 | 28112.02 | 0.0 | C1170794006 | 975121.0 | 22190.99 | -28112.02 | -952930.01 |
| 1.0 | DEBIT | 3875.99 | C1252540239 | 259138.0 | 255262.01 | C1509514333 | 608925.82 | 2415.16 | -3875.9899999999907 | -606510.6599999999 |
| 1.0 | PAYMENT | 2148.89 | C1136005694 | 31213.0 | 29064.11 | M638486177 | 0.0 | 0.0 | -2148.8899999999994 | 0.0 |
| 1.0 | PAYMENT | 466.97 | C426019904 | 80030.0 | 79563.03 | M314411620 | 0.0 | 0.0 | -466.97000000000116 | 0.0 |
| 1.0 | CASH_OUT | 154139.72 | C1642679791 | 79563.03 | 0.0 | C564160838 | 410433.04 | 1254956.07 | -79563.03 | 844523.03 |
| 1.0 | CASH_OUT | 82685.93 | C855700733 | 0.0 | 0.0 | C392292416 | 833216.44 | 3420103.09 | 0.0 | 2586886.65 |
| 1.0 | CASH_OUT | 83857.23 | C247162961 | 0.0 | 0.0 | C1297685781 | 215457.59 | 16997.22 | 0.0 | -198460.37 |
| 1.0 | CASH_OUT | 99109.77 | C1890266440 | 0.0 | 0.0 | C1297685781 | 299314.82 | 16997.22 | 0.0 | -282317.6 |
| 1.0 | CASH_OUT | 187726.67 | C1527152775 | 0.0 | 0.0 | C747464370 | 190573.0 | 1567434.81 | 0.0 | 1376861.81 |
| 1.0 | CASH_OUT | 281339.92 | C1863655430 | 0.0 | 0.0 | C1509514333 | 612801.81 | 2415.16 | 0.0 | -610386.65 |
| 1.0 | CASH_OUT | 186447.51 | C976827477 | 0.0 | 0.0 | C1590550415 | 5862200.03 | 1.916920493e7 | 0.0 | 1.3307004899999999e7 |
| 1.0 | CASH_OUT | 81029.86 | C324112183 | 0.0 | 0.0 | C288665596 | 105343.0 | 8496.61 | 0.0 | -96846.39 |
| 1.0 | CASH_OUT | 139054.95 | C2092709730 | 0.0 | 0.0 | C1749186397 | 182538.03 | 277515.05 | 0.0 | 94977.01999999999 |
| 1.0 | CASH_OUT | 154716.2 | C980364771 | 0.0 | 0.0 | C453211571 | 187433.0 | 3461666.05 | 0.0 | 3274233.05 |
| 1.0 | CASH_OUT | 53631.83 | C1233595751 | 0.0 | 0.0 | C757108857 | 83244.0 | 0.0 | 0.0 | -83244.0 |
| 1.0 | CASH_OUT | 289645.52 | C1446001495 | 0.0 | 0.0 | C1023714065 | 871442.79 | 1412484.09 | 0.0 | 541041.3 |
| 1.0 | CASH_OUT | 267148.82 | C1261044180 | 0.0 | 0.0 | C401424608 | 641975.35 | 1178808.14 | 0.0 | 536832.7899999999 |
| 1.0 | CASH_OUT | 47913.58 | C141110631 | 0.0 | 0.0 | C240650537 | 51304.0 | 0.0 | 0.0 | -51304.0 |
| 1.0 | CASH_OUT | 193605.38 | C2029372696 | 0.0 | 0.0 | C1971489295 | 249452.05 | 0.0 | 0.0 | -249452.05 |
| 1.0 | CASH_OUT | 344464.4 | C793293778 | 0.0 | 0.0 | C766572210 | 1133312.56 | 0.0 | 0.0 | -1133312.56 |
| 1.0 | CASH_OUT | 169409.87 | C888611662 | 0.0 | 0.0 | C401424608 | 909124.16 | 1178808.14 | 0.0 | 269683.97999999986 |
| 1.0 | CASH_OUT | 25793.74 | C1966355106 | 0.0 | 0.0 | C476800120 | 215312.31 | 49864.36 | 0.0 | -165447.95 |
| 1.0 | CASH_OUT | 114712.48 | C599782425 | 0.0 | 0.0 | C1209271652 | 145400.0 | 0.0 | 0.0 | -145400.0 |
| 1.0 | CASH_OUT | 89038.75 | C1233327519 | 0.0 | 0.0 | C2083562754 | 189808.0 | 1186556.81 | 0.0 | 996748.81 |
| 1.0 | CASH_OUT | 1341.58 | C1155769010 | 0.0 | 0.0 | C557041912 | 80341.0 | 557537.26 | 0.0 | 477196.26 |
| 1.0 | CASH_OUT | 44443.08 | C269892014 | 0.0 | 0.0 | C1590550415 | 6048647.54 | 1.916920493e7 | 0.0 | 1.312055739e7 |
| 1.0 | CASH_OUT | 34729.64 | C1280641161 | 0.0 | 0.0 | C1435804085 | 44853.0 | 96.88 | 0.0 | -44756.12 |
| 1.0 | CASH_OUT | 105979.34 | C489411441 | 0.0 | 0.0 | C736709391 | 435962.9 | 46820.71 | 0.0 | -389142.19 |
| 1.0 | CASH_OUT | 57794.61 | C1141113940 | 0.0 | 0.0 | C240650537 | 99217.58 | 0.0 | 0.0 | -99217.58 |
| 1.0 | CASH_OUT | 15918.79 | C1711185459 | 0.0 | 0.0 | C1854778591 | 24553.0 | 0.0 | 0.0 | -24553.0 |
| 1.0 | CASH_OUT | 19162.08 | C25936709 | 0.0 | 0.0 | C885951223 | 60169.0 | 215851.28 | 0.0 | 155682.28 |
| 1.0 | CASH_OUT | 220691.42 | C1123559518 | 0.0 | 0.0 | C1590550415 | 6093090.62 | 1.916920493e7 | 0.0 | 1.3076114309999999e7 |
| 1.0 | CASH_OUT | 9536.54 | C649769713 | 0.0 | 0.0 | C1060830840 | 49161.0 | 130747.56 | 0.0 | 81586.56 |
| 1.0 | CASH_OUT | 29784.06 | C925150995 | 0.0 | 0.0 | C920011586 | 63553.0 | 0.0 | 0.0 | -63553.0 |
| 1.0 | CASH_OUT | 190445.51 | C1760219993 | 0.0 | 0.0 | C1360767589 | 203679.0 | 2107965.39 | 0.0 | 1904286.3900000001 |
| 1.0 | CASH_OUT | 57279.11 | C1800649922 | 0.0 | 0.0 | C824009085 | 127206.9 | 64106.18 | 0.0 | -63100.719999999994 |
| 1.0 | CASH_OUT | 112086.81 | C403547747 | 0.0 | 0.0 | C1531333864 | 292728.0 | 55974.56 | 0.0 | -236753.44 |
| 1.0 | CASH_OUT | 80311.89 | C172215878 | 0.0 | 0.0 | C434091818 | 87408.0 | 63112.23 | 0.0 | -24295.769999999997 |
| 1.0 | CASH_OUT | 151829.91 | C873309260 | 0.0 | 0.0 | C1023714065 | 1161088.31 | 1412484.09 | 0.0 | 251395.78000000003 |
| 1.0 | CASH_OUT | 26004.52 | C1112456099 | 0.0 | 0.0 | C1225616405 | 52251.58 | 0.0 | 0.0 | -52251.58 |
| 1.0 | CASH_OUT | 3146.16 | C923083575 | 0.0 | 0.0 | C1504109395 | 9471.0 | 593737.38 | 0.0 | 584266.38 |
| 1.0 | CASH_OUT | 296752.75 | C589363823 | 0.0 | 0.0 | C1531333864 | 404814.81 | 55974.56 | 0.0 | -348840.25 |
| 1.0 | CASH_OUT | 191702.06 | C2052457859 | 0.0 | 0.0 | C985934102 | 272624.5 | 971418.91 | 0.0 | 698794.41 |
| 1.0 | CASH_OUT | 365510.05 | C1299327689 | 0.0 | 0.0 | C564160838 | 564572.76 | 1254956.07 | 0.0 | 690383.31 |
| 1.0 | CASH_OUT | 202332.08 | C1408279755 | 0.0 | 0.0 | C453211571 | 342149.2 | 3461666.05 | 0.0 | 3119516.8499999996 |
| 1.0 | CASH_OUT | 55105.9 | C2007486296 | 0.0 | 0.0 | C932583850 | 317952.89 | 2719172.89 | 0.0 | 2401220.0 |
| 1.0 | CASH_OUT | 171508.59 | C1033348658 | 0.0 | 0.0 | C1971489295 | 443057.43 | 0.0 | 0.0 | -443057.43 |
| 1.0 | CASH_OUT | 178439.26 | C1634723627 | 0.0 | 0.0 | C2083562754 | 278846.75 | 1186556.81 | 0.0 | 907710.06 |
| 1.0 | CASH_OUT | 88834.86 | C938463537 | 0.0 | 0.0 | C985934102 | 464326.57 | 971418.91 | 0.0 | 507092.34 |
| 1.0 | CASH_OUT | 210370.09 | C2121995675 | 0.0 | 0.0 | C1170794006 | 1442298.03 | 22190.99 | 0.0 | -1420107.04 |
| 1.0 | CASH_OUT | 36437.06 | C2120063568 | 0.0 | 0.0 | C1740000325 | 154606.0 | 1363368.51 | 0.0 | 1208762.51 |
| 1.0 | CASH_OUT | 82691.56 | C1620409359 | 0.0 | 0.0 | C248609774 | 657983.89 | 6453430.91 | 0.0 | 5795447.0200000005 |
| 1.0 | CASH_OUT | 338767.1 | C691691381 | 0.0 | 0.0 | C453211571 | 544481.28 | 3461666.05 | 0.0 | 2917184.7699999996 |
| 1.0 | CASH_OUT | 187728.59 | C264978436 | 0.0 | 0.0 | C1360767589 | 394124.51 | 2107965.39 | 0.0 | 1713840.8800000001 |
| 1.0 | CASH_OUT | 271002.7 | C500618423 | 0.0 | 0.0 | C451111351 | 851368.24 | 3940085.21 | 0.0 | 3088716.9699999997 |
| 1.0 | CASH_OUT | 323105.08 | C1458091526 | 0.0 | 0.0 | C747464370 | 378299.67 | 1567434.81 | 0.0 | 1189135.1400000001 |
| 1.0 | CASH_OUT | 10782.94 | C768776793 | 0.0 | 0.0 | C796533847 | 100585.0 | 0.0 | 0.0 | -100585.0 |
| 1.0 | CASH_OUT | 89962.11 | C2018260103 | 0.0 | 0.0 | C240650537 | 157012.19 | 0.0 | 0.0 | -157012.19 |
| 1.0 | CASH_OUT | 101940.14 | C1393828949 | 0.0 | 0.0 | C357863579 | 105362.0 | 92307.65 | 0.0 | -13054.350000000006 |
| 1.0 | CASH_OUT | 163100.34 | C1088417975 | 0.0 | 0.0 | C401424608 | 1078534.03 | 1178808.14 | 0.0 | 100274.10999999987 |
| 1.0 | CASH_OUT | 136030.66 | C2036775591 | 0.0 | 0.0 | C757108857 | 136875.83 | 0.0 | 0.0 | -136875.83 |
| 1.0 | CASH_OUT | 68912.23 | C1620529408 | 0.0 | 0.0 | C1688019098 | 237326.8 | 97263.78 | 0.0 | -140063.02 |
| 1.0 | CASH_OUT | 73464.06 | C307488715 | 0.0 | 0.0 | C1789550256 | 444779.0 | 4619798.56 | 0.0 | 4175019.5599999996 |
| 1.0 | CASH_OUT | 106117.28 | C758572926 | 0.0 | 0.0 | C476402209 | 234216.94 | 51513.44 | 0.0 | -182703.5 |
| 1.0 | CASH_OUT | 503333.79 | C1640612861 | 0.0 | 0.0 | C985934102 | 553161.42 | 971418.91 | 0.0 | 418257.49 |
| 1.0 | CASH_OUT | 71991.42 | C990679445 | 0.0 | 0.0 | C557041912 | 81682.58 | 557537.26 | 0.0 | 475854.68 |
| 1.0 | CASH_OUT | 91379.93 | C913065088 | 0.0 | 0.0 | C401424608 | 1241634.38 | 1178808.14 | 0.0 | -62826.23999999999 |
| 1.0 | CASH_OUT | 26759.05 | C746280996 | 0.0 | 0.0 | C1360767589 | 581853.1 | 2107965.39 | 0.0 | 1526112.29 |
| 1.0 | CASH_OUT | 128487.35 | C50503805 | 0.0 | 0.0 | C747464370 | 701404.75 | 1567434.81 | 0.0 | 866030.06 |
| 1.0 | CASH_OUT | 252908.87 | C1557989809 | 0.0 | 0.0 | C33524623 | 919763.4 | 1517262.16 | 0.0 | 597498.7599999999 |
| 1.0 | CASH_OUT | 64489.64 | C146874094 | 0.0 | 0.0 | C1297685781 | 398424.59 | 16997.22 | 0.0 | -381427.37 |
| 1.0 | CASH_OUT | 171369.73 | C2123222442 | 0.0 | 0.0 | C451111351 | 1122370.94 | 3940085.21 | 0.0 | 2817714.27 |
| 1.0 | PAYMENT | 4493.29 | C1746570062 | 8258.0 | 3764.71 | M1555990397 | 0.0 | 0.0 | -4493.29 | 0.0 |
| 1.0 | PAYMENT | 12121.5 | C1544078442 | 97058.0 | 84936.5 | M1291367132 | 0.0 | 0.0 | -12121.5 | 0.0 |
| 1.0 | PAYMENT | 2278.19 | C1887699190 | 290.0 | 0.0 | M1479140596 | 0.0 | 0.0 | -290.0 | 0.0 |
| 1.0 | PAYMENT | 7707.44 | C1108889615 | 99827.0 | 92119.56 | M1275028674 | 0.0 | 0.0 | -7707.440000000002 | 0.0 |
| 1.0 | PAYMENT | 4013.02 | C455888635 | 92119.56 | 88106.55 | M204805934 | 0.0 | 0.0 | -4013.0099999999948 | 0.0 |
| 1.0 | PAYMENT | 5775.15 | C204322447 | 132329.0 | 126553.85 | M1019484860 | 0.0 | 0.0 | -5775.149999999994 | 0.0 |
| 1.0 | PAYMENT | 4843.93 | C1164365897 | 51718.0 | 46874.07 | M1640899500 | 0.0 | 0.0 | -4843.93 | 0.0 |
| 1.0 | PAYMENT | 2397.82 | C3565780 | 20421.0 | 18023.18 | M473666452 | 0.0 | 0.0 | -2397.8199999999997 | 0.0 |
| 1.0 | PAYMENT | 4391.44 | C1865219266 | 228.0 | 0.0 | M2138005960 | 0.0 | 0.0 | -228.0 | 0.0 |
| 1.0 | DEBIT | 4497.87 | C1278002745 | 107388.0 | 102890.13 | C510113906 | 19053.0 | 5417.23 | -4497.869999999995 | -13635.77 |
| 1.0 | PAYMENT | 7452.89 | C214649627 | 102397.0 | 94944.11 | M67730604 | 0.0 | 0.0 | -7452.889999999999 | 0.0 |
| 1.0 | PAYMENT | 3930.81 | C15477956 | 6032.0 | 2101.19 | M710623214 | 0.0 | 0.0 | -3930.81 | 0.0 |
| 1.0 | CASH_OUT | 71532.24 | C2082351661 | 2101.19 | 0.0 | C1810132623 | 74956.0 | 97128.19 | -2101.19 | 22172.190000000002 |
| 1.0 | CASH_OUT | 61159.51 | C859690270 | 0.0 | 0.0 | C476402209 | 340334.22 | 51513.44 | 0.0 | -288820.77999999997 |
| 1.0 | CASH_OUT | 12401.9 | C1389774257 | 0.0 | 0.0 | C1096283470 | 349763.0 | 0.0 | 0.0 | -349763.0 |
| 1.0 | CASH_OUT | 32702.61 | C553759818 | 0.0 | 0.0 | C75457651 | 253053.0 | 31469.78 | 0.0 | -221583.22 |
| 1.0 | PAYMENT | 6926.67 | C751021317 | 152927.0 | 146000.33 | M1896552614 | 0.0 | 0.0 | -6926.670000000013 | 0.0 |
| 1.0 | PAYMENT | 893.57 | C294658299 | 10676.0 | 9782.43 | M1033856359 | 0.0 | 0.0 | -893.5699999999997 | 0.0 |
| 1.0 | TRANSFER | 480222.51 | C201677908 | 11110.0 | 0.0 | C451111351 | 1293740.67 | 3940085.21 | -11110.0 | 2646344.54 |
| 1.0 | PAYMENT | 3608.41 | C635611994 | 11150.0 | 7541.59 | M904253669 | 0.0 | 0.0 | -3608.41 | 0.0 |
| 1.0 | PAYMENT | 5946.79 | C1930903395 | 7541.59 | 1594.8 | M1431710377 | 0.0 | 0.0 | -5946.79 | 0.0 |
| 1.0 | PAYMENT | 6099.96 | C1700721442 | 42133.0 | 36033.04 | M2116511124 | 0.0 | 0.0 | -6099.959999999999 | 0.0 |
| 1.0 | PAYMENT | 372.34 | C872522004 | 65254.0 | 64881.66 | M1348916831 | 0.0 | 0.0 | -372.3399999999965 | 0.0 |
| 1.0 | PAYMENT | 4635.18 | C1110698130 | 6313782.05 | 6309146.87 | M125644421 | 0.0 | 0.0 | -4635.179999999702 | 0.0 |
| 1.0 | PAYMENT | 1267.97 | C1053632127 | 6309146.87 | 6307878.9 | M1493158871 | 0.0 | 0.0 | -1267.9699999997392 | 0.0 |
| 1.0 | PAYMENT | 6911.99 | C89509666 | 6307878.9 | 6300966.92 | M1806880779 | 0.0 | 0.0 | -6911.980000000447 | 0.0 |
| 1.0 | PAYMENT | 1795.67 | C1016856028 | 6300966.92 | 6299171.25 | M446445803 | 0.0 | 0.0 | -1795.6699999999255 | 0.0 |
| 1.0 | PAYMENT | 3199.06 | C832292933 | 6299171.25 | 6295972.18 | M1280603381 | 0.0 | 0.0 | -3199.070000000298 | 0.0 |
| 1.0 | PAYMENT | 10265.17 | C792855998 | 6295972.18 | 6285707.02 | M1424108509 | 0.0 | 0.0 | -10265.160000000149 | 0.0 |
| 1.0 | PAYMENT | 9029.12 | C1003755748 | 25480.0 | 16450.88 | M1414013111 | 0.0 | 0.0 | -9029.119999999999 | 0.0 |
| 1.0 | PAYMENT | 7600.57 | C1805164661 | 50747.0 | 43146.43 | M778162712 | 0.0 | 0.0 | -7600.57 | 0.0 |
| 1.0 | PAYMENT | 2057.71 | C896138248 | 25132.0 | 23074.29 | M548482954 | 0.0 | 0.0 | -2057.709999999999 | 0.0 |
| 1.0 | PAYMENT | 3823.08 | C243575009 | 10382.0 | 6558.92 | M777341499 | 0.0 | 0.0 | -3823.08 | 0.0 |
| 1.0 | TRANSFER | 2806.0 | C1420196421 | 2806.0 | 0.0 | C972765878 | 0.0 | 0.0 | -2806.0 | 0.0 |
| 1.0 | CASH_OUT | 2806.0 | C2101527076 | 2806.0 | 0.0 | C1007251739 | 26202.0 | 0.0 | -2806.0 | -26202.0 |
| 1.0 | PAYMENT | 6737.2 | C1454026445 | 624.0 | 0.0 | M1378497201 | 0.0 | 0.0 | -624.0 | 0.0 |
| 1.0 | PAYMENT | 9161.98 | C892931811 | 11128.0 | 1966.02 | M1577167234 | 0.0 | 0.0 | -9161.98 | 0.0 |
| 1.0 | PAYMENT | 1239.06 | C1574873161 | 396386.0 | 395146.94 | M1591916281 | 0.0 | 0.0 | -1239.0599999999977 | 0.0 |
| 1.0 | PAYMENT | 365.0 | C600958416 | 319.0 | 0.0 | M1884231057 | 0.0 | 0.0 | -319.0 | 0.0 |
| 1.0 | PAYMENT | 5237.54 | C1492875057 | 27105.0 | 21867.46 | M333793193 | 0.0 | 0.0 | -5237.540000000001 | 0.0 |
| 1.0 | PAYMENT | 2618.76 | C1068945248 | 21867.46 | 19248.7 | M937277082 | 0.0 | 0.0 | -2618.7599999999984 | 0.0 |
| 1.0 | PAYMENT | 339.82 | C882646447 | 12076.0 | 11736.18 | M234740890 | 0.0 | 0.0 | -339.8199999999997 | 0.0 |
| 1.0 | DEBIT | 1393.46 | C79290250 | 100068.0 | 98674.54 | C1076835071 | 26446.0 | 318637.36 | -1393.4600000000064 | 292191.36 |
| 1.0 | PAYMENT | 2731.37 | C1586151649 | 17540.0 | 14808.63 | M179294202 | 0.0 | 0.0 | -2731.370000000001 | 0.0 |
| 1.0 | PAYMENT | 8364.73 | C1767230265 | 81.0 | 0.0 | M593103894 | 0.0 | 0.0 | -81.0 | 0.0 |
| 1.0 | PAYMENT | 6598.71 | C727666004 | 16392.0 | 9793.29 | M1601935322 | 0.0 | 0.0 | -6598.709999999999 | 0.0 |
| 1.0 | PAYMENT | 4304.58 | C414225167 | 10708.0 | 6403.42 | M1803093683 | 0.0 | 0.0 | -4304.58 | 0.0 |
| 1.0 | PAYMENT | 2867.14 | C975033189 | 943.0 | 0.0 | M962287291 | 0.0 | 0.0 | -943.0 | 0.0 |
| 1.0 | PAYMENT | 2946.38 | C628064884 | 0.0 | 0.0 | M109069556 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 507.12 | C1389509050 | 87005.0 | 86497.88 | M828326869 | 0.0 | 0.0 | -507.11999999999534 | 0.0 |
| 1.0 | PAYMENT | 3020.86 | C1761217448 | 50361.0 | 47340.14 | M1817789863 | 0.0 | 0.0 | -3020.8600000000006 | 0.0 |
| 1.0 | PAYMENT | 8948.03 | C788905599 | 11137.0 | 2188.97 | M1678709153 | 0.0 | 0.0 | -8948.03 | 0.0 |
| 1.0 | CASH_OUT | 280877.92 | C1544614339 | 2188.97 | 0.0 | C1297685781 | 462914.23 | 16997.22 | -2188.97 | -445917.01 |
| 1.0 | PAYMENT | 1084.9 | C2059592603 | 13727.0 | 12642.1 | M2016828666 | 0.0 | 0.0 | -1084.8999999999996 | 0.0 |
| 1.0 | PAYMENT | 9456.69 | C1688782916 | 12642.1 | 3185.41 | M1064154107 | 0.0 | 0.0 | -9456.69 | 0.0 |
| 1.0 | PAYMENT | 5812.36 | C1390301622 | 3185.41 | 0.0 | M415565467 | 0.0 | 0.0 | -3185.41 | 0.0 |
| 1.0 | TRANSFER | 157883.07 | C973936431 | 46547.0 | 0.0 | C1335050193 | 72793.0 | 353532.56 | -46547.0 | 280739.56 |
| 1.0 | PAYMENT | 2734.77 | C1756207614 | 4511.0 | 1776.23 | M1214133948 | 0.0 | 0.0 | -2734.77 | 0.0 |
| 1.0 | CASH_OUT | 102153.86 | C2015301874 | 1776.23 | 0.0 | C1335050193 | 230676.07 | 353532.56 | -1776.23 | 122856.48999999999 |
| 1.0 | PAYMENT | 584.52 | C712627377 | 15479.0 | 14894.48 | M782228073 | 0.0 | 0.0 | -584.5200000000004 | 0.0 |
| 1.0 | PAYMENT | 6113.14 | C399373008 | 15629.0 | 9515.86 | M391506011 | 0.0 | 0.0 | -6113.139999999999 | 0.0 |
| 1.0 | CASH_OUT | 369989.2 | C1936550492 | 9515.86 | 0.0 | C1789550256 | 518243.06 | 4619798.56 | -9515.86 | 4101555.4999999995 |
| 1.0 | CASH_OUT | 215338.28 | C594651850 | 0.0 | 0.0 | C75457651 | 285755.61 | 31469.78 | 0.0 | -254285.83 |
| 1.0 | CASH_OUT | 65521.42 | C1532139270 | 0.0 | 0.0 | C920011586 | 93337.06 | 0.0 | 0.0 | -93337.06 |
| 1.0 | CASH_OUT | 116977.58 | C1677568775 | 0.0 | 0.0 | C1740000325 | 191043.06 | 1363368.51 | 0.0 | 1172325.45 |
| 1.0 | CASH_OUT | 262392.36 | C2069500590 | 0.0 | 0.0 | C2083562754 | 457286.01 | 1186556.81 | 0.0 | 729270.8 |
| 1.0 | CASH_OUT | 51121.05 | C1431556341 | 0.0 | 0.0 | C1740000325 | 308020.64 | 1363368.51 | 0.0 | 1055347.87 |
| 1.0 | CASH_OUT | 202624.59 | C452364286 | 0.0 | 0.0 | C985934102 | 1056495.21 | 971418.91 | 0.0 | -85076.29999999993 |
| 1.0 | CASH_OUT | 118235.16 | C350069300 | 0.0 | 0.0 | C1335050193 | 332829.93 | 353532.56 | 0.0 | 20702.630000000005 |
| 1.0 | CASH_OUT | 118056.71 | C1060703587 | 0.0 | 0.0 | C757108857 | 272906.49 | 0.0 | 0.0 | -272906.49 |
| 1.0 | CASH_OUT | 498960.91 | C1957078537 | 0.0 | 0.0 | C1360767589 | 608612.15 | 2107965.39 | 0.0 | 1499353.2400000002 |
| 1.0 | CASH_OUT | 691738.36 | C1514214932 | 0.0 | 0.0 | C1590550415 | 6285707.02 | 1.916920493e7 | 0.0 | 1.288349791e7 |
| 1.0 | CASH_OUT | 58820.08 | C594858858 | 0.0 | 0.0 | C1360767589 | 1107573.06 | 2107965.39 | 0.0 | 1000392.3300000001 |
| 1.0 | PAYMENT | 4419.04 | C1423016050 | 52951.0 | 48531.96 | M352776719 | 0.0 | 0.0 | -4419.040000000001 | 0.0 |
| 1.0 | PAYMENT | 4270.02 | C1202042637 | 9516.0 | 5245.98 | M1112527632 | 0.0 | 0.0 | -4270.02 | 0.0 |
| 1.0 | PAYMENT | 3007.83 | C440736059 | 49905.0 | 46897.17 | M955324150 | 0.0 | 0.0 | -3007.8300000000017 | 0.0 |
| 1.0 | PAYMENT | 1824.59 | C1384563514 | 25138.0 | 23313.41 | M1852661033 | 0.0 | 0.0 | -1824.5900000000001 | 0.0 |
| 1.0 | PAYMENT | 993.46 | C1548946718 | 19448.0 | 18454.54 | M1814423236 | 0.0 | 0.0 | -993.4599999999991 | 0.0 |
| 1.0 | CASH_OUT | 302693.14 | C140404585 | 18454.54 | 0.0 | C1688019098 | 306239.03 | 97263.78 | -18454.54 | -208975.25000000003 |
| 1.0 | PAYMENT | 1445.06 | C207546206 | 10892.0 | 9446.94 | M1218519094 | 0.0 | 0.0 | -1445.0599999999995 | 0.0 |
| 1.0 | PAYMENT | 4059.25 | C200404000 | 4186.0 | 126.75 | M1894758168 | 0.0 | 0.0 | -4059.25 | 0.0 |
| 1.0 | PAYMENT | 3417.18 | C191945292 | 126.75 | 0.0 | M1242688388 | 0.0 | 0.0 | -126.75 | 0.0 |
| 1.0 | DEBIT | 3428.95 | C1317375498 | 147798.0 | 144369.05 | C665576141 | 1534657.01 | 5515763.34 | -3428.9500000000116 | 3981106.33 |
| 1.0 | PAYMENT | 5580.98 | C1130346421 | 7781.0 | 2200.02 | M1316005672 | 0.0 | 0.0 | -5580.98 | 0.0 |
| 1.0 | DEBIT | 3455.85 | C192428201 | 3509.0 | 53.15 | C401424608 | 1333014.31 | 1178808.14 | -3455.85 | -154206.17000000016 |
| 1.0 | PAYMENT | 7265.63 | C1768882706 | 14508.0 | 7242.37 | M1463022229 | 0.0 | 0.0 | -7265.63 | 0.0 |
| 1.0 | PAYMENT | 3852.64 | C472991420 | 1449076.27 | 1445223.63 | M1455855843 | 0.0 | 0.0 | -3852.6400000001304 | 0.0 |
| 1.0 | PAYMENT | 7870.29 | C1202220987 | 1445223.63 | 1437353.34 | M256346753 | 0.0 | 0.0 | -7870.289999999804 | 0.0 |
| 1.0 | DEBIT | 12872.92 | C425015667 | 11197.0 | 0.0 | C466505482 | 51885.0 | 0.0 | -11197.0 | -51885.0 |
| 1.0 | PAYMENT | 6675.34 | C2075568954 | 10107.0 | 3431.66 | M2145896000 | 0.0 | 0.0 | -6675.34 | 0.0 |
| 1.0 | CASH_OUT | 410201.89 | C1279740095 | 21448.0 | 0.0 | C1750905143 | 61613.0 | 424250.45 | -21448.0 | 362637.45 |
| 1.0 | PAYMENT | 13310.78 | C2095677157 | 0.0 | 0.0 | M852026681 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 664.35 | C1943855334 | 102983.0 | 102318.65 | M94919826 | 0.0 | 0.0 | -664.3500000000058 | 0.0 |
| 1.0 | PAYMENT | 2130.3 | C847320212 | 10958.0 | 8827.7 | M1201004240 | 0.0 | 0.0 | -2130.2999999999993 | 0.0 |
| 1.0 | PAYMENT | 7771.4 | C1406848276 | 8827.7 | 1056.3 | M349259569 | 0.0 | 0.0 | -7771.400000000001 | 0.0 |
| 1.0 | PAYMENT | 36349.35 | C785306763 | 1056.3 | 0.0 | M1204088028 | 0.0 | 0.0 | -1056.3 | 0.0 |
| 1.0 | PAYMENT | 749.34 | C1123236701 | 0.0 | 0.0 | M1552221437 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 2340.34 | C79161706 | 0.0 | 0.0 | M1685407532 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 9686.85 | C1057468716 | 0.0 | 0.0 | M1272051933 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 15785.85 | C1888924788 | 0.0 | 0.0 | M1041547629 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 9006.78 | C1016633682 | 0.0 | 0.0 | M2018220300 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 9014.95 | C1027319653 | 0.0 | 0.0 | M633079302 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 14420.66 | C1561745898 | 0.0 | 0.0 | M2033268925 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 4802.31 | C424786033 | 0.0 | 0.0 | M1545077099 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 28527.44 | C867093003 | 0.0 | 0.0 | M1135278099 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 4182.93 | C1470911015 | 0.0 | 0.0 | M1268974304 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 4088.21 | C244872973 | 0.0 | 0.0 | M451312813 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 8491.9 | C795748540 | 0.0 | 0.0 | M2081000371 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | DEBIT | 6666.78 | C837073696 | 11876.0 | 5209.22 | C655381473 | 24777.0 | 189534.74 | -6666.78 | 164757.74 |
| 1.0 | PAYMENT | 6536.86 | C416201381 | 17319.0 | 10782.14 | M1178290888 | 0.0 | 0.0 | -6536.860000000001 | 0.0 |
| 1.0 | CASH_OUT | 40684.97 | C1985938863 | 10782.14 | 0.0 | C1291286504 | 49530.0 | 66575.5 | -10782.14 | 17045.5 |
| 1.0 | PAYMENT | 1553.21 | C1660325375 | 31516.0 | 29962.79 | M251664534 | 0.0 | 0.0 | -1553.2099999999991 | 0.0 |
| 1.0 | DEBIT | 7344.92 | C334593716 | 0.0 | 0.0 | C485041780 | 185432.0 | 0.0 | 0.0 | -185432.0 |
| 1.0 | CASH_OUT | 106267.21 | C487416600 | 0.0 | 0.0 | C476800120 | 241106.05 | 49864.36 | 0.0 | -191241.69 |
| 1.0 | PAYMENT | 5491.5 | C1271041075 | 36605.0 | 31113.5 | M1141500277 | 0.0 | 0.0 | -5491.5 | 0.0 |
| 1.0 | PAYMENT | 3666.42 | C1544895390 | 48117.0 | 44450.58 | M712410791 | 0.0 | 0.0 | -3666.4199999999983 | 0.0 |
| 1.0 | PAYMENT | 5507.18 | C1971991758 | 36030.0 | 30522.82 | M2126723403 | 0.0 | 0.0 | -5507.18 | 0.0 |
| 1.0 | DEBIT | 4565.14 | C201274566 | 30265.0 | 25699.86 | C747464370 | 829892.09 | 1567434.81 | -4565.139999999999 | 737542.7200000001 |
| 1.0 | PAYMENT | 11348.71 | C354605216 | 734899.0 | 723550.29 | M824881806 | 0.0 | 0.0 | -11348.709999999963 | 0.0 |
| 1.0 | TRANSFER | 123179.97 | C1447353473 | 7215.0 | 0.0 | C20671747 | 12904.68 | 43691.09 | -7215.0 | 30786.409999999996 |
| 1.0 | PAYMENT | 7073.6 | C611289995 | 0.0 | 0.0 | M663466110 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 2803.83 | C1129433283 | 0.0 | 0.0 | M864138492 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 106.81 | C1810518740 | 0.0 | 0.0 | M295180183 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 6297.71 | C1547883191 | 762.0 | 0.0 | M1650113431 | 0.0 | 0.0 | -762.0 | 0.0 |
| 1.0 | PAYMENT | 2610.01 | C1528769018 | 58697.54 | 56087.54 | M183155105 | 0.0 | 0.0 | -2610.0 | 0.0 |
| 1.0 | PAYMENT | 5320.1 | C1089930529 | 56087.54 | 50767.43 | M2001115307 | 0.0 | 0.0 | -5320.110000000001 | 0.0 |
| 1.0 | PAYMENT | 1460.63 | C1429483328 | 1132.0 | 0.0 | M172794870 | 0.0 | 0.0 | -1132.0 | 0.0 |
| 1.0 | TRANSFER | 522264.81 | C1927963027 | 24.0 | 0.0 | C1234776885 | 11361.0 | 2025098.66 | -24.0 | 2013737.66 |
| 1.0 | DEBIT | 3584.21 | C833240229 | 135496.0 | 131911.79 | C1330106945 | 100614.05 | 24044.18 | -3584.209999999992 | -76569.87 |
| 1.0 | PAYMENT | 3704.97 | C1167088639 | 24553.0 | 20848.03 | M707531688 | 0.0 | 0.0 | -3704.970000000001 | 0.0 |
| 1.0 | PAYMENT | 2773.61 | C191568263 | 14332.0 | 11558.39 | M1799635803 | 0.0 | 0.0 | -2773.6100000000006 | 0.0 |
| 1.0 | PAYMENT | 3896.42 | C751608431 | 11558.39 | 7661.97 | M1723290893 | 0.0 | 0.0 | -3896.419999999999 | 0.0 |
| 1.0 | PAYMENT | 18781.84 | C893640573 | 7661.97 | 0.0 | M2085886997 | 0.0 | 0.0 | -7661.97 | 0.0 |
| 1.0 | PAYMENT | 6660.33 | C893523498 | 17710.0 | 11049.67 | M1770070706 | 0.0 | 0.0 | -6660.33 | 0.0 |
| 1.0 | TRANSFER | 26923.42 | C1108517064 | 11049.67 | 0.0 | C1983747920 | 199404.91 | 95508.95 | -11049.67 | -103895.96 |
| 1.0 | TRANSFER | 53783.57 | C511354923 | 0.0 | 0.0 | C1899073220 | 75514.0 | 420946.86 | 0.0 | 345432.86 |
| 1.0 | TRANSFER | 80333.28 | C992743048 | 0.0 | 0.0 | C1749186397 | 321592.98 | 277515.05 | 0.0 | -44077.92999999999 |
| 1.0 | TRANSFER | 211075.91 | C1540894701 | 0.0 | 0.0 | C564160838 | 930082.81 | 1254956.07 | 0.0 | 324873.26 |
| 1.0 | TRANSFER | 100677.86 | C203149502 | 0.0 | 0.0 | C1531333864 | 701567.56 | 55974.56 | 0.0 | -645593.0 |
| 1.0 | TRANSFER | 473499.51 | C1198197478 | 0.0 | 0.0 | C453211571 | 883248.39 | 3461666.05 | 0.0 | 2578417.6599999997 |
| 1.0 | TRANSFER | 1538200.39 | C476579021 | 0.0 | 0.0 | C1590550415 | 6977445.38 | 1.916920493e7 | 0.0 | 1.219175955e7 |
| 1.0 | TRANSFER | 2421578.09 | C106297322 | 0.0 | 0.0 | C1590550415 | 8515645.77 | 1.916920493e7 | 0.0 | 1.065355916e7 |
| 1.0 | TRANSFER | 1349670.68 | C1419332030 | 0.0 | 0.0 | C665576141 | 1538085.96 | 5515763.34 | 0.0 | 3977677.38 |
| 1.0 | TRANSFER | 314403.14 | C1262110193 | 0.0 | 0.0 | C1789550256 | 888232.25 | 4619798.56 | 0.0 | 3731566.3099999996 |
| 1.0 | TRANSFER | 1457213.54 | C396918327 | 0.0 | 0.0 | C1590550415 | 1.093722386e7 | 1.916920493e7 | 0.0 | 8231981.07 |
| 1.0 | TRANSFER | 180368.84 | C1055601039 | 0.0 | 0.0 | C1359044626 | 1437353.34 | 16518.36 | 0.0 | -1420834.98 |
| 1.0 | TRANSFER | 445038.71 | C547441493 | 0.0 | 0.0 | C1531333864 | 802245.42 | 55974.56 | 0.0 | -746270.8600000001 |
| 1.0 | TRANSFER | 1123206.66 | C967677821 | 0.0 | 0.0 | C451111351 | 1773963.18 | 3940085.21 | 0.0 | 2166122.0300000003 |
| 1.0 | DEBIT | 996.0 | C1839206329 | 46132.0 | 45136.0 | C998351292 | 88106.55 | 1015132.48 | -996.0 | 927025.9299999999 |
| 1.0 | TRANSFER | 176334.26 | C169880779 | 45136.0 | 0.0 | C1286084959 | 1251626.46 | 2107778.11 | -45136.0 | 856151.6499999999 |
| 1.0 | TRANSFER | 161217.61 | C552674617 | 0.0 | 0.0 | C909295153 | 199733.0 | 5602234.95 | 0.0 | 5402501.95 |
| 1.0 | TRANSFER | 108443.61 | C1974622245 | 0.0 | 0.0 | C240650537 | 246974.3 | 0.0 | 0.0 | -246974.3 |
| 1.0 | TRANSFER | 75442.68 | C402808045 | 0.0 | 0.0 | C1318822808 | 80469.0 | 500631.71 | 0.0 | 420162.71 |
| 1.0 | TRANSFER | 55536.86 | C332365138 | 0.0 | 0.0 | C392292416 | 915902.37 | 3420103.09 | 0.0 | 2504200.7199999997 |
| 1.0 | TRANSFER | 438437.09 | C977160959 | 0.0 | 0.0 | C248609774 | 740675.45 | 6453430.91 | 0.0 | 5712755.46 |
| 1.0 | TRANSFER | 928722.69 | C1563053805 | 0.0 | 0.0 | C985934102 | 1259119.8 | 971418.91 | 0.0 | -287700.89 |
| 1.0 | TRANSFER | 1478772.04 | C1464177809 | 0.0 | 0.0 | C1170794006 | 1652668.12 | 22190.99 | 0.0 | -1630477.1300000001 |
| 1.0 | TRANSFER | 2545478.01 | C1057507014 | 0.0 | 0.0 | C1590550415 | 1.23944374e7 | 1.916920493e7 | 0.0 | 6774767.529999999 |
| 1.0 | TRANSFER | 2061082.82 | C2007599722 | 0.0 | 0.0 | C1590550415 | 1.493991542e7 | 1.916920493e7 | 0.0 | 4229289.51 |
| 1.0 | TRANSFER | 983479.66 | C2029780820 | 0.0 | 0.0 | C1170794006 | 3131440.16 | 22190.99 | 0.0 | -3109249.17 |
| 1.0 | TRANSFER | 635507.97 | C65080774 | 0.0 | 0.0 | C747464370 | 834457.23 | 1567434.81 | 0.0 | 732977.5800000001 |
| 1.0 | TRANSFER | 848231.57 | C2116179210 | 0.0 | 0.0 | C1170794006 | 4114919.82 | 22190.99 | 0.0 | -4092728.8299999996 |
| 1.0 | TRANSFER | 739112.93 | C1172535934 | 0.0 | 0.0 | C564160838 | 1141158.73 | 1254956.07 | 0.0 | 113797.34000000008 |
| 1.0 | TRANSFER | 324397.94 | C1648700617 | 0.0 | 0.0 | C932583850 | 373058.79 | 2719172.89 | 0.0 | 2346114.1 |
| 1.0 | TRANSFER | 982862.81 | C1765900922 | 0.0 | 0.0 | C985934102 | 2187842.49 | 971418.91 | 0.0 | -1216423.58 |
| 1.0 | TRANSFER | 955855.0 | C94830685 | 0.0 | 0.0 | C248609774 | 1179112.54 | 6453430.91 | 0.0 | 5274318.37 |
| 1.0 | TRANSFER | 179253.24 | C1539947037 | 0.0 | 0.0 | C1870252780 | 179614.1 | 46462.23 | 0.0 | -133151.87 |
| 1.0 | PAYMENT | 5446.88 | C270661321 | 666.0 | 0.0 | M1964434661 | 0.0 | 0.0 | -666.0 | 0.0 |
| 1.0 | PAYMENT | 648.64 | C1217312754 | 873.0 | 224.36 | M1585571244 | 0.0 | 0.0 | -648.64 | 0.0 |
| 1.0 | PAYMENT | 8494.71 | C1099552523 | 11129.0 | 2634.29 | M66724371 | 0.0 | 0.0 | -8494.71 | 0.0 |
| 1.0 | CASH_OUT | 373068.26 | C1047934137 | 20034.0 | 0.0 | C1286084959 | 1427960.73 | 2107778.11 | -20034.0 | 679817.3799999999 |
| 1.0 | CASH_IN | 143236.26 | C1862994526 | 0.0 | 143236.26 | C1688019098 | 608932.17 | 97263.78 | 143236.26 | -511668.39 |
| 1.0 | CASH_IN | 228451.89 | C1614133563 | 143236.26 | 371688.15 | C2083562754 | 719678.38 | 1186556.81 | 228451.89 | 466878.43000000005 |
| 1.0 | CASH_IN | 35902.49 | C839771540 | 371688.15 | 407590.65 | C2001112025 | 49003.3 | 0.0 | 35902.5 | -49003.3 |
| 1.0 | CASH_IN | 232953.64 | C1037163664 | 407590.65 | 640544.28 | C33524623 | 1172672.27 | 1517262.16 | 232953.63 | 344589.8899999999 |
| 1.0 | CASH_IN | 65912.95 | C180316302 | 640544.28 | 706457.23 | C1330106945 | 104198.26 | 24044.18 | 65912.94999999995 | -80154.07999999999 |
| 1.0 | CASH_IN | 193492.68 | C1200546947 | 706457.23 | 899949.91 | C1531333864 | 1247284.13 | 55974.56 | 193492.68000000005 | -1191309.5699999998 |
| 1.0 | CASH_IN | 60836.64 | C443713699 | 899949.91 | 960786.56 | C488044861 | 143436.0 | 5203.54 | 60836.65000000002 | -138232.46 |
| 1.0 | CASH_IN | 62325.15 | C695530017 | 960786.56 | 1023111.71 | C564160838 | 1880271.66 | 1254956.07 | 62325.14999999991 | -625315.5899999999 |
| 1.0 | CASH_IN | 349640.51 | C1493042329 | 1023111.71 | 1372752.22 | C909295153 | 360950.61 | 5602234.95 | 349640.51 | 5241284.34 |
| 1.0 | CASH_IN | 135324.19 | C1751403001 | 1372752.22 | 1508076.41 | C453211571 | 1356747.89 | 3461666.05 | 135324.18999999994 | 2104918.16 |
| 1.0 | CASH_IN | 380015.14 | C1717433286 | 1508076.41 | 1888091.55 | C401424608 | 1336470.16 | 1178808.14 | 380015.14000000013 | -157662.02000000002 |
| 1.0 | CASH_IN | 418688.27 | C1756819670 | 1888091.55 | 2306779.82 | C401424608 | 956455.03 | 1178808.14 | 418688.2699999998 | 222353.10999999987 |
| 1.0 | CASH_IN | 311023.52 | C1078262677 | 2306779.82 | 2617803.34 | C766572210 | 1477776.96 | 0.0 | 311023.52 | -1477776.96 |
| 1.0 | CASH_IN | 151862.38 | C178604517 | 2617803.34 | 2769665.72 | C920011586 | 158858.48 | 0.0 | 151862.38000000035 | -158858.48 |
| 1.0 | CASH_IN | 228710.05 | C57624756 | 2769665.72 | 2998375.78 | C1297685781 | 743792.14 | 16997.22 | 228710.0599999996 | -726794.92 |
| 1.0 | CASH_IN | 220431.09 | C1543518287 | 2998375.78 | 3218806.87 | C451111351 | 2897169.84 | 3940085.21 | 220431.09000000032 | 1042915.3700000001 |
| 1.0 | CASH_IN | 182861.45 | C998242313 | 3218806.87 | 3401668.32 | C485041780 | 192776.92 | 0.0 | 182861.44999999972 | -192776.92 |
| 1.0 | CASH_IN | 231881.5 | C464872674 | 3401668.32 | 3633549.82 | C1509514333 | 894141.72 | 2415.16 | 231881.5 | -891726.5599999999 |
| 1.0 | CASH_IN | 38071.17 | C1659286984 | 3633549.82 | 3671620.98 | C601893033 | 122487.0 | 54985.05 | 38071.16000000015 | -67501.95 |
| 1.0 | CASH_IN | 87117.29 | C1064905627 | 3671620.98 | 3758738.27 | C75457651 | 501093.89 | 31469.78 | 87117.29000000004 | -469624.11 |
| 1.0 | CASH_IN | 114271.72 | C2072130509 | 3758738.27 | 3873009.99 | C985934102 | 3170705.3 | 971418.91 | 114271.7200000002 | -2199286.3899999997 |
| 1.0 | CASH_IN | 73199.1 | C1260440107 | 3873009.99 | 3946209.08 | C1749186397 | 401926.25 | 277515.05 | 73199.08999999985 | -124411.20000000001 |
| 1.0 | CASH_IN | 764772.98 | C482307698 | 3946209.08 | 4710982.07 | C985934102 | 3056433.58 | 971418.91 | 764772.9900000002 | -2085014.67 |
| 1.0 | CASH_IN | 106847.36 | C1793899405 | 4710982.07 | 4817829.42 | C575335780 | 335711.51 | 52415.15 | 106847.34999999963 | -283296.36 |
| 1.0 | CASH_IN | 6287.28 | C1765702 | 4817829.42 | 4824116.71 | C1651855867 | 11274.0 | 0.0 | 6287.290000000037 | -11274.0 |
| 1.0 | CASH_IN | 24936.34 | C1302725372 | 4824116.71 | 4849053.05 | C660143728 | 104408.0 | 42450.71 | 24936.33999999985 | -61957.29 |
| 1.0 | CASH_IN | 55837.16 | C713898436 | 4849053.05 | 4904890.21 | C100555887 | 66800.82 | 10963.66 | 55837.16000000015 | -55837.16 |
| 1.0 | CASH_IN | 63255.29 | C632475595 | 4904890.21 | 4968145.5 | C1100439041 | 237735.3 | 0.0 | 63255.29000000004 | -237735.3 |
| 1.0 | CASH_IN | 104605.78 | C1920305914 | 4968145.5 | 5072751.28 | C357863579 | 207302.14 | 92307.65 | 104605.78000000026 | -114994.49000000002 |
| 1.0 | CASH_IN | 154160.82 | C263833514 | 5072751.28 | 5226912.1 | C503195940 | 157376.0 | 0.0 | 154160.81999999937 | -157376.0 |
| 1.0 | CASH_IN | 257348.03 | C1278839936 | 5226912.1 | 5484260.13 | C985934102 | 2291660.6 | 971418.91 | 257348.03000000026 | -1320241.69 |
| 1.0 | CASH_IN | 201073.81 | C2143739483 | 5484260.13 | 5685333.94 | C401424608 | 537766.76 | 1178808.14 | 201073.81000000052 | 641041.3799999999 |
| 1.0 | CASH_IN | 53560.68 | C565881091 | 5685333.94 | 5738894.62 | C1761291320 | 172857.68 | 22233.65 | 53560.6799999997 | -150624.03 |
| 1.0 | CASH_IN | 334234.39 | C1618984457 | 5738894.62 | 6073129.01 | C1870252780 | 358867.35 | 46462.23 | 334234.38999999966 | -312405.12 |
| 1.0 | CASH_IN | 227335.16 | C1621254922 | 6073129.01 | 6300464.17 | C564160838 | 1817946.5 | 1254956.07 | 227335.16000000015 | -562990.4299999999 |
| 1.0 | CASH_IN | 134426.08 | C702500163 | 6300464.17 | 6434890.26 | C575335780 | 228864.16 | 52415.15 | 134426.08999999985 | -176449.01 |
| 1.0 | CASH_IN | 2643.45 | C1574509514 | 6434890.26 | 6437533.71 | C215145189 | 49974.0 | 1891.79 | 2643.4500000001863 | -48082.21 |
| 1.0 | CASH_IN | 134977.11 | C1087072654 | 6437533.71 | 6572510.82 | C1750905143 | 471814.89 | 424250.45 | 134977.11000000034 | -47564.44 |
| 1.0 | CASH_IN | 25090.03 | C864221358 | 6572510.82 | 6597600.85 | C1282788025 | 80148.6 | 0.0 | 25090.02999999933 | -80148.6 |
| 1.0 | CASH_IN | 147388.67 | C1617174216 | 6597600.85 | 6744989.53 | C824009085 | 184486.01 | 64106.18 | 147388.68000000063 | -120379.83000000002 |
| 1.0 | CASH_IN | 217615.09 | C1979080261 | 6744989.53 | 6962604.61 | C392292416 | 971439.24 | 3420103.09 | 217615.08000000007 | 2448663.8499999996 |
| 1.0 | CASH_IN | 355294.5 | C1860886124 | 6962604.61 | 7317899.11 | C747464370 | 1469965.2 | 1567434.81 | 355294.5 | 97469.6100000001 |
| 1.0 | CASH_IN | 12336.48 | C1250499735 | 7317899.11 | 7330235.59 | C990398217 | 21024.0 | 83845.22 | 12336.479999999516 | 62821.22 |
| 1.0 | CASH_IN | 349505.89 | C173791568 | 7330235.59 | 7679741.48 | C1590550415 | 1.700099823e7 | 1.916920493e7 | 349505.8900000006 | 2168206.6999999993 |
| 1.0 | CASH_IN | 285185.34 | C1293462056 | 7679741.48 | 7964926.82 | C1335050193 | 451065.09 | 353532.56 | 285185.33999999985 | -97532.53000000003 |
| 1.0 | CASH_IN | 132953.45 | C1966670937 | 7964926.82 | 8097880.27 | C1359044626 | 1617722.17 | 16518.36 | 132953.44999999925 | -1601203.8099999998 |
| 1.0 | CASH_IN | 214851.41 | C2002174925 | 8097880.27 | 8312731.68 | C33524623 | 939718.63 | 1517262.16 | 214851.41000000015 | 577543.5299999999 |
| 1.0 | CASH_IN | 54988.56 | C588449070 | 8312731.68 | 8367720.25 | C11003494 | 60870.0 | 1.058888527e7 | 54988.5700000003 | 1.052801527e7 |
| 1.0 | CASH_IN | 62871.06 | C735228558 | 8367720.25 | 8430591.3 | C716157500 | 177707.91 | 4894.45 | 62871.050000000745 | -172813.46 |
| 1.0 | CASH_IN | 61150.85 | C376725601 | 8430591.3 | 8491742.16 | C1360767589 | 1166393.13 | 2107965.39 | 61150.859999999404 | 941572.2600000002 |
| 1.0 | CASH_IN | 131409.27 | C1476235721 | 8491742.16 | 8623151.43 | C1068824137 | 290276.03 | 265092.36 | 131409.26999999955 | -25183.670000000042 |
| 1.0 | CASH_IN | 10844.33 | C1828508781 | 8623151.43 | 8633995.76 | C2083117811 | 42274.0 | 290772.6 | 10844.330000000075 | 248498.59999999998 |
| 1.0 | PAYMENT | 2054.84 | C1376017854 | 22195.0 | 20140.16 | M1732663543 | 0.0 | 0.0 | -2054.84 | 0.0 |
| 1.0 | CASH_IN | 313373.56 | C1552870927 | 20140.16 | 333513.72 | C985934102 | 2034312.57 | 971418.91 | 313373.56 | -1062893.6600000001 |
| 1.0 | CASH_IN | 13289.87 | C1448805967 | 333513.72 | 346803.59 | C1225616405 | 78256.1 | 0.0 | 13289.870000000054 | -78256.1 |
| 1.0 | CASH_IN | 27070.11 | C641882263 | 346803.59 | 373873.7 | C1568059495 | 70595.0 | 122750.49 | 27070.109999999986 | 52155.490000000005 |
| 1.0 | CASH_IN | 127954.7 | C1930837320 | 373873.7 | 501828.4 | C564160838 | 1590611.34 | 1254956.07 | 127954.70000000001 | -335655.27 |
| 1.0 | CASH_IN | 197031.04 | C1489193907 | 501828.4 | 698859.44 | C1297685781 | 515082.09 | 16997.22 | 197031.03999999992 | -498084.87 |
| 1.0 | CASH_IN | 144366.64 | C1453606810 | 698859.44 | 843226.08 | C985934102 | 1720939.01 | 971418.91 | 144366.64 | -749520.1 |
| 1.0 | CASH_IN | 7965.49 | C1645624121 | 843226.08 | 851191.57 | C195600860 | 35100.09 | 40348.79 | 7965.489999999991 | 5248.700000000004 |
| 1.0 | CASH_IN | 123103.74 | C547923534 | 851191.57 | 974295.31 | C736709391 | 541942.24 | 46820.71 | 123103.7400000001 | -495121.52999999997 |
| 1.0 | CASH_IN | 106634.77 | C1660223291 | 974295.31 | 1080930.07 | C1170794006 | 4963151.39 | 22190.99 | 106634.76000000001 | -4940960.399999999 |
| 1.0 | CASH_IN | 74933.22 | C379124840 | 1080930.07 | 1155863.3 | C1023714065 | 1312918.22 | 1412484.09 | 74933.22999999998 | 99565.87000000011 |
| 1.0 | CASH_IN | 120234.99 | C1157943921 | 1155863.3 | 1276098.29 | C977993101 | 194026.33 | 965870.05 | 120234.98999999999 | 771843.7200000001 |
| 1.0 | CASH_IN | 5763.99 | C544966217 | 1276098.29 | 1281862.28 | C1870252780 | 24632.95 | 46462.23 | 5763.989999999991 | 21829.280000000002 |
| 1.0 | CASH_IN | 156922.36 | C1706272858 | 1281862.28 | 1438784.65 | C665576141 | 2887756.64 | 5515763.34 | 156922.36999999988 | 2628006.6999999997 |
| 1.0 | CASH_IN | 162665.98 | C882471736 | 1438784.65 | 1601450.63 | C1096283470 | 362164.9 | 0.0 | 162665.97999999998 | -362164.9 |
| 1.0 | CASH_IN | 110226.34 | C1475192960 | 1601450.63 | 1601450.63 | C1816757085 | 1601450.63 | 1.068123879e7 | 0.0 | 9079788.16 |
| 1.0 | CASH_IN | 142577.44 | C727197178 | 1601450.63 | 1744028.07 | C1100439041 | 174480.01 | 0.0 | 142577.44000000018 | -174480.01 |
| 1.0 | CASH_IN | 6915.38 | C1406253491 | 1744028.07 | 1750943.45 | C85777802 | 49685.0 | 0.0 | 6915.379999999888 | -49685.0 |
| 1.0 | CASH_IN | 157108.01 | C749604930 | 1750943.45 | 1908051.46 | C985934102 | 1576572.37 | 971418.91 | 157108.01 | -605153.4600000001 |
| 1.0 | CASH_IN | 223555.14 | C373097727 | 1908051.46 | 2131606.6 | C564160838 | 1462656.64 | 1254956.07 | 223555.14000000013 | -207700.56999999983 |
| 1.0 | CASH_IN | 146331.12 | C2082509879 | 2131606.6 | 2277937.72 | C1531333864 | 1053791.45 | 55974.56 | 146331.1200000001 | -997816.8899999999 |
| 1.0 | CASH_IN | 141131.23 | C576894497 | 2277937.72 | 2419068.96 | C985934102 | 1419464.36 | 971418.91 | 141131.23999999976 | -448045.45000000007 |
| 1.0 | CASH_IN | 222711.47 | C2123533871 | 2419068.96 | 2641780.43 | C1590550415 | 1.665149234e7 | 1.916920493e7 | 222711.4700000002 | 2517712.59 |
| 1.0 | CASH_IN | 84980.31 | C16148478 | 2641780.43 | 2726760.74 | C1335050193 | 165879.75 | 353532.56 | 84980.31000000006 | 187652.81 |
| 1.0 | CASH_IN | 628719.07 | C2022689531 | 2726760.74 | 3355479.81 | C1359044626 | 1484768.73 | 16518.36 | 628719.0699999998 | -1468250.3699999999 |
| 1.0 | CASH_IN | 79285.8 | C1340848245 | 3355479.81 | 3434765.61 | C1170794006 | 4856516.62 | 22190.99 | 79285.79999999981 | -4834325.63 |
| 1.0 | CASH_IN | 57809.81 | C635610193 | 3434765.61 | 3492575.42 | C1454031203 | 59666.0 | 0.0 | 57809.810000000056 | -59666.0 |
| 1.0 | CASH_IN | 75043.17 | C379121284 | 3492575.42 | 3567618.59 | C1511785794 | 156547.0 | 0.0 | 75043.16999999993 | -156547.0 |
| 1.0 | CASH_IN | 248196.92 | C281421502 | 3567618.59 | 3815815.51 | C1297685781 | 318051.05 | 16997.22 | 248196.91999999993 | -301053.82999999996 |
| 1.0 | CASH_IN | 18851.44 | C50072771 | 3815815.51 | 3834666.95 | C215145189 | 47330.55 | 1891.79 | 18851.44000000041 | -45438.76 |
| 1.0 | CASH_IN | 403418.39 | C848097505 | 3834666.95 | 4238085.34 | C1286084959 | 1801028.99 | 2107778.11 | 403418.38999999966 | 306749.1199999999 |
| 1.0 | CASH_IN | 235564.81 | C1672598778 | 4238085.34 | 4473650.15 | C451111351 | 2676738.75 | 3940085.21 | 235564.81000000052 | 1263346.46 |
| 1.0 | CASH_IN | 32325.24 | C1122233828 | 4473650.15 | 4505975.39 | C1068824137 | 158866.76 | 265092.36 | 32325.239999999292 | 106225.59999999998 |
| 1.0 | CASH_IN | 345347.7 | C538667887 | 4505975.39 | 4851323.08 | C240650537 | 355417.92 | 0.0 | 345347.6900000004 | -355417.92 |
| 1.0 | CASH_IN | 355500.25 | C1967496309 | 4851323.08 | 5206823.33 | C766572210 | 1166753.44 | 0.0 | 355500.25 | -1166753.44 |
| 1.0 | CASH_IN | 103005.78 | C406749219 | 5206823.33 | 5309829.12 | C716157500 | 114836.85 | 4894.45 | 103005.79000000004 | -109942.40000000001 |
| 1.0 | CASH_IN | 108803.98 | C1173340685 | 5309829.12 | 5418633.09 | C766572210 | 811253.19 | 0.0 | 108803.96999999974 | -811253.19 |
| 1.0 | CASH_IN | 259753.27 | C1045731788 | 5418633.09 | 5678386.36 | C1209271652 | 260112.48 | 0.0 | 259753.27000000048 | -260112.48 |
| 1.0 | CASH_IN | 234094.24 | C1739267143 | 5678386.36 | 5912480.6 | C985934102 | 1278333.12 | 971418.91 | 234094.2399999993 | -306914.2100000001 |
| 1.0 | CASH_IN | 273305.73 | C192456457 | 5912480.6 | 6185786.33 | C1023714065 | 1237985.0 | 1412484.09 | 273305.73000000045 | 174499.09000000008 |
| 1.0 | CASH_IN | 186838.95 | C817689537 | 6185786.33 | 6372625.28 | C736709391 | 418838.5 | 46820.71 | 186838.9500000002 | -372017.79 |
| 1.0 | CASH_IN | 193161.04 | C708613859 | 6372625.28 | 6565786.32 | C451111351 | 2441173.94 | 3940085.21 | 193161.04000000004 | 1498911.27 |
| 1.0 | CASH_IN | 152823.85 | C365625031 | 6565786.32 | 6718610.17 | C1789550256 | 1202635.39 | 4619798.56 | 152823.84999999963 | 3417163.17 |
| 1.0 | CASH_IN | 91457.82 | C1531200408 | 6718610.17 | 6810067.99 | C1509514333 | 662260.23 | 2415.16 | 91457.8200000003 | -659845.07 |
| 1.0 | CASH_IN | 277807.48 | C212963786 | 6810067.99 | 7087875.47 | C1531333864 | 907460.33 | 55974.56 | 277807.4799999995 | -851485.77 |
| 1.0 | CASH_IN | 8679.13 | C1123321137 | 7087875.47 | 7096554.61 | C575335780 | 94438.08 | 52415.15 | 8679.140000000596 | -42022.93 |
| 1.0 | CASH_IN | 2099.59 | C685934 | 7096554.61 | 7098654.2 | C1854778591 | 40471.79 | 0.0 | 2099.589999999851 | -40471.79 |
| 1.0 | CASH_IN | 221693.08 | C2032909428 | 7098654.2 | 7320347.28 | C1531333864 | 629652.85 | 55974.56 | 221693.08000000007 | -573678.29 |
| 1.0 | CASH_IN | 206097.65 | C2031377754 | 7320347.28 | 7526444.93 | C564160838 | 1239101.5 | 1254956.07 | 206097.64999999944 | 15854.570000000065 |
| 1.0 | CASH_IN | 22384.21 | C523211332 | 7526444.93 | 7548829.13 | C459857341 | 42211.0 | 387263.02 | 22384.200000000186 | 345052.02 |
| 1.0 | CASH_IN | 73210.99 | C1280124872 | 7548829.13 | 7622040.12 | C1435804085 | 79582.64 | 96.88 | 73210.99000000022 | -79485.76 |
| 1.0 | CASH_IN | 236747.6 | C1747053097 | 7622040.12 | 7858787.73 | C757108857 | 390963.21 | 0.0 | 236747.61000000034 | -390963.21 |
| 1.0 | CASH_IN | 6284.18 | C864326906 | 7858787.73 | 7865071.9 | C1749186397 | 328727.16 | 277515.05 | 6284.1699999999255 | -51212.109999999986 |
| 1.0 | CASH_IN | 100198.77 | C1348115836 | 7865071.9 | 7965270.68 | C1750905143 | 336837.78 | 424250.45 | 100198.77999999933 | 87412.66999999998 |
| 1.0 | CASH_IN | 160347.32 | C1972143064 | 7965270.68 | 8125617.99 | C33524623 | 724867.22 | 1517262.16 | 160347.31000000052 | 792394.94 |
| 1.0 | CASH_IN | 289272.75 | C312168418 | 8125617.99 | 8414890.75 | C75457651 | 413976.6 | 31469.78 | 289272.7599999998 | -382506.81999999995 |
| 1.0 | CASH_IN | 63189.26 | C95369743 | 8414890.75 | 8478080.0 | C1860513229 | 101925.0 | 0.0 | 63189.25 | -101925.0 |
| 1.0 | CASH_IN | 196725.32 | C26928827 | 8478080.0 | 8674805.32 | C451111351 | 2248012.9 | 3940085.21 | 196725.3200000003 | 1692072.31 |
| 1.0 | PAYMENT | 5882.32 | C278476563 | 10252.0 | 4369.68 | M1479909053 | 0.0 | 0.0 | -5882.32 | 0.0 |
| 1.0 | DEBIT | 625.92 | C615954678 | 30165.0 | 29539.08 | C811587677 | 1042.0 | 0.0 | -625.9199999999983 | -1042.0 |
| 1.0 | PAYMENT | 5937.72 | C1926180325 | 21779.0 | 15841.28 | M733461760 | 0.0 | 0.0 | -5937.719999999999 | 0.0 |
| 1.0 | PAYMENT | 9796.29 | C1594039997 | 274658.0 | 264861.71 | M1323531427 | 0.0 | 0.0 | -9796.289999999979 | 0.0 |
| 1.0 | PAYMENT | 13449.95 | C1170788511 | 11080.0 | 0.0 | M1400973979 | 0.0 | 0.0 | -11080.0 | 0.0 |
| 1.0 | PAYMENT | 4749.6 | C347091104 | 8038.0 | 3288.4 | M1105416433 | 0.0 | 0.0 | -4749.6 | 0.0 |
| 1.0 | PAYMENT | 6460.86 | C1110195322 | 31009.0 | 24548.14 | M404947798 | 0.0 | 0.0 | -6460.860000000001 | 0.0 |
| 1.0 | PAYMENT | 3605.26 | C226690498 | 10120.0 | 6514.74 | M1455204443 | 0.0 | 0.0 | -3605.26 | 0.0 |
| 1.0 | PAYMENT | 624.61 | C1976602906 | 52594.0 | 51969.39 | M1978209605 | 0.0 | 0.0 | -624.6100000000006 | 0.0 |
| 1.0 | PAYMENT | 3854.92 | C1624351535 | 9241.0 | 5386.08 | M1394356510 | 0.0 | 0.0 | -3854.92 | 0.0 |
| 1.0 | TRANSFER | 420532.33 | C582300198 | 5386.08 | 0.0 | C1971489295 | 614566.02 | 0.0 | -5386.08 | -614566.02 |
| 1.0 | PAYMENT | 4402.86 | C533415944 | 4469.0 | 66.14 | M1919834117 | 0.0 | 0.0 | -4402.86 | 0.0 |
| 1.0 | CASH_OUT | 147804.02 | C1086849943 | 66.14 | 0.0 | C1182461167 | 158919.0 | 23508.22 | -66.14 | -135410.78 |
| 1.0 | PAYMENT | 151.2 | C1265053098 | 1904.0 | 1752.8 | M246003654 | 0.0 | 0.0 | -151.20000000000005 | 0.0 |
| 1.0 | PAYMENT | 58.21 | C235370598 | 14506.0 | 14447.79 | M248710794 | 0.0 | 0.0 | -58.20999999999913 | 0.0 |
| 1.0 | PAYMENT | 5936.82 | C2083217543 | 741.0 | 0.0 | M1073741635 | 0.0 | 0.0 | -741.0 | 0.0 |
| 1.0 | CASH_OUT | 145944.67 | C177104018 | 24376.0 | 0.0 | C1123629720 | 222.0 | 46393.85 | -24376.0 | 46171.85 |
| 1.0 | TRANSFER | 7206.33 | C478139423 | 24932.0 | 17725.67 | C1032986144 | 21308.0 | 18161.79 | -7206.330000000002 | -3146.209999999999 |
| 1.0 | DEBIT | 5581.55 | C997695567 | 530.0 | 0.0 | C1282788025 | 55058.57 | 0.0 | -530.0 | -55058.57 |
| 1.0 | DEBIT | 5853.21 | C2066892165 | 5039.0 | 0.0 | C662736689 | 20018.0 | 4891090.56 | -5039.0 | 4871072.56 |
| 1.0 | PAYMENT | 2528.1 | C48305285 | 5037.0 | 2508.9 | M1625090026 | 0.0 | 0.0 | -2528.1 | 0.0 |
| 1.0 | PAYMENT | 5655.17 | C1714378342 | 9522.0 | 3866.83 | M265824587 | 0.0 | 0.0 | -5655.17 | 0.0 |
| 1.0 | PAYMENT | 1456.09 | C93798665 | 101018.0 | 99561.91 | M41092582 | 0.0 | 0.0 | -1456.0899999999965 | 0.0 |
| 1.0 | PAYMENT | 4252.82 | C519303080 | 5545.0 | 1292.18 | M1345265484 | 0.0 | 0.0 | -4252.82 | 0.0 |
| 1.0 | TRANSFER | 276460.62 | C1871680329 | 595.0 | 0.0 | C1360767589 | 1105242.28 | 2107965.39 | -595.0 | 1002723.1100000001 |
| 1.0 | PAYMENT | 8811.67 | C1976166251 | 30848.0 | 22036.33 | M1950800085 | 0.0 | 0.0 | -8811.669999999998 | 0.0 |
| 1.0 | PAYMENT | 12666.68 | C1547398254 | 891.0 | 0.0 | M1337829755 | 0.0 | 0.0 | -891.0 | 0.0 |
| 1.0 | CASH_OUT | 95636.49 | C1979055448 | 0.0 | 0.0 | C1740000325 | 359141.7 | 1363368.51 | 0.0 | 1004226.81 |
| 1.0 | CASH_OUT | 195314.34 | C934740803 | 0.0 | 0.0 | C2083562754 | 491226.48 | 1186556.81 | 0.0 | 695330.3300000001 |
| 1.0 | PAYMENT | 6049.09 | C812143047 | 1.0 | 0.0 | M314364096 | 0.0 | 0.0 | -1.0 | 0.0 |
| 1.0 | PAYMENT | 14306.8 | C1215951090 | 0.0 | 0.0 | M1205559205 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 1117.34 | C894421232 | 35611.0 | 34493.66 | M1098986569 | 0.0 | 0.0 | -1117.3399999999965 | 0.0 |
| 1.0 | DEBIT | 3179.7 | C1182311147 | 124703.85 | 121524.14 | C1540270363 | 20767.0 | 0.0 | -3179.7100000000064 | -20767.0 |
| 1.0 | PAYMENT | 2880.68 | C1531182070 | 121524.14 | 118643.46 | M276869158 | 0.0 | 0.0 | -2880.679999999993 | 0.0 |
| 1.0 | PAYMENT | 3060.54 | C1491860739 | 118643.46 | 115582.92 | M1527214863 | 0.0 | 0.0 | -3060.540000000008 | 0.0 |
| 1.0 | PAYMENT | 879.42 | C534753234 | 115582.92 | 114703.5 | M172699023 | 0.0 | 0.0 | -879.4199999999983 | 0.0 |
| 1.0 | PAYMENT | 3486.78 | C231725600 | 114703.5 | 111216.72 | M1831882653 | 0.0 | 0.0 | -3486.779999999999 | 0.0 |
| 1.0 | PAYMENT | 21530.96 | C259251414 | 111216.72 | 89685.77 | M1552400354 | 0.0 | 0.0 | -21530.949999999997 | 0.0 |
| 1.0 | PAYMENT | 2426.31 | C803893384 | 89685.77 | 87259.46 | M1078566479 | 0.0 | 0.0 | -2426.3099999999977 | 0.0 |
| 1.0 | PAYMENT | 5110.1 | C1372422140 | 87259.46 | 82149.35 | M819245704 | 0.0 | 0.0 | -5110.110000000001 | 0.0 |
| 1.0 | PAYMENT | 207.75 | C1288108586 | 82149.35 | 81941.6 | M1089584667 | 0.0 | 0.0 | -207.75 | 0.0 |
| 1.0 | PAYMENT | 18437.04 | C2054757222 | 81941.6 | 63504.55 | M1865201235 | 0.0 | 0.0 | -18437.050000000003 | 0.0 |
| 1.0 | PAYMENT | 8690.69 | C348132918 | 63504.55 | 54813.86 | M1493988307 | 0.0 | 0.0 | -8690.690000000002 | 0.0 |
| 1.0 | PAYMENT | 4946.03 | C1805443519 | 54813.86 | 49867.83 | M1636322481 | 0.0 | 0.0 | -4946.029999999999 | 0.0 |
| 1.0 | PAYMENT | 5432.09 | C1978504976 | 49867.83 | 44435.75 | M1338368149 | 0.0 | 0.0 | -5432.080000000002 | 0.0 |
| 1.0 | PAYMENT | 7649.41 | C1129869771 | 44435.75 | 36786.34 | M1620459733 | 0.0 | 0.0 | -7649.4100000000035 | 0.0 |
| 1.0 | PAYMENT | 3943.17 | C2044337856 | 36786.34 | 32843.16 | M435914790 | 0.0 | 0.0 | -3943.179999999993 | 0.0 |
| 1.0 | PAYMENT | 3101.33 | C422409467 | 32843.16 | 29741.84 | M1273958371 | 0.0 | 0.0 | -3101.3200000000033 | 0.0 |
| 1.0 | TRANSFER | 51719.13 | C1659515968 | 882.0 | 0.0 | C1629911510 | 59670.0 | 15375.37 | -882.0 | -44294.63 |
| 1.0 | PAYMENT | 13465.91 | C664091267 | 0.0 | 0.0 | M1433208870 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 19561.05 | C1724814719 | 0.0 | 0.0 | M1437988306 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 9762.28 | C1543146693 | 129.0 | 0.0 | M1718322084 | 0.0 | 0.0 | -129.0 | 0.0 |
| 1.0 | PAYMENT | 9778.81 | C1956415355 | 18824.0 | 9045.19 | M251520863 | 0.0 | 0.0 | -9778.81 | 0.0 |
| 1.0 | CASH_OUT | 562903.81 | C24039137 | 9045.19 | 0.0 | C33524623 | 564519.9 | 1517262.16 | -9045.19 | 952742.2599999999 |
| 1.0 | PAYMENT | 5055.94 | C992086987 | 30295.0 | 25239.06 | M731243659 | 0.0 | 0.0 | -5055.939999999999 | 0.0 |
| 1.0 | PAYMENT | 2858.89 | C1244867001 | 21820.0 | 18961.11 | M1037955032 | 0.0 | 0.0 | -2858.8899999999994 | 0.0 |
| 1.0 | PAYMENT | 8531.42 | C369805307 | 18961.11 | 10429.69 | M314562573 | 0.0 | 0.0 | -8531.42 | 0.0 |
| 1.0 | PAYMENT | 84.22 | C1100109058 | 25828.0 | 25743.78 | M333693383 | 0.0 | 0.0 | -84.22000000000116 | 0.0 |
| 1.0 | CASH_OUT | 227478.01 | C1394010463 | 25743.78 | 0.0 | C1590550415 | 1.642878087e7 | 1.916920493e7 | -25743.78 | 2740424.0600000005 |
| 1.0 | PAYMENT | 5033.02 | C1057307776 | 23946.7 | 18913.68 | M1819038759 | 0.0 | 0.0 | -5033.02 | 0.0 |
| 1.0 | TRANSFER | 100588.8 | C1636588948 | 18913.68 | 0.0 | C2050019814 | 105223.0 | 0.0 | -18913.68 | -105223.0 |
| 1.0 | PAYMENT | 9374.72 | C95685867 | 21064.0 | 11689.28 | M331596257 | 0.0 | 0.0 | -9374.72 | 0.0 |
| 1.0 | PAYMENT | 598.24 | C494953170 | 102631.0 | 102032.76 | M509864971 | 0.0 | 0.0 | -598.2400000000052 | 0.0 |
| 1.0 | PAYMENT | 4531.55 | C999647352 | 20125.0 | 15593.45 | M911501858 | 0.0 | 0.0 | -4531.549999999999 | 0.0 |
| 1.0 | PAYMENT | 2998.2 | C677120200 | 15593.45 | 12595.25 | M694069884 | 0.0 | 0.0 | -2998.2000000000007 | 0.0 |
| 1.0 | PAYMENT | 9269.99 | C957923719 | 12595.25 | 3325.26 | M747158012 | 0.0 | 0.0 | -9269.99 | 0.0 |
| 1.0 | PAYMENT | 1209.64 | C1692073709 | 3325.26 | 2115.61 | M948675904 | 0.0 | 0.0 | -1209.65 | 0.0 |
| 1.0 | PAYMENT | 1298.7 | C1197498159 | 2115.61 | 816.91 | M785271142 | 0.0 | 0.0 | -1298.7000000000003 | 0.0 |
| 1.0 | PAYMENT | 7797.0 | C500230084 | 816.91 | 0.0 | M2023026843 | 0.0 | 0.0 | -816.91 | 0.0 |
| 1.0 | PAYMENT | 5831.03 | C1028145537 | 0.0 | 0.0 | M1628551735 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 7067.9 | C2078641942 | 0.0 | 0.0 | M606775513 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 14895.51 | C1527882132 | 0.0 | 0.0 | M935160003 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 3316.21 | C1626852381 | 0.0 | 0.0 | M300604602 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 22276.0 | C1193398802 | 0.0 | 0.0 | M453398853 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 16505.51 | C1171255580 | 0.0 | 0.0 | M34073107 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 636.95 | C1275009283 | 0.0 | 0.0 | M1658512704 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 2056.59 | C1478995734 | 0.0 | 0.0 | M1699578227 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 704.74 | C1456061400 | 0.0 | 0.0 | M1661777060 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 3222.32 | C104261836 | 0.0 | 0.0 | M259630944 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 11889.61 | C351656492 | 0.0 | 0.0 | M316335490 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 9491.89 | C597364637 | 0.0 | 0.0 | M1637613097 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 6131.21 | C1527086220 | 0.0 | 0.0 | M515273883 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 11938.61 | C1697281847 | 0.0 | 0.0 | M1535026957 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 4233.45 | C737104370 | 0.0 | 0.0 | M105966264 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 32643.79 | C821405322 | 0.0 | 0.0 | M505231702 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 622.15 | C1328323494 | 14112.8 | 13490.65 | M1311292881 | 0.0 | 0.0 | -622.1499999999996 | 0.0 |
| 1.0 | PAYMENT | 4573.12 | C1408533352 | 13490.65 | 8917.54 | M617928649 | 0.0 | 0.0 | -4573.109999999999 | 0.0 |
| 1.0 | PAYMENT | 6057.8 | C252615006 | 6996.1 | 938.3 | M79919963 | 0.0 | 0.0 | -6057.8 | 0.0 |
| 1.0 | PAYMENT | 5770.32 | C1943750504 | 938.3 | 0.0 | M1907604549 | 0.0 | 0.0 | -938.3 | 0.0 |
| 1.0 | PAYMENT | 9152.97 | C176955204 | 0.0 | 0.0 | M353023213 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | DEBIT | 12154.7 | C1011044643 | 39699.0 | 27544.3 | C451111351 | 2051287.58 | 3940085.21 | -12154.7 | 1888797.63 |
| 1.0 | PAYMENT | 8378.01 | C1358857082 | 80899.44 | 72521.43 | M979550238 | 0.0 | 0.0 | -8378.01000000001 | 0.0 |
| 1.0 | PAYMENT | 9616.45 | C2056234595 | 72521.43 | 62904.98 | M1558395480 | 0.0 | 0.0 | -9616.44999999999 | 0.0 |
| 1.0 | PAYMENT | 2231.28 | C2023917549 | 12998.0 | 10766.72 | M1416415959 | 0.0 | 0.0 | -2231.2800000000007 | 0.0 |
| 1.0 | PAYMENT | 4932.54 | C1398260359 | 136555.0 | 131622.46 | M371397455 | 0.0 | 0.0 | -4932.540000000008 | 0.0 |
| 1.0 | CASH_IN | 65413.89 | C2052223881 | 131622.46 | 197036.35 | C614685048 | 144605.0 | 0.0 | 65413.890000000014 | -144605.0 |
| 1.0 | CASH_IN | 98560.08 | C1213871206 | 197036.35 | 295596.43 | C434091818 | 167719.89 | 63112.23 | 98560.07999999999 | -104607.66 |
| 1.0 | CASH_IN | 15054.61 | C628392976 | 295596.43 | 310651.04 | C832279283 | 33340.0 | 185389.8 | 15054.609999999986 | 152049.8 |
| 1.0 | CASH_IN | 161632.16 | C725832346 | 310651.04 | 472283.2 | C985934102 | 1044238.89 | 971418.91 | 161632.16000000003 | -72819.97999999998 |
| 1.0 | CASH_IN | 47529.19 | C1113895488 | 472283.2 | 519812.39 | C1359044626 | 856049.65 | 16518.36 | 47529.19 | -839531.29 |
| 1.0 | CASH_IN | 9577.45 | C1527007086 | 519812.39 | 529389.85 | C838411509 | 32204.0 | 22774.25 | 9577.459999999963 | -9429.75 |
| 1.0 | CASH_IN | 414238.59 | C1523649562 | 529389.85 | 943628.44 | C1359044626 | 808520.46 | 16518.36 | 414238.58999999997 | -792002.1 |
| 1.0 | CASH_IN | 89327.65 | C1807176280 | 943628.44 | 1032956.09 | C451111351 | 2063442.28 | 3940085.21 | 89327.65000000002 | 1876642.93 |
| 1.0 | CASH_IN | 194900.16 | C203819996 | 1032956.09 | 1227856.25 | C1262822392 | 8633995.76 | 1.249436715e7 | 194900.16000000003 | 3860371.3900000006 |
| 1.0 | CASH_IN | 24470.43 | C1977618945 | 1227856.25 | 1252326.68 | C1360767589 | 1381702.9 | 2107965.39 | 24470.429999999935 | 726262.4900000002 |
| 1.0 | CASH_IN | 217668.38 | C662925691 | 1252326.68 | 1469995.06 | C476800120 | 347373.26 | 49864.36 | 217668.38000000012 | -297508.9 |
| 1.0 | CASH_IN | 9067.51 | C1995952705 | 1469995.06 | 1479062.57 | C1182461167 | 306723.02 | 23508.22 | 9067.51000000001 | -283214.80000000005 |
| 1.0 | CASH_IN | 336827.91 | C1244880808 | 1479062.57 | 1815890.48 | C476402209 | 401493.73 | 51513.44 | 336827.9099999999 | -349980.29 |
| 1.0 | CASH_IN | 45317.95 | C1060519157 | 1815890.48 | 1861208.43 | C2096057945 | 99578.86 | 0.0 | 45317.94999999995 | -99578.86 |
| 1.0 | CASH_IN | 230021.01 | C58890945 | 1861208.43 | 2091229.45 | C1170794006 | 4777230.82 | 22190.99 | 230021.02000000002 | -4755039.83 |
| 1.0 | CASH_IN | 154578.28 | C1269773610 | 2091229.45 | 2245807.73 | C564160838 | 1033003.86 | 1254956.07 | 154578.28000000003 | 221952.21000000008 |
| 1.0 | CASH_IN | 314134.01 | C464649704 | 2245807.73 | 2559941.74 | C1509514333 | 570802.41 | 2415.16 | 314134.01000000024 | -568387.25 |
| 1.0 | CASH_IN | 206405.92 | C367967231 | 2559941.74 | 2766347.65 | C453211571 | 1221423.7 | 3461666.05 | 206405.90999999968 | 2240242.3499999996 |
| 1.0 | CASH_IN | 158995.29 | C36531985 | 2766347.65 | 2925342.94 | C1816757085 | 8674805.32 | 1.068123879e7 | 158995.29000000004 | 2006433.4699999988 |
| 1.0 | CASH_IN | 188238.54 | C815336475 | 2925342.94 | 3113581.47 | C766572210 | 702449.21 | 0.0 | 188238.53000000026 | -702449.21 |
| 1.0 | CASH_IN | 21255.8 | C609483101 | 3113581.47 | 3134837.27 | C1381128261 | 52109.0 | 0.0 | 21255.799999999814 | -52109.0 |
| 1.0 | CASH_IN | 205955.77 | C1149407083 | 3134837.27 | 3340793.04 | C1688019098 | 465695.91 | 97263.78 | 205955.77000000002 | -368432.13 |
| 1.0 | CASH_IN | 178370.36 | C767436045 | 3340793.04 | 3519163.4 | C1590550415 | 1.665625888e7 | 1.916920493e7 | 178370.35999999987 | 2512946.049999999 |
| 1.0 | CASH_IN | 42547.96 | C1430199669 | 3519163.4 | 3561711.36 | C673068808 | 59693.0 | 9672.67 | 42547.95999999996 | -50020.33 |
| 1.0 | CASH_IN | 15152.15 | C1847431070 | 3561711.36 | 3576863.51 | C1262822392 | 8439095.6 | 1.249436715e7 | 15152.149999999907 | 4055271.5500000007 |
| 1.0 | CASH_IN | 39249.67 | C1043639521 | 3576863.51 | 3616113.18 | C1225616405 | 64966.23 | 0.0 | 39249.67000000039 | -64966.23 |
| 1.0 | CASH_IN | 101669.17 | C1660837991 | 3616113.18 | 3717782.35 | C1234776885 | 533625.81 | 2025098.66 | 101669.16999999993 | 1491472.8499999999 |
| 1.0 | CASH_IN | 81694.11 | C1325866488 | 3717782.35 | 3799476.47 | C2083562754 | 686540.82 | 1186556.81 | 81694.12000000011 | 500015.9900000001 |
| 1.0 | CASH_IN | 10266.7 | C1306794745 | 3799476.47 | 3809743.17 | C1256728724 | 104323.0 | 69756.86 | 10266.69999999972 | -34566.14 |
| 1.0 | CASH_IN | 35502.25 | C1454171136 | 3809743.17 | 3845245.42 | C1832580921 | 41159.0 | 0.0 | 35502.25 | -41159.0 |
| 1.0 | CASH_IN | 183250.0 | C1195396074 | 3845245.42 | 4028495.42 | C736709391 | 231999.55 | 46820.71 | 183250.0 | -185178.84 |
| 1.0 | CASH_IN | 219220.21 | C811562535 | 4028495.42 | 4247715.63 | C451111351 | 1974114.63 | 3940085.21 | 219220.20999999996 | 1965970.58 |
| 1.0 | CASH_IN | 254895.5 | C1560379655 | 4247715.63 | 4502611.13 | C1509514333 | 256668.41 | 2415.16 | 254895.5 | -254253.25 |
| 1.0 | CASH_IN | 143148.97 | C1824322115 | 4502611.13 | 4645760.1 | C1170794006 | 4547209.8 | 22190.99 | 143148.96999999974 | -4525018.81 |
| 1.0 | CASH_IN | 132457.52 | C702999333 | 4645760.1 | 4778217.62 | C1262822392 | 8423943.45 | 1.249436715e7 | 132457.52000000048 | 4070423.700000001 |
| 1.0 | CASH_IN | 148834.28 | C1648582256 | 4778217.62 | 4927051.9 | C766572210 | 514210.68 | 0.0 | 148834.28000000026 | -514210.68 |
| 1.0 | CASH_IN | 106727.81 | C865858182 | 4927051.9 | 5033779.71 | C665576141 | 2730834.27 | 5515763.34 | 106727.80999999959 | 2784929.07 |
| 1.0 | CASH_IN | 223123.71 | C951988316 | 5033779.71 | 5256903.42 | C1359044626 | 394281.87 | 16518.36 | 223123.70999999996 | -377763.51 |
| 1.0 | CASH_IN | 194311.34 | C647973805 | 5256903.42 | 5451214.77 | C33524623 | 1127423.71 | 1517262.16 | 194311.34999999963 | 389838.44999999995 |
| 1.0 | CASH_IN | 338964.91 | C1591161296 | 5451214.77 | 5790179.68 | C766572210 | 365376.39 | 0.0 | 338964.91000000015 | -365376.39 |
| 1.0 | CASH_IN | 247575.4 | C842331982 | 5790179.68 | 6037755.07 | C1262822392 | 8291485.93 | 1.249436715e7 | 247575.3900000006 | 4202881.220000001 |
| 1.0 | CASH_IN | 29467.02 | C2076249476 | 6037755.07 | 6067222.09 | C1749186397 | 322442.98 | 277515.05 | 29467.019999999553 | -44927.92999999999 |
| 1.0 | CASH_IN | 90340.16 | C660595570 | 6067222.09 | 6157562.25 | C1590550415 | 1.647788852e7 | 1.916920493e7 | 90340.16000000015 | 2691316.41 |
| 1.0 | CASH_IN | 198847.32 | C1088491512 | 6157562.25 | 6356409.58 | C1023714065 | 964679.26 | 1412484.09 | 198847.33000000007 | 447804.8300000001 |
| 1.0 | CASH_IN | 155075.41 | C1434066477 | 6356409.58 | 6511484.99 | C1971489295 | 1035098.36 | 0.0 | 155075.41000000015 | -1035098.36 |
| 1.0 | CASH_IN | 76932.89 | C648315947 | 6511484.99 | 6588417.88 | C1023714065 | 765831.94 | 1412484.09 | 76932.88999999966 | 646652.1500000001 |
| 1.0 | CASH_IN | 92971.79 | C2117642238 | 6588417.88 | 6681389.66 | C796533847 | 111367.94 | 0.0 | 92971.78000000026 | -111367.94 |
| 1.0 | CASH_IN | 7322.98 | C913242382 | 6681389.66 | 6688712.64 | C146305349 | 64966.0 | 57643.02 | 7322.979999999516 | -7322.980000000003 |
| 1.0 | CASH_IN | 222126.95 | C870322840 | 6688712.64 | 6910839.59 | C1286084959 | 1397610.6 | 2107778.11 | 222126.9500000002 | 710167.5099999998 |
| 1.0 | CASH_IN | 62983.91 | C837246227 | 6910839.59 | 6973823.5 | C1740000325 | 454778.18 | 1363368.51 | 62983.91000000015 | 908590.3300000001 |
| 1.0 | CASH_IN | 1271.77 | C173738886 | 6973823.5 | 6975095.27 | C932583850 | 697456.73 | 2719172.89 | 1271.769999999553 | 2021716.1600000001 |
| 1.0 | CASH_IN | 246816.65 | C1262237002 | 6975095.27 | 7221911.92 | C33524623 | 933112.37 | 1517262.16 | 246816.65000000037 | 584149.7899999999 |
| 1.0 | CASH_IN | 21898.97 | C2029754983 | 7221911.92 | 7243810.89 | C747464370 | 1114670.7 | 1567434.81 | 21898.96999999974 | 452764.1100000001 |
| 1.0 | CASH_IN | 30811.56 | C1946111918 | 7243810.89 | 7274622.45 | C22805895 | 152178.0 | 651524.92 | 30811.56000000052 | 499346.92000000004 |
| 1.0 | CASH_IN | 100275.09 | C564817260 | 7274622.45 | 7374897.54 | C564160838 | 878425.58 | 1254956.07 | 100275.08999999985 | 376530.4900000001 |
| 1.0 | CASH_IN | 46630.53 | C194415222 | 7374897.54 | 7421528.07 | C2083562754 | 604846.71 | 1186556.81 | 46630.53000000026 | 581710.1000000001 |
| 1.0 | CASH_IN | 158775.58 | C798622145 | 7421528.07 | 7580303.64 | C1023714065 | 688899.05 | 1412484.09 | 158775.56999999937 | 723585.04 |
| 1.0 | CASH_IN | 90556.08 | C638695843 | 7580303.64 | 7670859.72 | C288665596 | 186372.86 | 8496.61 | 90556.08000000007 | -177876.25 |
| 1.0 | CASH_IN | 173948.89 | C1411543296 | 7670859.72 | 7844808.61 | C1740000325 | 391794.27 | 1363368.51 | 173948.8900000006 | 971574.24 |
| 1.0 | CASH_IN | 66216.71 | C1800967368 | 7844808.61 | 7911025.32 | C97730845 | 117785.88 | 9940339.29 | 66216.70999999996 | 9822553.409999998 |
| 1.0 | CASH_IN | 216133.99 | C338887787 | 7911025.32 | 8127159.31 | C747464370 | 1092771.73 | 1567434.81 | 216133.9899999993 | 474663.0800000001 |
| 1.0 | CASH_IN | 371883.82 | C1061448687 | 8127159.31 | 8499043.13 | C1971489295 | 880022.94 | 0.0 | 371883.82000000123 | -880022.94 |
| 1.0 | CASH_IN | 770537.37 | C2015999862 | 8499043.13 | 8499043.13 | C1883840933 | 8499043.13 | 1.687464309e7 | 0.0 | 8375599.959999999 |
| 1.0 | CASH_IN | 71275.47 | C1467515503 | 8499043.13 | 8570318.6 | C20671747 | 136084.66 | 43691.09 | 71275.46999999881 | -92393.57 |
| 1.0 | CASH_IN | 336298.77 | C1533330615 | 8570318.6 | 8906617.38 | C1789550256 | 1049811.54 | 4619798.56 | 336298.7800000012 | 3569987.0199999996 |
| 1.0 | CASH_IN | 317393.38 | C1197721383 | 8906617.38 | 9224010.75 | C1286084959 | 1175483.65 | 2107778.11 | 317393.3699999992 | 932294.46 |
| 1.0 | CASH_IN | 181002.01 | C1917082298 | 9224010.75 | 9405012.76 | C1749186397 | 292975.96 | 277515.05 | 181002.00999999978 | -15460.910000000033 |
| 1.0 | CASH_IN | 311449.38 | C1639765351 | 9405012.76 | 9716462.14 | C1789550256 | 713512.77 | 4619798.56 | 311449.3800000008 | 3906285.7899999996 |
| 1.0 | CASH_IN | 270824.42 | C478209179 | 9716462.14 | 9987286.56 | C1816757085 | 8515810.04 | 1.068123879e7 | 270824.4199999999 | 2165428.75 |
| 1.0 | PAYMENT | 1449.03 | C1166230227 | 80983.0 | 79533.97 | M365056339 | 0.0 | 0.0 | -1449.0299999999988 | 0.0 |
| 1.0 | PAYMENT | 3551.48 | C307411297 | 1015017.79 | 1011466.31 | M166536076 | 0.0 | 0.0 | -3551.4799999999814 | 0.0 |
| 1.0 | CASH_OUT | 227768.63 | C1445424568 | 1011466.31 | 783697.68 | C1023714065 | 530123.48 | 1412484.09 | -227768.63 | 882360.6100000001 |
| 1.0 | CASH_OUT | 172986.7 | C1374217958 | 783697.68 | 610710.98 | C33524623 | 686295.71 | 1517262.16 | -172986.70000000007 | 830966.45 |
| 1.0 | PAYMENT | 4940.2 | C1459016715 | 48005.0 | 43064.8 | M912747546 | 0.0 | 0.0 | -4940.199999999997 | 0.0 |
| 1.0 | PAYMENT | 2755.96 | C1530957251 | 20987.0 | 18231.04 | M1292472219 | 0.0 | 0.0 | -2755.959999999999 | 0.0 |
| 1.0 | DEBIT | 8807.01 | C767511741 | 139428.0 | 130620.99 | C20671747 | 64809.18 | 43691.09 | -8807.009999999995 | -21118.090000000004 |
| 1.0 | DEBIT | 3111.86 | C548795052 | 64665.82 | 61553.96 | C240650537 | 10070.22 | 0.0 | -3111.8600000000006 | -10070.22 |
| 1.0 | PAYMENT | 10040.52 | C2143571436 | 61553.96 | 51513.44 | M2117099736 | 0.0 | 0.0 | -10040.519999999997 | 0.0 |
| 1.0 | DEBIT | 5363.0 | C691863815 | 21260.0 | 15897.0 | C1321640594 | 24765.26 | 18910.85 | -5363.0 | -5854.41 |
| 1.0 | PAYMENT | 11990.19 | C647149086 | 885.0 | 0.0 | M2070160397 | 0.0 | 0.0 | -885.0 | 0.0 |
| 1.0 | PAYMENT | 266.88 | C103787801 | 4182.0 | 3915.12 | M1205580258 | 0.0 | 0.0 | -266.8800000000001 | 0.0 |
| 1.0 | PAYMENT | 1928.55 | C1959451969 | 5096.0 | 3167.45 | M777313177 | 0.0 | 0.0 | -1928.5500000000002 | 0.0 |
| 1.0 | PAYMENT | 10648.06 | C1441328175 | 37419.0 | 26770.94 | M1327895505 | 0.0 | 0.0 | -10648.060000000001 | 0.0 |
| 1.0 | PAYMENT | 962.43 | C1831141281 | 26770.94 | 25808.5 | M1705277839 | 0.0 | 0.0 | -962.4399999999987 | 0.0 |
| 1.0 | PAYMENT | 8348.1 | C2000648320 | 16583.0 | 8234.9 | M142099757 | 0.0 | 0.0 | -8348.1 | 0.0 |
| 1.0 | PAYMENT | 6969.67 | C1114335860 | 61091.0 | 54121.33 | M80026551 | 0.0 | 0.0 | -6969.669999999998 | 0.0 |
| 1.0 | TRANSFER | 20128.0 | C137533655 | 20128.0 | 0.0 | C1848415041 | 0.0 | 0.0 | -20128.0 | 0.0 |
| 1.0 | CASH_OUT | 20128.0 | C1118430673 | 20128.0 | 0.0 | C339924917 | 6268.0 | 12145.85 | -20128.0 | 5877.85 |
| 1.0 | PAYMENT | 154.87 | C1527254842 | 9339.0 | 9184.13 | M2000469839 | 0.0 | 0.0 | -154.8700000000008 | 0.0 |
| 1.0 | DEBIT | 4040.84 | C1800693087 | 1938.0 | 0.0 | C1825027294 | 51339.0 | 36757.68 | -1938.0 | -14581.32 |
| 1.0 | PAYMENT | 6490.17 | C1717473929 | 548504.0 | 542013.83 | M2100572327 | 0.0 | 0.0 | -6490.170000000042 | 0.0 |
| 1.0 | PAYMENT | 1101.09 | C1868578441 | 30858.0 | 29756.91 | M659996839 | 0.0 | 0.0 | -1101.0900000000001 | 0.0 |
| 1.0 | DEBIT | 4347.78 | C890160158 | 20766.0 | 16418.22 | C1531333864 | 407959.76 | 55974.56 | -4347.779999999999 | -351985.2 |
| 1.0 | CASH_OUT | 213922.13 | C1768127248 | 16418.22 | 0.0 | C1234776885 | 431956.63 | 2025098.66 | -16418.22 | 1593142.0299999998 |
| 1.0 | CASH_OUT | 83859.59 | C119112899 | 0.0 | 0.0 | C1590550415 | 1.638754836e7 | 1.916920493e7 | 0.0 | 2781656.5700000003 |
| 1.0 | CASH_OUT | 33269.25 | C375074687 | 0.0 | 0.0 | C1629911510 | 111389.13 | 15375.37 | 0.0 | -96013.76000000001 |
| 1.0 | CASH_OUT | 51749.94 | C1429616542 | 0.0 | 0.0 | C297927961 | 62470.0 | 132842.64 | 0.0 | 70372.64000000001 |
| 1.0 | CASH_OUT | 86410.3 | C662666707 | 0.0 | 0.0 | C1023714065 | 757892.11 | 1412484.09 | 0.0 | 654591.9800000001 |
| 1.0 | CASH_OUT | 10856.63 | C1821100643 | 0.0 | 0.0 | C1740000325 | 217845.38 | 1363368.51 | 0.0 | 1145523.13 |
| 1.0 | CASH_OUT | 161613.93 | C593768538 | 0.0 | 0.0 | C1789550256 | 402063.39 | 4619798.56 | 0.0 | 4217735.17 |
| 1.0 | CASH_OUT | 244361.81 | C1191864687 | 0.0 | 0.0 | C33524623 | 859282.41 | 1517262.16 | 0.0 | 657979.7499999999 |
| 1.0 | CASH_OUT | 596617.87 | C466032056 | 0.0 | 0.0 | C1234776885 | 645878.76 | 2025098.66 | 0.0 | 1379219.9 |
| 1.0 | CASH_OUT | 108600.78 | C690822257 | 0.0 | 0.0 | C1810132623 | 146488.24 | 97128.19 | 0.0 | -49360.04999999999 |
| 1.0 | CASH_OUT | 377520.94 | C726212590 | 0.0 | 0.0 | C1789550256 | 563677.32 | 4619798.56 | 0.0 | 4056121.2399999998 |
| 1.0 | CASH_OUT | 60758.42 | C1058822905 | 0.0 | 0.0 | C1318822808 | 155911.68 | 500631.71 | 0.0 | 344720.03 |
| 1.0 | CASH_OUT | 86204.31 | C475394679 | 0.0 | 0.0 | C1789550256 | 941198.26 | 4619798.56 | 0.0 | 3678600.3 |
| 1.0 | CASH_OUT | 1234.0 | C1574615832 | 0.0 | 0.0 | C1983025922 | 29906.0 | 7550.03 | 0.0 | -22355.97 |
| 1.0 | CASH_OUT | 18288.91 | C1049590050 | 0.0 | 0.0 | C1286084959 | 858090.28 | 2107778.11 | 0.0 | 1249687.8299999998 |
| 1.0 | CASH_OUT | 248979.22 | C1662592920 | 0.0 | 0.0 | C33524623 | 1103644.22 | 1517262.16 | 0.0 | 413617.93999999994 |
| 1.0 | CASH_OUT | 126774.5 | C1357686726 | 0.0 | 0.0 | C1234776885 | 1242496.63 | 2025098.66 | 0.0 | 782602.03 |
| 1.0 | CASH_OUT | 38428.19 | C1491522744 | 0.0 | 0.0 | C1568059495 | 43524.89 | 122750.49 | 0.0 | 79225.6 |
| 1.0 | CASH_OUT | 109732.52 | C501608687 | 0.0 | 0.0 | C1816757085 | 8244985.62 | 1.068123879e7 | 0.0 | 2436253.169999999 |
| 1.0 | CASH_OUT | 17851.54 | C929706284 | 0.0 | 0.0 | C824208363 | 32353.0 | 0.0 | 0.0 | -32353.0 |
| 1.0 | CASH_OUT | 5223.97 | C42820240 | 0.0 | 0.0 | C1122805102 | 10429.69 | 23568.91 | 0.0 | 13139.22 |
| 1.0 | CASH_OUT | 63807.17 | C1822434669 | 0.0 | 0.0 | C2083562754 | 558216.18 | 1186556.81 | 0.0 | 628340.63 |
| 1.0 | CASH_OUT | 406297.68 | C1592840862 | 0.0 | 0.0 | C985934102 | 882606.73 | 971418.91 | 0.0 | 88812.18000000005 |
| 1.0 | PAYMENT | 4779.93 | C955611965 | 121.0 | 0.0 | M1367672657 | 0.0 | 0.0 | -121.0 | 0.0 |
| 1.0 | TRANSFER | 16631.14 | C1021565761 | 0.0 | 0.0 | C998351292 | 89102.55 | 1015132.48 | 0.0 | 926029.9299999999 |
| 1.0 | PAYMENT | 2771.16 | C1692375649 | 60721.0 | 57949.84 | M2002790740 | 0.0 | 0.0 | -2771.1600000000035 | 0.0 |
| 1.0 | PAYMENT | 3154.06 | C237373286 | 24239.0 | 21084.94 | M742255664 | 0.0 | 0.0 | -3154.0600000000013 | 0.0 |
| 1.0 | PAYMENT | 7813.63 | C883521348 | 21084.94 | 13271.31 | M1115013644 | 0.0 | 0.0 | -7813.629999999999 | 0.0 |
| 1.0 | PAYMENT | 360.13 | C2086068243 | 38939.0 | 38578.87 | M1070194629 | 0.0 | 0.0 | -360.1299999999974 | 0.0 |
| 1.0 | CASH_OUT | 431381.17 | C1076202543 | 40481.0 | 0.0 | C306206744 | 55566.0 | 3554299.27 | -40481.0 | 3498733.27 |
| 1.0 | TRANSFER | 43670.84 | C749486981 | 0.0 | 0.0 | C885951223 | 79331.08 | 215851.28 | 0.0 | 136520.2 |
| 1.0 | PAYMENT | 7342.63 | C1731295355 | 19443.0 | 12100.37 | M385332399 | 0.0 | 0.0 | -7342.629999999999 | 0.0 |
| 1.0 | PAYMENT | 7417.09 | C2061440682 | 843.0 | 0.0 | M883020319 | 0.0 | 0.0 | -843.0 | 0.0 |
| 1.0 | PAYMENT | 8865.26 | C1488946768 | 84415.83 | 75550.57 | M1942356772 | 0.0 | 0.0 | -8865.259999999995 | 0.0 |
| 1.0 | CASH_OUT | 213490.87 | C1907241392 | 75550.57 | 0.0 | C1971489295 | 508139.13 | 0.0 | -75550.57 | -508139.13 |
| 1.0 | PAYMENT | 7444.4 | C1269118128 | 31902.57 | 24458.17 | M1863100050 | 0.0 | 0.0 | -7444.4000000000015 | 0.0 |
| 1.0 | CASH_OUT | 287265.38 | C739264372 | 24458.17 | 0.0 | C306206744 | 486947.17 | 3554299.27 | -24458.17 | 3067352.1 |
| 1.0 | CASH_OUT | 416001.33 | C749981943 | 0.0 | 0.0 | C667346055 | 102.0 | 9291619.62 | 0.0 | 9291517.62 |
| 1.0 | PAYMENT | 6432.26 | C1313960293 | 0.0 | 0.0 | M1155757579 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 8690.86 | C1785111044 | 29772.0 | 21081.14 | M1482748887 | 0.0 | 0.0 | -8690.86 | 0.0 |
| 1.0 | PAYMENT | 3791.28 | C494894055 | 32463.0 | 28671.72 | M1123226989 | 0.0 | 0.0 | -3791.279999999999 | 0.0 |
| 1.0 | PAYMENT | 4111.34 | C500885941 | 128052.0 | 123940.66 | M1115381650 | 0.0 | 0.0 | -4111.3399999999965 | 0.0 |
| 1.0 | PAYMENT | 7163.9 | C616412281 | 114219.94 | 107056.04 | M2118096382 | 0.0 | 0.0 | -7163.900000000009 | 0.0 |
| 1.0 | PAYMENT | 6557.51 | C878861517 | 107056.04 | 100498.52 | M746394140 | 0.0 | 0.0 | -6557.5199999999895 | 0.0 |
| 1.0 | PAYMENT | 17348.25 | C1705665942 | 100498.52 | 83150.27 | M1731763384 | 0.0 | 0.0 | -17348.25 | 0.0 |
| 1.0 | PAYMENT | 1586.28 | C1100619942 | 83150.27 | 81564.0 | M87242619 | 0.0 | 0.0 | -1586.270000000004 | 0.0 |
| 1.0 | PAYMENT | 6811.97 | C864248990 | 81564.0 | 74752.02 | M285074186 | 0.0 | 0.0 | -6811.979999999996 | 0.0 |
| 1.0 | PAYMENT | 36651.07 | C15892131 | 74752.02 | 38100.95 | M484841769 | 0.0 | 0.0 | -36651.07000000001 | 0.0 |
| 1.0 | PAYMENT | 4804.68 | C879311295 | 38100.95 | 33296.27 | M1274247563 | 0.0 | 0.0 | -4804.68 | 0.0 |
| 1.0 | PAYMENT | 19375.5 | C1483145520 | 33296.27 | 13920.76 | M1961129028 | 0.0 | 0.0 | -19375.509999999995 | 0.0 |
| 1.0 | PAYMENT | 13833.75 | C163385254 | 13920.76 | 87.01 | M1506938939 | 0.0 | 0.0 | -13833.75 | 0.0 |
| 1.0 | PAYMENT | 21008.52 | C970781872 | 87.01 | 0.0 | M1850597787 | 0.0 | 0.0 | -87.01 | 0.0 |
| 1.0 | PAYMENT | 22043.92 | C258737099 | 0.0 | 0.0 | M1190566357 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 32635.14 | C407997647 | 0.0 | 0.0 | M428996455 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 11438.39 | C1988939205 | 0.0 | 0.0 | M80141040 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 13527.16 | C275056979 | 0.0 | 0.0 | M106557175 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 11881.78 | C371976476 | 0.0 | 0.0 | M612937843 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 9805.46 | C480184864 | 0.0 | 0.0 | M1433956626 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 9470.85 | C483525032 | 0.0 | 0.0 | M2109219177 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 20560.86 | C429058804 | 0.0 | 0.0 | M259319861 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 13679.84 | C52913970 | 0.0 | 0.0 | M1059634518 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 15448.0 | C836969741 | 0.0 | 0.0 | M436094532 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 1987.4 | C567852222 | 0.0 | 0.0 | M1541433310 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 4145.04 | C1938025186 | 97168.0 | 93022.96 | M405036720 | 0.0 | 0.0 | -4145.039999999994 | 0.0 |
| 1.0 | PAYMENT | 3511.45 | C2088582214 | 11310.0 | 7798.55 | M620456576 | 0.0 | 0.0 | -3511.45 | 0.0 |
| 1.0 | PAYMENT | 1118.1 | C1220759559 | 9041.0 | 7922.9 | M1923634801 | 0.0 | 0.0 | -1118.1000000000004 | 0.0 |
| 1.0 | PAYMENT | 15.06 | C1730337646 | 204682.0 | 204666.94 | M418513504 | 0.0 | 0.0 | -15.059999999997672 | 0.0 |
| 1.0 | CASH_IN | 352568.35 | C1256405521 | 9945.0 | 362513.35 | C665576141 | 2624106.46 | 5515763.34 | 352568.35 | 2891656.88 |
| 1.0 | PAYMENT | 3043.27 | C2051598050 | 36369.0 | 33325.73 | M188791662 | 0.0 | 0.0 | -3043.269999999997 | 0.0 |
| 1.0 | PAYMENT | 9758.09 | C61426020 | 21217.0 | 11458.91 | M1347016882 | 0.0 | 0.0 | -9758.09 | 0.0 |
| 1.0 | PAYMENT | 5924.89 | C1829460911 | 1518.0 | 0.0 | M1662912171 | 0.0 | 0.0 | -1518.0 | 0.0 |
| 1.0 | PAYMENT | 9550.43 | C708153797 | 4021.0 | 0.0 | M883380345 | 0.0 | 0.0 | -4021.0 | 0.0 |
| 1.0 | PAYMENT | 4355.66 | C54960993 | 120.0 | 0.0 | M276748028 | 0.0 | 0.0 | -120.0 | 0.0 |
| 1.0 | DEBIT | 6973.12 | C373544591 | 50015.0 | 43041.88 | C1568059495 | 81953.08 | 122750.49 | -6973.120000000003 | 40797.41 |
| 1.0 | PAYMENT | 2358.63 | C409565350 | 70824.0 | 68465.37 | M771923297 | 0.0 | 0.0 | -2358.6300000000047 | 0.0 |
| 1.0 | DEBIT | 374.36 | C1210939243 | 15237.0 | 14862.64 | C1335050193 | 62904.98 | 353532.56 | -374.3600000000006 | 290627.58 |
| 1.0 | PAYMENT | 8946.63 | C1079125839 | 95066.0 | 86119.37 | M321772459 | 0.0 | 0.0 | -8946.630000000005 | 0.0 |
| 1.0 | PAYMENT | 20573.4 | C696165690 | 24641.0 | 4067.6 | M181078353 | 0.0 | 0.0 | -20573.4 | 0.0 |
| 1.0 | PAYMENT | 8358.12 | C1542598424 | 293.0 | 0.0 | M645875534 | 0.0 | 0.0 | -293.0 | 0.0 |
| 1.0 | PAYMENT | 1006.76 | C2054451662 | 696008.0 | 695001.24 | M673672784 | 0.0 | 0.0 | -1006.7600000000093 | 0.0 |
| 1.0 | PAYMENT | 10498.05 | C1659309731 | 16730.0 | 6231.95 | M1056862342 | 0.0 | 0.0 | -10498.05 | 0.0 |
| 1.0 | TRANSFER | 463722.76 | C1734184086 | 349.0 | 0.0 | C832279283 | 18285.39 | 185389.8 | -349.0 | 167104.40999999997 |
| 1.0 | TRANSFER | 138857.19 | C1947941827 | 109473.0 | 0.0 | C1789550256 | 1027402.57 | 4619798.56 | -109473.0 | 3592395.9899999998 |
| 1.0 | PAYMENT | 5184.9 | C785625589 | 283509.0 | 278324.1 | M1667534569 | 0.0 | 0.0 | -5184.900000000023 | 0.0 |
| 1.0 | PAYMENT | 3626.96 | C1758893871 | 23506.0 | 19879.04 | M1170111701 | 0.0 | 0.0 | -3626.959999999999 | 0.0 |
| 1.0 | PAYMENT | 282.42 | C1985028494 | 30603.0 | 30320.58 | M830863979 | 0.0 | 0.0 | -282.41999999999825 | 0.0 |
| 1.0 | DEBIT | 8703.54 | C1832283291 | 137782.0 | 129078.46 | C1335050193 | 63279.34 | 353532.56 | -8703.539999999994 | 290253.22 |
| 1.0 | PAYMENT | 3673.1 | C1532554556 | 30507.0 | 26833.9 | M620423816 | 0.0 | 0.0 | -3673.0999999999985 | 0.0 |
| 1.0 | PAYMENT | 9800.58 | C976358110 | 12617.16 | 2816.58 | M1690233783 | 0.0 | 0.0 | -9800.58 | 0.0 |
| 1.0 | CASH_OUT | 88987.11 | C2014563089 | 2816.58 | 0.0 | C932583850 | 696184.96 | 2719172.89 | -2816.58 | 2022987.9300000002 |
| 1.0 | PAYMENT | 4227.19 | C198098993 | 36054.0 | 31826.81 | M553672556 | 0.0 | 0.0 | -4227.189999999999 | 0.0 |
| 1.0 | PAYMENT | 2881.51 | C854653864 | 90250.0 | 87368.49 | M1415439780 | 0.0 | 0.0 | -2881.5099999999948 | 0.0 |
| 1.0 | PAYMENT | 4376.46 | C703555670 | 9809.0 | 5432.54 | M179808568 | 0.0 | 0.0 | -4376.46 | 0.0 |
| 1.0 | PAYMENT | 11656.12 | C1068445309 | 10181.0 | 0.0 | M73698537 | 0.0 | 0.0 | -10181.0 | 0.0 |
| 1.0 | TRANSFER | 345698.98 | C1453062635 | 40419.0 | 0.0 | C997608398 | 20489.94 | 157982.12 | -40419.0 | 137492.18 |
| 1.0 | PAYMENT | 3292.21 | C1632789609 | 20484.0 | 17191.79 | M659059448 | 0.0 | 0.0 | -3292.209999999999 | 0.0 |
| 1.0 | PAYMENT | 7367.06 | C124494140 | 14528.0 | 7160.94 | M246826139 | 0.0 | 0.0 | -7367.06 | 0.0 |
| 1.0 | PAYMENT | 2937.34 | C2083854344 | 19703.0 | 16765.66 | M403598020 | 0.0 | 0.0 | -2937.34 | 0.0 |
| 1.0 | DEBIT | 1150.14 | C1119242936 | 21634.0 | 20483.86 | C1122805102 | 15653.66 | 23568.91 | -1150.1399999999994 | 7915.25 |
| 1.0 | PAYMENT | 3563.36 | C2123914473 | 185397.0 | 181833.64 | M708443754 | 0.0 | 0.0 | -3563.359999999986 | 0.0 |
| 1.0 | PAYMENT | 2380.46 | C1091234211 | 101428.0 | 99047.54 | M832145584 | 0.0 | 0.0 | -2380.4600000000064 | 0.0 |
| 1.0 | PAYMENT | 2266.02 | C224060798 | 64757.92 | 62491.91 | M1752535057 | 0.0 | 0.0 | -2266.0099999999948 | 0.0 |
| 1.0 | TRANSFER | 32132.45 | C2116299597 | 62491.91 | 30359.46 | C1466073198 | 59605.0 | 0.0 | -32132.450000000004 | -59605.0 |
| 1.0 | TRANSFER | 51627.39 | C50720817 | 30359.46 | 0.0 | C1526298704 | 81243.0 | 32092.07 | -30359.46 | -49150.93 |
| 1.0 | TRANSFER | 62073.54 | C1496220730 | 0.0 | 0.0 | C1740000325 | 228702.01 | 1363368.51 | 0.0 | 1134666.5 |
| 1.0 | TRANSFER | 13614.91 | C488600086 | 0.0 | 0.0 | C380242442 | 30195.0 | 0.0 | 0.0 | -30195.0 |
| 1.0 | TRANSFER | 2290.73 | C1111379131 | 0.0 | 0.0 | C660143728 | 79471.66 | 42450.71 | 0.0 | -37020.950000000004 |
| 1.0 | TRANSFER | 637161.0 | C1846982837 | 0.0 | 0.0 | C1789550256 | 1166259.76 | 4619798.56 | 0.0 | 3453538.8 |
| 1.0 | TRANSFER | 639378.37 | C1135935001 | 0.0 | 0.0 | C392292416 | 753824.15 | 3420103.09 | 0.0 | 2666278.94 |
| 1.0 | TRANSFER | 34390.85 | C635288507 | 0.0 | 0.0 | C1291286504 | 90214.97 | 66575.5 | 0.0 | -23639.47 |
| 1.0 | TRANSFER | 1867849.02 | C355885103 | 0.0 | 0.0 | C665576141 | 2271538.11 | 5515763.34 | 0.0 | 3244225.23 |
| 1.0 | TRANSFER | 1193410.46 | C1321115948 | 0.0 | 0.0 | C1590550415 | 1.647140795e7 | 1.916920493e7 | 0.0 | 2697796.9800000004 |
| 1.0 | TRANSFER | 348186.2 | C706665172 | 0.0 | 0.0 | C306206744 | 774212.55 | 3554299.27 | 0.0 | 2780086.7199999997 |
| 1.0 | TRANSFER | 1440296.14 | C1649847375 | 0.0 | 0.0 | C1590550415 | 1.76648184e7 | 1.916920493e7 | 0.0 | 1504386.5300000012 |
| 1.0 | TRANSFER | 799483.57 | C1153933106 | 0.0 | 0.0 | C306206744 | 1122398.75 | 3554299.27 | 0.0 | 2431900.52 |
| 1.0 | TRANSFER | 969631.31 | C2144067911 | 0.0 | 0.0 | C248609774 | 2134967.54 | 6453430.91 | 0.0 | 4318463.37 |
| 1.0 | TRANSFER | 483544.3 | C593447952 | 0.0 | 0.0 | C1286084959 | 876379.19 | 2107778.11 | 0.0 | 1231398.92 |
| 1.0 | TRANSFER | 326349.91 | C1950136544 | 0.0 | 0.0 | C564160838 | 778150.49 | 1254956.07 | 0.0 | 476805.5800000001 |
| 1.0 | DEBIT | 7546.88 | C209360730 | 51368.0 | 43821.12 | C1259769769 | 11353.0 | 252055.24 | -7546.879999999997 | 240702.24 |
| 1.0 | PAYMENT | 2469.75 | C740007499 | 43821.12 | 41351.37 | M1971152916 | 0.0 | 0.0 | -2469.75 | 0.0 |
| 1.0 | DEBIT | 1741.24 | C1687627235 | 80942.0 | 79200.76 | C997608398 | 366188.92 | 157982.12 | -1741.2400000000052 | -208206.8 |
| 1.0 | PAYMENT | 553.25 | C1007660652 | 7522.0 | 6968.75 | M1276544608 | 0.0 | 0.0 | -553.25 | 0.0 |
| 1.0 | PAYMENT | 6606.31 | C1387620926 | 6968.75 | 362.44 | M265348534 | 0.0 | 0.0 | -6606.31 | 0.0 |
| 1.0 | PAYMENT | 2075.94 | C1324173038 | 110403.0 | 108327.06 | M917568725 | 0.0 | 0.0 | -2075.9400000000023 | 0.0 |
| 1.0 | TRANSFER | 84212.94 | C1336775847 | 53199.0 | 0.0 | C13329486 | 0.0 | 84212.94 | -53199.0 | 84212.94 |
| 1.0 | CASH_OUT | 312108.51 | C1196963249 | 0.0 | 0.0 | C1262822392 | 8043910.53 | 1.249436715e7 | 0.0 | 4450456.62 |
| 1.0 | PAYMENT | 1647.93 | C1663488386 | 70889.0 | 69241.07 | M1380044647 | 0.0 | 0.0 | -1647.929999999993 | 0.0 |
| 1.0 | PAYMENT | 3533.32 | C1902801188 | 38211.0 | 34677.68 | M1998137093 | 0.0 | 0.0 | -3533.3199999999997 | 0.0 |
| 1.0 | PAYMENT | 7659.86 | C577127077 | 544.0 | 0.0 | M2122357625 | 0.0 | 0.0 | -544.0 | 0.0 |
| 1.0 | PAYMENT | 7203.07 | C1556162268 | 290775.55 | 283572.48 | M1650332494 | 0.0 | 0.0 | -7203.070000000007 | 0.0 |
| 1.0 | CASH_OUT | 116206.75 | C1924990666 | 283572.48 | 167365.73 | C1318822808 | 216670.1 | 500631.71 | -116206.74999999997 | 283961.61 |
| 1.0 | CASH_OUT | 30331.38 | C144445623 | 167365.73 | 137034.36 | C998351292 | 105733.69 | 1015132.48 | -30331.370000000024 | 909398.79 |
| 1.0 | PAYMENT | 2063.77 | C1572745406 | 16064.0 | 14000.23 | M1340644388 | 0.0 | 0.0 | -2063.7700000000004 | 0.0 |
| 1.0 | PAYMENT | 3035.35 | C1434015813 | 26657.0 | 23621.65 | M1023996917 | 0.0 | 0.0 | -3035.3499999999985 | 0.0 |
| 1.0 | CASH_OUT | 123797.1 | C295640874 | 23621.65 | 0.0 | C557041912 | 153674.01 | 557537.26 | -23621.65 | 403863.25 |
| 1.0 | DEBIT | 8233.25 | C438151297 | 7024.0 | 0.0 | C846947180 | 14673.0 | 12901.08 | -7024.0 | -1771.92 |
| 1.0 | PAYMENT | 2271.34 | C96039159 | 31346.0 | 29074.66 | M2144644334 | 0.0 | 0.0 | -2271.34 | 0.0 |
| 1.0 | PAYMENT | 3801.74 | C375097969 | 73616.2 | 69814.46 | M1635082651 | 0.0 | 0.0 | -3801.7399999999907 | 0.0 |
| 1.0 | PAYMENT | 1530.83 | C788892554 | 69814.46 | 68283.63 | M1970073944 | 0.0 | 0.0 | -1530.8300000000017 | 0.0 |
| 1.0 | PAYMENT | 4833.96 | C1060042118 | 41005.0 | 36171.04 | M1964847681 | 0.0 | 0.0 | -4833.959999999999 | 0.0 |
| 1.0 | PAYMENT | 5842.61 | C1992801971 | 40.0 | 0.0 | M1681094402 | 0.0 | 0.0 | -40.0 | 0.0 |
| 1.0 | PAYMENT | 2871.89 | C754527431 | 653.0 | 0.0 | M820677667 | 0.0 | 0.0 | -653.0 | 0.0 |
| 1.0 | DEBIT | 5297.36 | C2064883371 | 82134.0 | 76836.64 | C655381473 | 31443.78 | 189534.74 | -5297.360000000001 | 158090.96 |
| 1.0 | TRANSFER | 621561.65 | C82275756 | 123122.0 | 0.0 | C1782113663 | 0.0 | 3997768.55 | -123122.0 | 3997768.55 |
| 1.0 | PAYMENT | 829.07 | C1629353699 | 11199.0 | 10369.93 | M99808631 | 0.0 | 0.0 | -829.0699999999997 | 0.0 |
| 1.0 | PAYMENT | 3418.07 | C1651754404 | 260.0 | 0.0 | M1564892747 | 0.0 | 0.0 | -260.0 | 0.0 |
| 1.0 | CASH_OUT | 41924.75 | C1407324654 | 0.0 | 0.0 | C1526298704 | 132870.39 | 32092.07 | 0.0 | -100778.32 |
| 1.0 | CASH_OUT | 173597.57 | C1101598632 | 0.0 | 0.0 | C248609774 | 3104598.86 | 6453430.91 | 0.0 | 3348832.0500000003 |
| 1.0 | CASH_OUT | 77642.84 | C1032568028 | 0.0 | 0.0 | C667346055 | 416103.33 | 9291619.62 | 0.0 | 8875516.29 |
| 1.0 | CASH_OUT | 144964.23 | C1380976928 | 0.0 | 0.0 | C932583850 | 785172.07 | 2719172.89 | 0.0 | 1934000.8200000003 |
| 1.0 | CASH_OUT | 94038.9 | C293474277 | 0.0 | 0.0 | C998351292 | 136065.07 | 1015132.48 | 0.0 | 879067.4099999999 |
| 1.0 | CASH_OUT | 26051.33 | C413373997 | 0.0 | 0.0 | C215145189 | 28479.11 | 1891.79 | 0.0 | -26587.32 |
| 1.0 | CASH_OUT | 134143.27 | C2024711353 | 0.0 | 0.0 | C451111351 | 1754894.42 | 3940085.21 | 0.0 | 2185190.79 |
| 1.0 | CASH_OUT | 497424.19 | C711310213 | 0.0 | 0.0 | C932583850 | 930136.3 | 2719172.89 | 0.0 | 1789036.59 |
| 1.0 | CASH_OUT | 607616.73 | C1267042315 | 0.0 | 0.0 | C1286084959 | 1359923.49 | 2107778.11 | 0.0 | 747854.6199999999 |
| 1.0 | CASH_OUT | 681093.57 | C1335061928 | 0.0 | 0.0 | C1023714065 | 844302.4 | 1412484.09 | 0.0 | 568181.6900000001 |
| 1.0 | CASH_OUT | 114057.61 | C1108706191 | 0.0 | 0.0 | C932583850 | 1427560.49 | 2719172.89 | 0.0 | 1291612.4000000001 |
| 1.0 | CASH_OUT | 205279.62 | C1160487387 | 0.0 | 0.0 | C932583850 | 1541618.1 | 2719172.89 | 0.0 | 1177554.79 |
| 1.0 | CASH_OUT | 173512.93 | C579447973 | 0.0 | 0.0 | C1816757085 | 8354718.14 | 1.068123879e7 | 0.0 | 2326520.6499999994 |
| 1.0 | CASH_OUT | 166817.98 | C1990421361 | 0.0 | 0.0 | C1971489295 | 721630.0 | 0.0 | 0.0 | -721630.0 |
| 1.0 | CASH_OUT | 113283.13 | C407148497 | 0.0 | 0.0 | C885951223 | 123001.91 | 215851.28 | 0.0 | 92849.37 |
| 1.0 | CASH_OUT | 179030.05 | C16373883 | 0.0 | 0.0 | C1360767589 | 1357232.47 | 2107965.39 | 0.0 | 750732.9200000002 |
| 1.0 | CASH_OUT | 94332.0 | C1878700101 | 0.0 | 0.0 | C1782113663 | 621561.65 | 3997768.55 | 0.0 | 3376206.9 |
| 1.0 | CASH_OUT | 141111.15 | C40875560 | 0.0 | 0.0 | C2083562754 | 622023.34 | 1186556.81 | 0.0 | 564533.4700000001 |
| 1.0 | CASH_OUT | 45756.23 | C1042891691 | 0.0 | 0.0 | C392292416 | 1393202.52 | 3420103.09 | 0.0 | 2026900.5699999998 |
| 1.0 | CASH_OUT | 6039.61 | C1893563925 | 0.0 | 0.0 | C1653844940 | 31164.0 | 37203.61 | 0.0 | 6039.610000000001 |
| 1.0 | CASH_OUT | 46261.02 | C1251967187 | 0.0 | 0.0 | C575335780 | 85758.95 | 52415.15 | 0.0 | -33343.799999999996 |
| 1.0 | CASH_OUT | 312471.16 | C901689694 | 0.0 | 0.0 | C1789550256 | 1803420.75 | 4619798.56 | 0.0 | 2816377.8099999996 |
| 1.0 | CASH_OUT | 195490.04 | C1107204185 | 0.0 | 0.0 | C564160838 | 1104500.4 | 1254956.07 | 0.0 | 150455.67000000016 |
| 1.0 | CASH_OUT | 107308.84 | C1523084197 | 0.0 | 0.0 | C1291286504 | 124605.82 | 66575.5 | 0.0 | -58030.32000000001 |
| 1.0 | CASH_OUT | 48252.82 | C1818747191 | 0.0 | 0.0 | C451111351 | 1889037.69 | 3940085.21 | 0.0 | 2051047.52 |
| 1.0 | CASH_OUT | 25674.03 | C747870628 | 0.0 | 0.0 | C597190999 | 30563.0 | 6830.83 | 0.0 | -23732.17 |
| 1.0 | CASH_OUT | 63830.49 | C421191743 | 0.0 | 0.0 | C1531333864 | 412307.54 | 55974.56 | 0.0 | -356332.98 |
| 1.0 | CASH_OUT | 96178.8 | C9844218 | 0.0 | 0.0 | C1749186397 | 111973.96 | 277515.05 | 0.0 | 165541.08999999997 |
| 1.0 | CASH_OUT | 155122.86 | C1403716230 | 0.0 | 0.0 | C997608398 | 367930.17 | 157982.12 | 0.0 | -209948.05 |
| 1.0 | CASH_OUT | 171757.76 | C1673916398 | 0.0 | 0.0 | C2050019814 | 205811.8 | 0.0 | 0.0 | -205811.8 |
| 1.0 | CASH_OUT | 8809.04 | C756080817 | 0.0 | 0.0 | C1740000325 | 137034.36 | 1363368.51 | 0.0 | 1226334.15 |
| 1.0 | CASH_OUT | 27320.2 | C479734028 | 0.0 | 0.0 | C614685048 | 79191.11 | 0.0 | 0.0 | -79191.11 |
| 1.0 | CASH_OUT | 517915.91 | C388802347 | 0.0 | 0.0 | C1170794006 | 4404060.84 | 22190.99 | 0.0 | -4381869.85 |
| 1.0 | CASH_OUT | 34629.16 | C1541046463 | 0.0 | 0.0 | C1860513229 | 38735.74 | 0.0 | 0.0 | -38735.74 |
| 1.0 | CASH_OUT | 94296.25 | C1910896157 | 0.0 | 0.0 | C401424608 | 336692.95 | 1178808.14 | 0.0 | 842115.19 |
| 1.0 | CASH_OUT | 285360.06 | C753426788 | 0.0 | 0.0 | C1782113663 | 715893.66 | 3997768.55 | 0.0 | 3281874.8899999997 |
| 1.0 | CASH_OUT | 161445.91 | C1586470445 | 0.0 | 0.0 | C557041912 | 277471.11 | 557537.26 | 0.0 | 280066.15 |
| 1.0 | CASH_OUT | 386683.04 | C1373577787 | 0.0 | 0.0 | C33524623 | 1352623.44 | 1517262.16 | 0.0 | 164638.71999999997 |
| 1.0 | CASH_OUT | 169854.57 | C265577219 | 0.0 | 0.0 | C1816757085 | 8528231.07 | 1.068123879e7 | 0.0 | 2153007.719999999 |
| 1.0 | CASH_OUT | 126167.7 | C1624817884 | 0.0 | 0.0 | C248609774 | 3278196.43 | 6453430.91 | 0.0 | 3175234.48 |
| 1.0 | CASH_OUT | 152757.58 | C1107579932 | 0.0 | 0.0 | C1262822392 | 8356019.04 | 1.249436715e7 | 0.0 | 4138348.1100000003 |
| 1.0 | CASH_OUT | 49661.18 | C1784834205 | 0.0 | 0.0 | C451111351 | 1937290.51 | 3940085.21 | 0.0 | 2002794.7 |
| 1.0 | CASH_OUT | 73513.5 | C1174586025 | 0.0 | 0.0 | C1750905143 | 236639.0 | 424250.45 | 0.0 | 187611.45 |
| 1.0 | CASH_OUT | 166335.32 | C1627010197 | 0.0 | 0.0 | C33524623 | 1739306.48 | 1517262.16 | 0.0 | -222044.32000000007 |
| 1.0 | CASH_OUT | 234268.82 | C4073506 | 0.0 | 0.0 | C1750905143 | 310152.51 | 424250.45 | 0.0 | 114097.94 |
| 1.0 | CASH_OUT | 306280.12 | C2044825144 | 0.0 | 0.0 | C1360767589 | 1536262.52 | 2107965.39 | 0.0 | 571702.8700000001 |
| 1.0 | CASH_OUT | 194620.68 | C698747943 | 0.0 | 0.0 | C453211571 | 610710.98 | 3461666.05 | 0.0 | 2850955.07 |
| 1.0 | CASH_OUT | 128278.74 | C407493402 | 0.0 | 0.0 | C1262822392 | 8508776.62 | 1.249436715e7 | 0.0 | 3985590.530000001 |
| 1.0 | CASH_OUT | 256773.54 | C1774690057 | 0.0 | 0.0 | C1971489295 | 888447.97 | 0.0 | 0.0 | -888447.97 |
| 1.0 | CASH_OUT | 105759.12 | C480402503 | 0.0 | 0.0 | C1318822808 | 332876.84 | 500631.71 | 0.0 | 167754.87 |
| 1.0 | CASH_OUT | 59390.46 | C100445376 | 0.0 | 0.0 | C977993101 | 73791.34 | 965870.05 | 0.0 | 892078.7100000001 |
| 1.0 | CASH_OUT | 131991.26 | C1396385390 | 0.0 | 0.0 | C1971489295 | 1145221.51 | 0.0 | 0.0 | -1145221.51 |
| 1.0 | CASH_OUT | 65025.3 | C114414807 | 0.0 | 0.0 | C977993101 | 133181.81 | 965870.05 | 0.0 | 832688.24 |
| 1.0 | CASH_OUT | 82054.36 | C144699438 | 0.0 | 0.0 | C1526298704 | 174795.14 | 32092.07 | 0.0 | -142703.07 |
| 1.0 | CASH_OUT | 69708.79 | C1751500625 | 0.0 | 0.0 | C1335050193 | 71982.88 | 353532.56 | 0.0 | 281549.68 |
| 1.0 | CASH_OUT | 9104.26 | C1650955365 | 0.0 | 0.0 | C990355670 | 10623.0 | 19727.26 | 0.0 | 9104.259999999998 |
| 1.0 | CASH_OUT | 92553.64 | C1788380050 | 0.0 | 0.0 | C1182461167 | 297655.51 | 23508.22 | 0.0 | -274147.29000000004 |
| 1.0 | CASH_OUT | 66312.08 | C493944943 | 0.0 | 0.0 | C1750905143 | 544421.33 | 424250.45 | 0.0 | -120170.87999999995 |
| 1.0 | CASH_OUT | 142282.63 | C1533547487 | 0.0 | 0.0 | C453211571 | 805331.66 | 3461666.05 | 0.0 | 2656334.3899999997 |
| 1.0 | CASH_OUT | 114499.14 | C99770475 | 0.0 | 0.0 | C1899073220 | 129297.57 | 420946.86 | 0.0 | 291649.29 |
| 1.0 | CASH_OUT | 76635.24 | C727250772 | 0.0 | 0.0 | C357863579 | 102696.35 | 92307.65 | 0.0 | -10388.700000000012 |
| 1.0 | CASH_OUT | 132574.0 | C1052768296 | 0.0 | 0.0 | C977993101 | 198207.11 | 965870.05 | 0.0 | 767662.9400000001 |
| 1.0 | CASH_OUT | 39485.21 | C626193099 | 0.0 | 0.0 | C1883840933 | 9987286.56 | 1.687464309e7 | 0.0 | 6887356.529999999 |
| 1.0 | CASH_OUT | 236825.42 | C859123506 | 0.0 | 0.0 | C1899073220 | 243796.7 | 420946.86 | 0.0 | 177150.15999999997 |
| 1.0 | CASH_OUT | 327066.19 | C1597742167 | 0.0 | 0.0 | C453211571 | 947614.29 | 3461666.05 | 0.0 | 2514051.76 |
| 1.0 | CASH_OUT | 156819.6 | C503595296 | 0.0 | 0.0 | C1318822808 | 438635.96 | 500631.71 | 0.0 | 61995.75 |
| 1.0 | CASH_OUT | 13136.44 | C754072705 | 0.0 | 0.0 | C1023714065 | 1525395.97 | 1412484.09 | 0.0 | -112911.87999999989 |
| 1.0 | PAYMENT | 4306.89 | C908384914 | 256849.49 | 252542.6 | M905847077 | 0.0 | 0.0 | -4306.889999999985 | 0.0 |
| 1.0 | PAYMENT | 8309.52 | C156199931 | 252542.6 | 244233.08 | M878991463 | 0.0 | 0.0 | -8309.520000000019 | 0.0 |
| 1.0 | PAYMENT | 5363.35 | C612693043 | 81503.83 | 76140.48 | M1622613647 | 0.0 | 0.0 | -5363.350000000006 | 0.0 |
| 1.0 | PAYMENT | 132.42 | C307605969 | 76140.48 | 76008.06 | M73688220 | 0.0 | 0.0 | -132.41999999999825 | 0.0 |
| 1.0 | PAYMENT | 19644.08 | C130166095 | 76008.06 | 56363.98 | M1599674462 | 0.0 | 0.0 | -19644.079999999994 | 0.0 |
| 1.0 | PAYMENT | 895.24 | C1623060829 | 56363.98 | 55468.74 | M1205483858 | 0.0 | 0.0 | -895.2400000000052 | 0.0 |
| 1.0 | PAYMENT | 7641.08 | C1373949107 | 55468.74 | 47827.66 | M1189651769 | 0.0 | 0.0 | -7641.0799999999945 | 0.0 |
| 1.0 | PAYMENT | 31188.22 | C1230013344 | 47827.66 | 16639.44 | M1607869297 | 0.0 | 0.0 | -31188.220000000005 | 0.0 |
| 1.0 | PAYMENT | 13954.75 | C681639276 | 16639.44 | 2684.69 | M727860268 | 0.0 | 0.0 | -13954.749999999998 | 0.0 |
| 1.0 | PAYMENT | 10497.33 | C1717739363 | 2684.69 | 0.0 | M138536309 | 0.0 | 0.0 | -2684.69 | 0.0 |
| 1.0 | PAYMENT | 5843.75 | C665137804 | 0.0 | 0.0 | M509559152 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 7260.2 | C1815370847 | 0.0 | 0.0 | M1801021153 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 10285.86 | C1709578324 | 0.0 | 0.0 | M22446425 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 6249.78 | C1338958728 | 0.0 | 0.0 | M1870723838 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 848.74 | C988904418 | 0.0 | 0.0 | M261650860 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 6993.7 | C938613108 | 0.0 | 0.0 | M1598898814 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 795.29 | C1850874910 | 10360.0 | 9564.71 | M575360353 | 0.0 | 0.0 | -795.2900000000009 | 0.0 |
| 1.0 | PAYMENT | 9191.46 | C97901029 | 0.0 | 0.0 | M809383315 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | CASH_OUT | 132038.41 | C1324514662 | 0.0 | 0.0 | C998351292 | 230103.96 | 1015132.48 | 0.0 | 785028.52 |
| 1.0 | PAYMENT | 6495.27 | C908722588 | 22040.0 | 15544.73 | M496757837 | 0.0 | 0.0 | -6495.27 | 0.0 |
| 1.0 | PAYMENT | 1679.68 | C1548271808 | 15544.73 | 13865.06 | M17600354 | 0.0 | 0.0 | -1679.67 | 0.0 |
| 1.0 | TRANSFER | 47477.97 | C1733056574 | 6066.0 | 0.0 | C1629911510 | 144658.38 | 15375.37 | -6066.0 | -129283.01000000001 |
| 1.0 | DEBIT | 1260.62 | C636959006 | 1483.0 | 222.38 | C1686100174 | 10086.0 | 9837.06 | -1260.62 | -248.9400000000005 |
| 1.0 | PAYMENT | 15034.23 | C1059300256 | 40458.0 | 25423.77 | M1521568953 | 0.0 | 0.0 | -15034.23 | 0.0 |
| 1.0 | PAYMENT | 3238.39 | C1605988985 | 28773.0 | 25534.61 | M1134202713 | 0.0 | 0.0 | -3238.3899999999994 | 0.0 |
| 1.0 | CASH_OUT | 251241.14 | C1636178473 | 25534.61 | 0.0 | C997608398 | 523053.03 | 157982.12 | -25534.61 | -365070.91000000003 |
| 1.0 | PAYMENT | 3644.12 | C822232612 | 1230.0 | 0.0 | M633131207 | 0.0 | 0.0 | -1230.0 | 0.0 |
| 1.0 | CASH_OUT | 283123.48 | C852190062 | 497409.0 | 214285.52 | C1831477404 | 16872.0 | 247063.16 | -283123.48 | 230191.16 |
| 1.0 | PAYMENT | 2851.83 | C1674403916 | 10646.0 | 7794.17 | M1088239991 | 0.0 | 0.0 | -2851.83 | 0.0 |
| 1.0 | CASH_IN | 94726.64 | C61137731 | 69854.13 | 164580.77 | C476800120 | 129704.88 | 49864.36 | 94726.63999999998 | -79840.52 |
| 1.0 | PAYMENT | 17020.69 | C1780293706 | 164580.77 | 147560.08 | M1658511941 | 0.0 | 0.0 | -17020.690000000002 | 0.0 |
| 1.0 | PAYMENT | 6523.19 | C1264941544 | 147560.08 | 141036.89 | M1878992188 | 0.0 | 0.0 | -6523.189999999973 | 0.0 |
| 1.0 | PAYMENT | 1383.56 | C1818449913 | 5309.0 | 3925.44 | M1495161082 | 0.0 | 0.0 | -1383.56 | 0.0 |
| 1.0 | DEBIT | 1580.66 | C961859592 | 31156.0 | 29575.34 | C1556995360 | 12298.0 | 0.0 | -1580.6599999999999 | -12298.0 |
| 1.0 | PAYMENT | 2466.95 | C798278875 | 34978.24 | 32511.29 | M1497268815 | 0.0 | 0.0 | -2466.949999999997 | 0.0 |
| 1.0 | PAYMENT | 1017.85 | C1926027290 | 32511.29 | 31493.45 | M1837601499 | 0.0 | 0.0 | -1017.8400000000001 | 0.0 |
| 1.0 | PAYMENT | 2270.41 | C1690050988 | 60640.12 | 58369.71 | M1228798862 | 0.0 | 0.0 | -2270.4100000000035 | 0.0 |
| 1.0 | CASH_OUT | 102265.88 | C1562764987 | 58369.71 | 0.0 | C392292416 | 1438958.75 | 3420103.09 | -58369.71 | 1981144.3399999999 |
| 1.0 | DEBIT | 13758.63 | C216376974 | 29858.0 | 16099.37 | C1219161283 | 21305.0 | 0.0 | -13758.63 | -21305.0 |
| 1.0 | CASH_IN | 5221.77 | C164714495 | 1046.0 | 6267.77 | C317071334 | 0.0 | 139555.44 | 5221.77 | 139555.44 |
| 1.0 | PAYMENT | 12256.66 | C789982400 | 20800.0 | 8543.34 | M1576277927 | 0.0 | 0.0 | -12256.66 | 0.0 |
| 1.0 | PAYMENT | 8227.3 | C2080643905 | 6207.0 | 0.0 | M2137642385 | 0.0 | 0.0 | -6207.0 | 0.0 |
| 1.0 | PAYMENT | 527.15 | C1627883152 | 72421.0 | 71893.85 | M481553464 | 0.0 | 0.0 | -527.1499999999942 | 0.0 |
| 1.0 | PAYMENT | 950.25 | C1076966140 | 204944.0 | 203993.75 | M1908781622 | 0.0 | 0.0 | -950.25 | 0.0 |
| 1.0 | PAYMENT | 12728.46 | C749443480 | 83636.0 | 70907.54 | M796553753 | 0.0 | 0.0 | -12728.460000000006 | 0.0 |
| 1.0 | PAYMENT | 1002.96 | C1721045976 | 8325.0 | 7322.04 | M975001918 | 0.0 | 0.0 | -1002.96 | 0.0 |
| 1.0 | PAYMENT | 6917.1 | C1250582716 | 13555.0 | 6637.9 | M907815246 | 0.0 | 0.0 | -6917.1 | 0.0 |
| 1.0 | PAYMENT | 1977.17 | C2019157894 | 43619.0 | 41641.83 | M677217562 | 0.0 | 0.0 | -1977.1699999999983 | 0.0 |
| 1.0 | PAYMENT | 9314.84 | C827035437 | 104536.0 | 95221.16 | M1716164115 | 0.0 | 0.0 | -9314.839999999997 | 0.0 |
| 1.0 | PAYMENT | 1157.05 | C1360541835 | 52867.0 | 51709.95 | M363397863 | 0.0 | 0.0 | -1157.050000000003 | 0.0 |
| 1.0 | DEBIT | 3766.99 | C2021053848 | 16600.0 | 12833.01 | C1123629720 | 146166.67 | 46393.85 | -3766.99 | -99772.82 |
| 1.0 | PAYMENT | 3464.41 | C1245593227 | 5548.0 | 2083.59 | M1525844775 | 0.0 | 0.0 | -3464.41 | 0.0 |
| 1.0 | CASH_OUT | 68662.06 | C118555812 | 2083.59 | 0.0 | C1740000325 | 145843.39 | 1363368.51 | -2083.59 | 1217525.12 |
| 1.0 | DEBIT | 8679.29 | C1233505227 | 150592.0 | 141912.71 | C1877453512 | 21056.0 | 0.0 | -8679.290000000008 | -21056.0 |
| 1.0 | PAYMENT | 15266.05 | C1928621590 | 13679.0 | 0.0 | M1016162524 | 0.0 | 0.0 | -13679.0 | 0.0 |
| 1.0 | PAYMENT | 8348.75 | C858423246 | 0.0 | 0.0 | M1419125235 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 8419.73 | C88301993 | 0.0 | 0.0 | M841166421 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 6688.74 | C1377301456 | 9207.0 | 2518.26 | M938199512 | 0.0 | 0.0 | -6688.74 | 0.0 |
| 1.0 | PAYMENT | 1979.94 | C691779749 | 82034.0 | 80054.06 | M37841489 | 0.0 | 0.0 | -1979.9400000000023 | 0.0 |
| 1.0 | PAYMENT | 9574.31 | C1873121466 | 52463.0 | 42888.69 | M115945887 | 0.0 | 0.0 | -9574.309999999998 | 0.0 |
| 1.0 | PAYMENT | 5287.68 | C1927499639 | 11424.0 | 6136.32 | M2079961240 | 0.0 | 0.0 | -5287.68 | 0.0 |
| 1.0 | PAYMENT | 10627.18 | C949673757 | 6136.32 | 0.0 | M1999664216 | 0.0 | 0.0 | -6136.32 | 0.0 |
| 1.0 | PAYMENT | 5809.78 | C532677950 | 99884.0 | 94074.22 | M688593710 | 0.0 | 0.0 | -5809.779999999999 | 0.0 |
| 1.0 | TRANSFER | 411363.54 | C1200048933 | 39951.0 | 0.0 | C1049817027 | 52170.0 | 60738.03 | -39951.0 | 8568.029999999999 |
| 1.0 | PAYMENT | 4086.5 | C862025017 | 0.0 | 0.0 | M739737502 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0 | PAYMENT | 19741.22 | C1572127577 | 81762.38 | 62021.17 | M546796969 | 0.0 | 0.0 | -19741.210000000006 | 0.0 |
| 1.0 | PAYMENT | 5270.72 | C417797183 | 62021.17 | 56750.45 | M261845810 | 0.0 | 0.0 | -5270.720000000001 | 0.0 |
| 1.0 | CASH_IN | 55827.09 | C1959708563 | 9907.0 | 65734.09 | C1850180796 | 19873.0 | 1268668.92 | 55827.09 | 1248795.92 |
| 1.0 | CASH_OUT | 174068.82 | C1877986974 | 65734.09 | 0.0 | C33524623 | 1905641.8 | 1517262.16 | -65734.09 | -388379.64000000013 |
| 1.0 | TRANSFER | 1277212.77 | C1334405552 | 1277212.77 | 0.0 | C431687661 | 0.0 | 0.0 | -1277212.77 | 0.0 |
| 1.0 | CASH_OUT | 1277212.77 | C467632528 | 1277212.77 | 0.0 | C716083600 | 0.0 | 2444985.19 | -1277212.77 | 2444985.19 |
| 1.0 | CASH_IN | 24105.5 | C422522663 | 144.0 | 24249.5 | C644345897 | 20875.0 | 0.0 | 24105.5 | -20875.0 |
| 1.0 | PAYMENT | 6854.06 | C145066402 | 19853.0 | 12998.94 | M207988207 | 0.0 | 0.0 | -6854.0599999999995 | 0.0 |
| 1.0 | CASH_OUT | 111899.09 | C1800500978 | 12998.94 | 0.0 | C1883840933 | 1.002677177e7 | 1.687464309e7 | -12998.94 | 6847871.32 |
| 1.0 | PAYMENT | 1231.79 | C191310504 | 20792.0 | 19560.21 | M799755007 | 0.0 | 0.0 | -1231.7900000000009 | 0.0 |
| 1.0 | PAYMENT | 3380.1 | C247515192 | 19560.21 | 16180.11 | M1850137076 | 0.0 | 0.0 | -3380.0999999999985 | 0.0 |
| 1.0 | DEBIT | 2869.65 | C1556520190 | 8768.0 | 5898.35 | C1664439369 | 15485.0 | 5484.37 | -2869.6499999999996 | -10000.630000000001 |
| 1.0 | PAYMENT | 1658.87 | C701487403 | 16543.0 | 14884.13 | M566641954 | 0.0 | 0.0 | -1658.8700000000008 | 0.0 |
| 1.0 | PAYMENT | 823.36 | C2039984535 | 14884.13 | 14060.77 | M299819831 | 0.0 | 0.0 | -823.3599999999988 | 0.0 |
| 1.0 | PAYMENT | 7541.47 | C1175418534 | 14060.77 | 6519.3 | M1442038800 | 0.0 | 0.0 | -7541.47 | 0.0 |
| 1.0 | DEBIT | 3635.93 | C296541232 | 106929.0 | 103293.07 | C288665596 | 95816.79 | 8496.61 | -3635.929999999993 | -87320.18 |
| 1.0 | PAYMENT | 1328.59 | C1997357673 | 14469.0 | 13140.41 | M1250603112 | 0.0 | 0.0 | -1328.5900000000001 | 0.0 |
| 1.0 | TRANSFER | 56145.85 | C26357357 | 21528.0 | 0.0 | C1058634310 | 5075.0 | 33008.44 | -21528.0 | 27933.440000000002 |
| 1.0 | PAYMENT | 973.18 | C1990733619 | 10762.0 | 9788.82 | M1806378373 | 0.0 | 0.0 | -973.1800000000003 | 0.0 |
| 1.0 | PAYMENT | 619.24 | C1958592872 | 31960.0 | 31340.76 | M1682733268 | 0.0 | 0.0 | -619.2400000000016 | 0.0 |
| 1.0 | DEBIT | 300.41 | C1395262169 | 39167.0 | 38866.59 | C736709391 | 48749.55 | 46820.71 | -300.4100000000035 | -1928.8400000000038 |
| 1.0 | TRANSFER | 700967.19 | C1629454900 | 15383.0 | 0.0 | C1060830840 | 50767.43 | 130747.56 | -15383.0 | 79980.13 |
| 1.0 | PAYMENT | 8354.27 | C2075372030 | 5611.0 | 0.0 | M2092885124 | 0.0 | 0.0 | -5611.0 | 0.0 |
| 1.0 | DEBIT | 5423.28 | C2139168000 | 20281.0 | 14857.72 | C367746789 | 11523.0 | 0.0 | -5423.280000000001 | -11523.0 |
| 1.0 | PAYMENT | 708.59 | C1653751526 | 14857.72 | 14149.13 | M67671827 | 0.0 | 0.0 | -708.5900000000001 | 0.0 |
| 1.0 | PAYMENT | 8358.81 | C253348306 | 3467.0 | 0.0 | M2013768748 | 0.0 | 0.0 | -3467.0 | 0.0 |
| 1.0 | PAYMENT | 2245.62 | C194107588 | 30910.0 | 28664.38 | M1264674474 | 0.0 | 0.0 | -2245.619999999999 | 0.0 |
| 1.0 | PAYMENT | 4593.59 | C175595853 | 78282.0 | 73688.41 | M1339345635 | 0.0 | 0.0 | -4593.5899999999965 | 0.0 |
| 1.0 | PAYMENT | 399.66 | C1241938981 | 153760.0 | 153360.34 | M168957945 | 0.0 | 0.0 | -399.6600000000035 | 0.0 |
| 1.0 | PAYMENT | 1617.9 | C1004430079 | 507865.0 | 506247.1 | M1379148981 | 0.0 | 0.0 | -1617.9000000000233 | 0.0 |
| 1.0 | PAYMENT | 2167.26 | C732597634 | 13669.0 | 11501.74 | M832432849 | 0.0 | 0.0 | -2167.26 | 0.0 |
| 1.0 | PAYMENT | 9738.95 | C2031927175 | 289748.0 | 280009.05 | M176041373 | 0.0 | 0.0 | -9738.950000000012 | 0.0 |
| 1.0 | PAYMENT | 3396.25 | C1967617997 | 18524.0 | 15127.75 | M726843606 | 0.0 | 0.0 | -3396.25 | 0.0 |
| 1.0 | PAYMENT | 6780.78 | C925803196 | 52640.0 | 45859.22 | M1288135425 | 0.0 | 0.0 | -6780.779999999999 | 0.0 |
| 1.0 | PAYMENT | 2284.54 | C49318987 | 539.0 | 0.0 | M1058650291 | 0.0 | 0.0 | -539.0 | 0.0 |
-- Organize by Type
select type, count(1) from financials group by type
| type | count(1) |
|---|---|
| TRANSFER | 532909.0 |
| CASH_IN | 1399284.0 |
| CASH_OUT | 2237500.0 |
| PAYMENT | 2151495.0 |
| DEBIT | 41432.0 |
select type, sum(amount) from financials group by type
| type | sum(amount) |
|---|---|
| TRANSFER | 4.852919872631695e11 |
| CASH_IN | 2.3636739191246045e11 |
| CASH_OUT | 3.9441299522449023e11 |
| PAYMENT | 2.809337113837001e10 |
| DEBIT | 2.2719922127999982e8 |
Rules-based Model: Create a set of rules to identify fraud based on known cases
The following where clause are a set of rules to identify know fraud-based cases using SQL; i.e. rules-based model. * Often, financial fraud analytics start with with clauses like the where clause below * Note, in reality, rules are often much larger and more complicated
from pyspark.sql import functions as F
# Rules to Identify Known Fraud-based
df = df.withColumn("label",
F.when(
(
(df.oldbalanceOrg <= 56900) & (df.type == "TRANSFER") & (df.newbalanceDest <= 105)) |
(
(df.oldbalanceOrg > 56900) & (df.newbalanceOrig <= 12)) |
(
(df.oldbalanceOrg > 56900) & (df.newbalanceOrig > 12) & (df.amount > 1160000)
), 1
).otherwise(0))
# Calculate proportions
fraud_cases = df.filter(df.label == 1).count()
total_cases = df.count()
fraud_pct = 1.*fraud_cases/total_cases
# Provide quick statistics
print("Based on these rules, we have flagged %s (%s) fraud cases out of a total of %s cases." % (fraud_cases, fraud_pct, total_cases))
# Create temporary view to review data
df.createOrReplaceTempView("financials_labeled")
Based on these rules, we have flagged 255640 (0.04017841706718302) fraud cases out of a total of 6362620 cases.
How much fraud are we talking about?
Based on the existing rules, while 4% of the transactions are fraudulent, it takes into account of the 11% of the total amount.
select label, count(1) as `Transactions`, sum(amount) as `Total Amount` from financials_labeled group by label
| label | Transactions | Total Amount |
|---|---|---|
| 1.0 | 255640.0 | 1.2932429667647998e11 |
| 0.0 | 6106980.0 | 1.0150686480832826e12 |
Top Origination / Destination Difference Pairs (>$1M TotalDestDiff)
Each bar represents a pair of entities performing a transaction
-- where sum(destDiff) >= 10000000.00
select nameOrig, nameDest, label, TotalOrgDiff, TotalDestDiff
from (
select nameOrig, nameDest, label, sum(OrgDiff) as TotalOrgDiff, sum(destDiff) as TotalDestDiff
from financials_labeled
group by nameOrig, nameDest, label
) a
where TotalDestDiff >= 1000000
limit 100
| nameOrig | nameDest | label | TotalOrgDiff | TotalDestDiff |
|---|---|---|---|---|
| C752686443 | C1816757085 | 0.0 | -51178.0 | 1037297.459999999 |
| C1034899044 | C306206744 | 0.0 | 0.0 | 2308448.1800000006 |
| C1337803451 | C1778801068 | 0.0 | 0.0 | 3155647.91 |
| C1570013189 | C1945802665 | 1.0 | -63916.0 | 1179067.2799999998 |
| C1379731404 | C413177525 | 0.0 | 0.0 | 2375262.37 |
| C608080514 | C2006081398 | 1.0 | -101396.29 | 3208776.46 |
| C1567963311 | C1141049797 | 0.0 | 136177.75999999978 | 1243657.54 |
| C582291919 | C461640598 | 0.0 | 0.0 | 3381134.63 |
| C689413511 | C1988852187 | 0.0 | 0.0 | 1161908.05 |
| C122861681 | C1754722089 | 0.0 | -22291.0 | 1512938.6399999997 |
| C1088745859 | C2049813033 | 0.0 | -42044.0 | 1806348.1500000001 |
| C1006778810 | C1825389809 | 0.0 | 0.0 | 2681653.25 |
| C1440763981 | C1436393926 | 0.0 | 35711.119999999995 | 1117034.7 |
| C50202201 | C663929766 | 0.0 | -10293.0 | 3159176.9200000004 |
| C1532100961 | C1049661557 | 0.0 | -32755.0 | 1668053.03 |
| C1538752236 | C791710001 | 0.0 | -76350.53000000003 | 1780469.75 |
| C700323381 | C988406186 | 0.0 | 60388.73999999999 | 8117469.14 |
| C2132142410 | C1558550579 | 0.0 | 0.0 | 4227345.199999999 |
| C2060552204 | C683891649 | 0.0 | -253.0 | 5932193.2 |
| C1809115549 | C2115178036 | 0.0 | -34061.37 | 5844315.460000001 |
| C1159220227 | C529351377 | 0.0 | 22624.52000000002 | 1274909.27 |
| C756046909 | C1151964959 | 0.0 | -37504.0 | 3953550.38 |
| C77003778 | C530216580 | 0.0 | -16420.0 | 2711634.1 |
| C945058760 | C5958610 | 1.0 | -57367.0 | 2136408.2 |
| C1627314039 | C1558550579 | 0.0 | 0.0 | 3263061.3899999997 |
| C1768965319 | C1553217607 | 0.0 | 194027.76999999955 | 1382299.6 |
| C1455537694 | C1917025677 | 0.0 | -43924.17000000016 | 1371223.48 |
| C748697380 | C1080314178 | 0.0 | 99377.57 | 1.13349226e7 |
| C1951089624 | C489693281 | 0.0 | -22967.26 | 2387625.5200000005 |
| C1964002964 | C744708435 | 0.0 | 0.0 | 1489751.1300000001 |
| C1203268505 | C459903071 | 0.0 | 0.0 | 1769126.75 |
| C1718688489 | C1469682037 | 0.0 | -42999.0 | 1013108.0999999999 |
| C1645613622 | C2047015557 | 0.0 | 133130.81000000006 | 1009068.89 |
| C1385602675 | C1790831319 | 1.0 | -274602.45 | 2445953.84 |
| C1674767172 | C1988058126 | 0.0 | 81933.46999999974 | 1731338.83 |
| C1888296222 | C276384108 | 0.0 | 0.0 | 3108280.4000000004 |
| C1282343859 | C1360767589 | 1.0 | -104066.0 | 2266055.2300000004 |
| C1560116595 | C1876351111 | 0.0 | 0.0 | 3266580.3800000004 |
| C423919427 | C1018394275 | 0.0 | 0.0 | 2625447.23 |
| C819894239 | C1247600089 | 0.0 | 0.0 | 5414463.799999999 |
| C1773614696 | C232933235 | 0.0 | -27745.01000000001 | 1345923.1099999999 |
| C1422230471 | C769591652 | 0.0 | 89261.90000000002 | 2713617.45 |
| C1782925064 | C1839337592 | 0.0 | 0.0 | 1757275.6799999997 |
| C99199522 | C934300202 | 0.0 | -10200.0 | 1167812.36 |
| C180184286 | C700689755 | 1.0 | -74712.0 | 3596897.9699999997 |
| C1638181666 | C749013731 | 0.0 | 0.0 | 2214655.4299999997 |
| C767211382 | C1393612077 | 0.0 | 0.0 | 2286610.52 |
| C234678587 | C797657283 | 1.0 | -124929.0 | 1154001.7000000002 |
| C20269622 | C2107960573 | 1.0 | -237119.41 | 1130794.31 |
| C598082082 | C1955746028 | 0.0 | 0.0 | 2546000.1399999997 |
| C325277081 | C1730964371 | 0.0 | 0.0 | 1711671.65 |
| C810441806 | C218165429 | 0.0 | -31031.04 | 1744178.31 |
| C1383044705 | C767544292 | 1.0 | -130542.0 | 1030078.6299999999 |
| C956369822 | C453306510 | 0.0 | -2627.11 | 2845873.34 |
| C93288387 | C1485516154 | 0.0 | 0.0 | 1818114.9499999993 |
| C1701105060 | C987174960 | 1.0 | -1019339.51 | 1537040.6799999997 |
| C749653721 | C1778404446 | 0.0 | 0.0 | 3217781.69 |
| C137669742 | C805814824 | 0.0 | 0.0 | 4403477.52 |
| C1990342884 | C1127443868 | 0.0 | 0.0 | 1068271.4699999997 |
| C1433651588 | C1379031360 | 0.0 | 0.0 | 1260404.5499999989 |
| C124283794 | C239330900 | 0.0 | -1.0 | 1368747.4699999997 |
| C330853726 | C2141828226 | 0.0 | -37689.0 | 2658344.96 |
| C2053130194 | C1685697739 | 0.0 | 0.0 | 1079265.73 |
| C2065848959 | C261292810 | 0.0 | 0.0 | 1096327.63 |
| C1187190307 | C915733493 | 0.0 | -9028.0 | 2118780.33 |
| C42014603 | C2078745453 | 0.0 | 57818.42 | 1954231.5700000003 |
| C1035196945 | C968408272 | 0.0 | 0.0 | 1665205.8000000003 |
| C1584984374 | C271588719 | 0.0 | 0.0 | 2111582.98 |
| C61333846 | C2145867337 | 0.0 | 0.0 | 1491380.9300000002 |
| C2143321563 | C1521704415 | 0.0 | -963.0 | 1328668.7400000002 |
| C1944196283 | C16729522 | 0.0 | -29746.0 | 3005476.4 |
| C373966597 | C1492359254 | 0.0 | -1744.0 | 1062979.0 |
| C1452543394 | C119950815 | 0.0 | 0.0 | 1482459.4100000001 |
| C1232347955 | C651049041 | 0.0 | 424585.81 | 1113506.17 |
| C1838683678 | C815306112 | 0.0 | -1347.0 | 2815123.61 |
| C1960113192 | C1437781451 | 0.0 | 151030.04999999888 | 1393690.8000000003 |
| C418409319 | C516516576 | 0.0 | 0.0 | 1234482.98 |
| C1443215275 | C541484789 | 0.0 | 0.0 | 2095243.3099999998 |
| C1289543366 | C578294406 | 1.0 | -60850.0 | 1808419.65 |
| C1540598314 | C1950696591 | 0.0 | 0.0 | 1765025.5999999996 |
| C778684365 | C254669696 | 0.0 | -728.0 | 3582937.19 |
| C1538133757 | C1433627902 | 0.0 | -8125.0 | 1291187.17 |
| C897984995 | C904765844 | 1.0 | -293009.18 | 2224366.4 |
| C1855151198 | C805643982 | 0.0 | 35591.80000000028 | 1932983.4300000002 |
| C401005628 | C776749939 | 0.0 | 0.0 | 2175311.59 |
| C1122629781 | C1991184172 | 0.0 | -5366.0 | 1167464.57 |
| C974166894 | C1363208344 | 0.0 | 0.0 | 1476888.1300000001 |
| C19787815 | C1858098329 | 0.0 | 0.0 | 1015287.6800000002 |
| C1902875287 | C105223833 | 0.0 | -223.0 | 3422145.88 |
| C1882353435 | C1481851679 | 0.0 | 327635.45 | 2305918.1499999994 |
| C1035277970 | C317983984 | 0.0 | -2658.0 | 1012950.8 |
| C698935038 | C1852790850 | 0.0 | 0.0 | 1177790.5 |
| C2081633996 | C214555146 | 0.0 | -5626.0 | 2124608.71 |
| C1763900287 | C24176920 | 0.0 | -42522.0 | 2674988.2799999993 |
| C1729080591 | C1045308594 | 0.0 | 0.0 | 1643239.79 |
| C235819446 | C311573688 | 1.0 | -1175734.28 | 2214979.5399999996 |
| C752293219 | C1523549102 | 1.0 | -221344.0 | 1974939.9 |
| C2117929123 | C931773845 | 0.0 | 0.0 | 1195544.19 |
| C788474781 | C210970463 | 0.0 | -21308.0 | 1034059.37 |
| C505005314 | C1298763451 | 0.0 | -536.0 | 1203170.67 |
What type of transactions are associated with fraud?
Reviewing the rules-based model, it appears that most fraudulent transactions are in the category of Transfer and Cash_Out.
select type, label, count(1) as `Transactions` from financials_labeled group by type, label
| type | label | Transactions |
|---|---|---|
| PAYMENT | 0.0 | 2150909.0 |
| CASH_OUT | 0.0 | 2052790.0 |
| DEBIT | 1.0 | 31.0 |
| TRANSFER | 1.0 | 70248.0 |
| CASH_OUT | 1.0 | 184710.0 |
| PAYMENT | 1.0 | 586.0 |
| DEBIT | 0.0 | 41401.0 |
| CASH_IN | 0.0 | 1399219.0 |
| TRANSFER | 0.0 | 462661.0 |
| CASH_IN | 1.0 | 65.0 |
Rules vs. ML model
Instead of creating specific rules that will change over time, can we be more precise and go to production faster by creating a ML model?
Decision Trees
Decision trees and their ensembles are popular methods for the machine learning tasks of classification and regression. Decision trees are widely used since they are easy to interpret, handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture non-linearities and feature interactions. Tree ensemble algorithms such as random forests and boosting are among the top performers for classification and regression tasks.
Because of these facets, decision trees often perform well on top of rules-based models and are often a good starting point for fraud detection.

Source: The Wise Old Tree
Create training and test datasets
To build and validate our generalized fraud ML model, we will initially split the data using randomSplit to create our training and test datasets.
# Initially split our dataset between training and test datasets
(train, test) = df.randomSplit([0.8, 0.2], seed=12345)
# Cache the training and test datasets
train.cache()
test.cache()
# Print out dataset counts
print("Total rows: %s, Training rows: %s, Test rows: %s" % (df.count(), train.count(), test.count()))
Total rows: 6362620, Training rows: 5090394, Test rows: 1272226
Create ML Pipeline
When creating an ML model, there are typically a set of repeated steps (e.g. StringIndexer, VectorAssembler, etc.). By creating a ML pipeline, we can reuse this pipeline (and all of its steps) to retrain on a new and/or updated dataset.
from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer
from pyspark.ml.feature import OneHotEncoderEstimator
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import DecisionTreeClassifier
# Encodes a string column of labels to a column of label indices
indexer = StringIndexer(inputCol = "type", outputCol = "typeIndexed")
# VectorAssembler is a transformer that combines a given list of columns into a single vector column
va = VectorAssembler(inputCols = ["typeIndexed", "amount", "oldbalanceOrg", "newbalanceOrig", "oldbalanceDest", "newbalanceDest", "orgDiff", "destDiff"], outputCol = "features")
# Using the DecisionTree classifier model
dt = DecisionTreeClassifier(labelCol = "label", featuresCol = "features", seed = 54321, maxDepth = 5)
# Create our pipeline stages
pipeline = Pipeline(stages=[indexer, va, dt])
# View the Decision Tree model (prior to CrossValidator)
dt_model = pipeline.fit(train)
display(dt_model.stages[-1])
| treeNode |
|---|
| {"index":13,"featureType":"continuous","prediction":null,"threshold":-59767.5,"categories":null,"feature":6,"overflow":false} |
| {"index":1,"featureType":"continuous","prediction":null,"threshold":17.18,"categories":null,"feature":3,"overflow":false} |
| {"index":0,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":7,"featureType":"continuous","prediction":null,"threshold":8142508.215,"categories":null,"feature":2,"overflow":false} |
| {"index":3,"featureType":"continuous","prediction":null,"threshold":671642.935,"categories":null,"feature":1,"overflow":false} |
| {"index":2,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":5,"featureType":"continuous","prediction":null,"threshold":2296867.575,"categories":null,"feature":2,"overflow":false} |
| {"index":4,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":6,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":9,"featureType":"continuous","prediction":null,"threshold":671642.935,"categories":null,"feature":1,"overflow":false} |
| {"index":8,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":11,"featureType":"continuous","prediction":null,"threshold":744825.315,"categories":null,"feature":4,"overflow":false} |
| {"index":10,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":12,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":21,"featureType":"continuous","prediction":null,"threshold":-38090.5,"categories":null,"feature":6,"overflow":false} |
| {"index":19,"featureType":"continuous","prediction":null,"threshold":50374.5,"categories":null,"feature":2,"overflow":false} |
| {"index":17,"featureType":"continuous","prediction":null,"threshold":46.855,"categories":null,"feature":5,"overflow":false} |
| {"index":15,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0,1.0],"feature":0,"overflow":false} |
| {"index":14,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":16,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":18,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":20,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":23,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0,1.0,2.0,4.0],"feature":0,"overflow":false} |
| {"index":22,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":27,"featureType":"continuous","prediction":null,"threshold":46.855,"categories":null,"feature":5,"overflow":false} |
| {"index":25,"featureType":"continuous","prediction":null,"threshold":50374.5,"categories":null,"feature":2,"overflow":false} |
| {"index":24,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":26,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":28,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
Use BinaryClassificationEvaluator
Determine the accuracy of the model by reviewing the areaUnderPR and areaUnderROC
from pyspark.ml.evaluation import BinaryClassificationEvaluator
# Use BinaryClassificationEvaluator to evaluate our model
evaluatorPR = BinaryClassificationEvaluator(labelCol = "label", rawPredictionCol = "prediction", metricName = "areaUnderPR")
evaluatorAUC = BinaryClassificationEvaluator(labelCol = "label", rawPredictionCol = "prediction", metricName = "areaUnderROC")
Setup CrossValidation
To try out different parameters to potentially improve our model, we will use CrossValidator in conjunction with the ParamGridBuilder to automate trying out different parameters.
Note, we are using evaluatorPR as our evaluator as the Precision-Recall curve is often better for an unbalanced distribution.
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
# Build the grid of different parameters
paramGrid = ParamGridBuilder() \
.addGrid(dt.maxDepth, [5, 10, 15]) \
.addGrid(dt.maxBins, [10, 20, 30]) \
.build()
# Build out the cross validation
crossval = CrossValidator(estimator = dt,
estimatorParamMaps = paramGrid,
evaluator = evaluatorPR,
numFolds = 3)
pipelineCV = Pipeline(stages=[indexer, va, crossval])
# Train the model using the pipeline, parameter grid, and preceding BinaryClassificationEvaluator
cvModel_u = pipelineCV.fit(train)
Review Results
Review the areaUnderPR (area under Precision Recall curve) and areaUnderROC (area under Receiver operating characteristic) or AUC (area under curve) metrics.
# Build the best model (training and test datasets)
train_pred = cvModel_u.transform(train)
test_pred = cvModel_u.transform(test)
# Evaluate the model on training datasets
pr_train = evaluatorPR.evaluate(train_pred)
auc_train = evaluatorAUC.evaluate(train_pred)
# Evaluate the model on test datasets
pr_test = evaluatorPR.evaluate(test_pred)
auc_test = evaluatorAUC.evaluate(test_pred)
# Print out the PR and AUC values
print("PR train:", pr_train)
print("AUC train:", auc_train)
print("PR test:", pr_test)
print("AUC test:", auc_test)
PR train: 0.9537894984523128
AUC train: 0.998647996459481
PR test: 0.9539170535377599
AUC test: 0.9984378183482442
Confusion Matrix Code-base
Subsequent cells will be using the following code to plot the confusion matrix.
# Create confusion matrix template
from pyspark.sql.functions import lit, expr, col, column
# Confusion matrix template
cmt = spark.createDataFrame([(1, 0), (0, 0), (1, 1), (0, 1)], ["label", "prediction"])
cmt.createOrReplaceTempView("cmt")
# Source code for plotting confusion matrix is based on `plot_confusion_matrix`
# via https://runawayhorse001.github.io/LearningApacheSpark/classification.html#decision-tree-classification
import matplotlib.pyplot as plt
import numpy as np
import itertools
def plot_confusion_matrix(cm, title):
# Clear Plot
plt.gcf().clear()
# Configure figure
fig = plt.figure(1)
# Configure plot
classes = ['Fraud', 'No Fraud']
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
# Normalize and establish threshold
normalize=False
fmt = 'd'
thresh = cm.max() / 2.
# Iterate through the confusion matrix cells
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
# Final plot configurations
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
# Display images
image = fig
# Show plot
#fig = plt.show()
# Save plot
fig.savefig("confusion-matrix.png")
# Display Plot
display(image)
# Close Plot
plt.close(fig)
# Create temporary view for test predictions
test_pred.createOrReplaceTempView("test_pred")
# Create test predictions confusion matrix
test_pred_cmdf = spark.sql("select a.label, a.prediction, coalesce(b.count, 0) as count from cmt a left outer join (select label, prediction, count(1) as count from test_pred group by label, prediction) b on b.label = a.label and b.prediction = a.prediction order by a.label desc, a.prediction desc")
# View confusion matrix
display(test_pred_cmdf)
| label | prediction | count |
|---|---|---|
| 1.0 | 1.0 | 50717.0 |
| 1.0 | 0.0 | 58.0 |
| 0.0 | 1.0 | 2421.0 |
| 0.0 | 0.0 | 1219030.0 |
View Confusion Matrix
Let's use matplotlib and pandas to visualize our confusion matrix
# Convert to pandas
cm_pdf = test_pred_cmdf.toPandas()
# Create 1d numpy array of confusion matrix values
cm_1d = cm_pdf.iloc[:, 2]
# Create 2d numpy array of confusion matrix values
cm = np.reshape(cm_1d, (-1, 2))
# Print out the 2d array
print(cm)
[[ 50717 58]
[ 2421 1219030]]
# Plot confusion matrix
plot_confusion_matrix(cm, "Confusion Matrix (Unbalanced Test)")
# Log MLflow
with mlflow.start_run(experiment_id = mlflow_experiment_id) as run:
# Log Parameters and metrics
mlflow.log_param("balanced", "no")
mlflow.log_metric("PR train", pr_train)
mlflow.log_metric("AUC train", auc_train)
mlflow.log_metric("PR test", pr_test)
mlflow.log_metric("AUC test", auc_test)
# Log model
mlflow.spark.log_model(dt_model, "model")
# Log Confusion matrix
mlflow.log_artifact("confusion-matrix.png")
Model with Balanced classes
Let's see if we can improve our decision tree model but balancing the Fraud vs. No Fraud cases. We will tune the model using the metrics areaUnderROC or (AUC)
# Reset the DataFrames for no fraud (`dfn`) and fraud (`dfy`)
dfn = train.filter(train.label == 0)
dfy = train.filter(train.label == 1)
# Calculate summary metrics
N = train.count()
y = dfy.count()
p = y/N
# Create a more balanced training dataset
train_b = dfn.sample(False, p, seed = 92285).union(dfy)
# Print out metrics
print("Total count: %s, Fraud cases count: %s, Proportion of fraud cases: %s" % (N, y, p))
print("Balanced training dataset count: %s" % train_b.count())
Total count: 5090394, Fraud cases count: 204865, Proportion of fraud cases: 0.040245411258932016
Balanced training dataset count: 401898
# Display our more balanced training dataset
display(train_b.groupBy("label").count())
| label | count |
|---|---|
| 1.0 | 204865.0 |
| 0.0 | 197033.0 |
Update ML pipeline
Because we had created the ML pipeline stages in the previous cells, we can re-use them to execute it against our balanced dataset.
Note, we are using evaluatorAUC as our evaluator as this is often better for a balanced distribution.
# Re-run the same ML pipeline (including parameters grid)
crossval_b = CrossValidator(estimator = dt,
estimatorParamMaps = paramGrid,
evaluator = evaluatorAUC,
numFolds = 3)
pipelineCV_b = Pipeline(stages=[indexer, va, crossval_b])
# Train the model using the pipeline, parameter grid, and BinaryClassificationEvaluator using the `train_b` dataset
cvModel_b = pipelineCV_b.fit(train_b)
# Build the best model (balanced training and full test datasets)
train_pred_b = cvModel_b.transform(train_b)
test_pred_b = cvModel_b.transform(test)
# Evaluate the model on the balanced training datasets
pr_train_b = evaluatorPR.evaluate(train_pred_b)
auc_train_b = evaluatorAUC.evaluate(train_pred_b)
# Evaluate the model on full test datasets
pr_test_b = evaluatorPR.evaluate(test_pred_b)
auc_test_b = evaluatorAUC.evaluate(test_pred_b)
# Print out the PR and AUC values
print("PR train:", pr_train_b)
print("AUC train:", auc_train_b)
print("PR test:", pr_test_b)
print("AUC test:", auc_test_b)
PR train: 0.999629161563572
AUC train: 0.9998071389056655
PR test: 0.9904709171789063
AUC test: 0.9997903902204509
# Create temporary view for test predictions
test_pred_b.createOrReplaceTempView("test_pred_b")
# Create test predictions confusion matrix
test_pred_b_cmdf = spark.sql("select a.label, a.prediction, coalesce(b.count, 0) as count from cmt a left outer join (select label, prediction, count(1) as count from test_pred_b group by label, prediction) b on b.label = a.label and b.prediction = a.prediction order by a.label desc, a.prediction desc")
# View confusion matrix
display(test_pred_b_cmdf)
| label | prediction | count |
|---|---|---|
| 1.0 | 1.0 | 50774.0 |
| 1.0 | 0.0 | 1.0 |
| 0.0 | 1.0 | 488.0 |
| 0.0 | 0.0 | 1220963.0 |
# Convert to pandas
cm_b_pdf = test_pred_b_cmdf.toPandas()
# Create 1d numpy array of confusion matrix values
cm_b_1d = cm_b_pdf.iloc[:, 2]
# Create 2d numpy array of confusion matrix vlaues
cm_b = np.reshape(cm_b_1d, (-1, 2))
# Print out the 2d array
print(cm_b)
[[ 50774 1]
[ 488 1220963]]
View Decision Tree Models
Visually compare the differences between the unbalanced and balanced decision tree models (basd on the train and train_b datasets respectively).
# Extract Feature Importance
# Attribution: Feature Selection Using Feature Importance Score - Creating a PySpark Estimator
# https://www.timlrx.com/2018/06/19/feature-selection-using-feature-importance-score-creating-a-pyspark-estimator/
import pandas as pd
def ExtractFeatureImp(featureImp, dataset, featuresCol):
list_extract = []
for i in dataset.schema[featuresCol].metadata["ml_attr"]["attrs"]:
list_extract = list_extract + dataset.schema[featuresCol].metadata["ml_attr"]["attrs"][i]
varlist = pd.DataFrame(list_extract)
varlist['score'] = varlist['idx'].apply(lambda x: featureImp[x])
return(varlist.sort_values('score', ascending = False))
# View the Unbalanced Decision Tree model (prior to CrossValidator)
dt_model = pipeline.fit(train)
display(dt_model.stages[-1])
| treeNode |
|---|
| {"index":13,"featureType":"continuous","prediction":null,"threshold":-59767.5,"categories":null,"feature":6,"overflow":false} |
| {"index":1,"featureType":"continuous","prediction":null,"threshold":17.18,"categories":null,"feature":3,"overflow":false} |
| {"index":0,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":7,"featureType":"continuous","prediction":null,"threshold":8142508.215,"categories":null,"feature":2,"overflow":false} |
| {"index":3,"featureType":"continuous","prediction":null,"threshold":671642.935,"categories":null,"feature":1,"overflow":false} |
| {"index":2,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":5,"featureType":"continuous","prediction":null,"threshold":2296867.575,"categories":null,"feature":2,"overflow":false} |
| {"index":4,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":6,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":9,"featureType":"continuous","prediction":null,"threshold":671642.935,"categories":null,"feature":1,"overflow":false} |
| {"index":8,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":11,"featureType":"continuous","prediction":null,"threshold":744825.315,"categories":null,"feature":4,"overflow":false} |
| {"index":10,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":12,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":21,"featureType":"continuous","prediction":null,"threshold":-38090.5,"categories":null,"feature":6,"overflow":false} |
| {"index":19,"featureType":"continuous","prediction":null,"threshold":50374.5,"categories":null,"feature":2,"overflow":false} |
| {"index":17,"featureType":"continuous","prediction":null,"threshold":46.855,"categories":null,"feature":5,"overflow":false} |
| {"index":15,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0,1.0],"feature":0,"overflow":false} |
| {"index":14,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":16,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":18,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":20,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":23,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0,1.0,2.0,4.0],"feature":0,"overflow":false} |
| {"index":22,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":27,"featureType":"continuous","prediction":null,"threshold":46.855,"categories":null,"feature":5,"overflow":false} |
| {"index":25,"featureType":"continuous","prediction":null,"threshold":50374.5,"categories":null,"feature":2,"overflow":false} |
| {"index":24,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":26,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":28,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
# Extract Feature Importance for the original unbalanced dt_model
ExtractFeatureImp(dt_model.stages[-1].featureImportances, train_pred, "features").head(10)
<span class="ansired">Out[</span><span class="ansired">47</span><span class="ansired">]: </span>
idx name vals \
6 6 orgDiff NaN
3 3 newbalanceOrig NaN
5 5 newbalanceDest NaN
2 2 oldbalanceOrg NaN
0 0 typeIndexed [CASH_OUT, PAYMENT, CASH_IN, TRANSFER, DEBIT]
1 1 amount NaN
4 4 oldbalanceDest NaN
7 7 destDiff NaN
score
6 0.609449
3 0.382837
5 0.005051
2 0.001903
0 0.000621
1 0.000128
4 0.000010
7 0.000000
# View the Balanced Decision Tree model (prior to CrossValidator)
dt_model_b = pipeline.fit(train_b)
display(dt_model_b.stages[-1])
| treeNode |
|---|
| {"index":15,"featureType":"continuous","prediction":null,"threshold":-39863.59500000001,"categories":null,"feature":6,"overflow":false} |
| {"index":7,"featureType":"continuous","prediction":null,"threshold":6.26,"categories":null,"feature":3,"overflow":false} |
| {"index":5,"featureType":"continuous","prediction":null,"threshold":51488.5,"categories":null,"feature":2,"overflow":false} |
| {"index":3,"featureType":"continuous","prediction":null,"threshold":201.245,"categories":null,"feature":5,"overflow":false} |
| {"index":1,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0,2.0],"feature":0,"overflow":false} |
| {"index":0,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":2,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":4,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":6,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":13,"featureType":"continuous","prediction":null,"threshold":914660.585,"categories":null,"feature":1,"overflow":false} |
| {"index":11,"featureType":"continuous","prediction":null,"threshold":50.19,"categories":null,"feature":3,"overflow":false} |
| {"index":9,"featureType":"continuous","prediction":null,"threshold":51488.5,"categories":null,"feature":2,"overflow":false} |
| {"index":8,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":10,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":12,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":14,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":21,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0,2.0,3.0,4.0],"feature":0,"overflow":false} |
| {"index":17,"featureType":"continuous","prediction":null,"threshold":914660.585,"categories":null,"feature":1,"overflow":false} |
| {"index":16,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":19,"featureType":"categorical","prediction":null,"threshold":null,"categories":[0.0],"feature":0,"overflow":false} |
| {"index":18,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":20,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":23,"featureType":"continuous","prediction":null,"threshold":201.245,"categories":null,"feature":5,"overflow":false} |
| {"index":22,"featureType":null,"prediction":1.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
| {"index":24,"featureType":null,"prediction":0.0,"threshold":null,"categories":null,"feature":null,"overflow":false} |
# Extract Feature Importance for the nbalanced dt_model
ExtractFeatureImp(dt_model_b.stages[-1].featureImportances, train_pred_b, "features").head(10)
<span class="ansired">Out[</span><span class="ansired">49</span><span class="ansired">]: </span>
idx name vals \
6 6 orgDiff NaN
3 3 newbalanceOrig NaN
2 2 oldbalanceOrg NaN
5 5 newbalanceDest NaN
1 1 amount NaN
0 0 typeIndexed [CASH_OUT, TRANSFER, PAYMENT, CASH_IN, DEBIT]
4 4 oldbalanceDest NaN
7 7 destDiff NaN
score
6 0.901013
3 0.057844
2 0.027848
5 0.010055
1 0.002049
0 0.001190
4 0.000000
7 0.000000
Comparing Confusion Matrices
Below we will compare the unbalanced and balanced decision tree ML model confusion matrices.
# Plot confusion matrix
plot_confusion_matrix(cm, "Confusion Matrix (Unbalanced Test)")
# Plot confusion matrix
plot_confusion_matrix(cm_b, "Confusion Matrix (Balanced Test)")
# Log MLflow
with mlflow.start_run(experiment_id = mlflow_experiment_id) as run:
# Log Parameters and metrics
mlflow.log_param("balanced", "yes")
mlflow.log_metric("PR train", pr_train_b)
mlflow.log_metric("AUC train", auc_train_b)
mlflow.log_metric("PR test", pr_test_b)
mlflow.log_metric("AUC test", auc_test_b)
# Log model
mlflow.spark.log_model(dt_model_b, "model")
# Log Confusion matrix
mlflow.log_artifact("confusion-matrix.png")
Observation
There is a significant difference for the PR and AUC values when comparing the unbalanced vs. balanced decision tree models.
| Metric/dataset | Unbalanced | Balanced |
|---|---|---|
| PR train | 0.9537894984523128 | 0.999629161563572 |
| AUC train | 0.998647996459481 | 0.9998071389056655 |
| PR test | 0.9539170535377599 | 0.9904709171789063 |
| AUC test | 0.9984378183482442 | 0.9997903902204509 |
You can also view this data directly within MLflow by comparing the unbalanced vs. balanced decision tree models as noted in the following animated GIF.















